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Performance Analysis of Mesh Networking Implementation on Mikrotik Router Board 941 Yudi Abdul Halim; Darwin Panjaitan; Alexander Silitonga; Suata Wan Kelispa Halawa; Roberto Kaban; Meiliyani Br Ginting
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 2 (2026): JCEIT: Journal of Computer Engineering and Information Technology (March 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i2.54

The increasingly rapid development of computer network technology demands a network system that is reliable, flexible, and able to adapt to dynamic environmental conditions. One of the network technologies that is currently developing is mesh networking. Mesh networking is a network topology where each node can be connected to each other directly or indirectly through other nodes. This research aims to analyze the application of the mesh networking method using the Mikrotik RouterBoard 941 device. The research method used is experimental by configuring, implementing, and testing mesh networking on the Mikrotik RouterBoard 941. The results of the research show that mesh networking can be applied to the Mikrotik RouterBoard 941 by utilizing available features, such as OLSR (Optimized Link State Routing) and WDS (Wireless Distribution System). Mesh networking is able to increase redundancy and network availability, and can adapt to changes in network topology. However, mesh networking also has several disadvantages, such as configuration complexity, routing overhead, and the possibility of bottlenecks at certain nodes. REFERENCES Aisyah, A. (2022). Mesh Network Model On Internet Of Things (IoT) Systems For Environmental Monitoring. Ardhitya, A. I. (2021). Definition and Explanation of Microtics. Available at Http://Ilmukomputer. org/2013/01/04/Definition-and-Explanation-Mikrotik/. Accessed, 20. Arman, M., & Kasran, K. (2023). Wireless Network Analysis on IoT-Based ATM Machines at PT. Bank Negara Indonesia (Persero) Tbk KCP Watansoppeng. Scientific Journal of Information Systems and Informatics Engineering (JISTI), 6(1), 77–84. https://doi.org/10.57093/jisti.v6i1.151 Bahtiar, D., Febrianto, W. J., Maulana, A., Saputra, S., Darmawan, W., Tafonao, R. P., Julianto, R., Zai, R., & Djutalov, R. (2021). Basic Introduction to Computer Network InstallationUsing Mikrotik. Informatics Student Creativity, 2, 507–518. Fahmi Faizar, F. (2020). The effect of Bluetooth 5.0 interference on 802.11b network performance. 2(10), 1390–1399. Fahriani, N. (2024). From Wired to Wireless:(Evolution and Innovation of Modern Networks). Hariyanto, T., & Rahayu, M. (2021). The WiFi bandwidth system of ad-hoc networks uses the class-based queue method. JITEL (Scientific Journal of Telecommunications, Electronics, and Power Electricity), 1(1), 17–24. https://doi.org/10.35313/jitel.v1.i1.2021.17-24 Iqbal, M., & Tambunan, L. (2021). Designing samba servers using ubuntu servers and network configuration using mikrotik routerboards (case study of pt. Mesitechmitra purnabangun). JSR: Robotic Information Systems Network, 5(1), 1–8. Juniarti, T. S. J. (2025). Wireless Mesh Network Implementation Strategy for Wireless Network Improvement and Reliability. Journal of Software Engineering and Information Systems (SEIS), 98–107. Kurniasih, D., & Rusfiana, Y. (2021). Analytical Techniques. Nugroho, H. A. S. A., Hartati, S., & Sonhaji, S. (2023). Comparative analysis of OSPF and static routing protocols for the optimization of xyz high school computer networks. Transformation, 18(2), 1–11. https://doi.org/10.56357/jt.v18i2.310 Oktafiandi, H. (2021). Design and build a wireless mesh network using ad-hoc Optimized Link State Routing (OLSR). Journal of Economics and Informatics Engineering, 9(2), 70–75. Putra, F. P. E., Arissandi, D. E., Rofiqi, A., & Hidayat, M. F. (2025). The Utilization of Mikrotik in Bandwidth Management in School Networks. Journal of Informatics and Computer Technology, 5. Rahman, A., & Nurwarsito, H. (2020). Performance analysis of is-is routing protocol and eigrp routing protocol on mesh topology network. Journal of Information Technology and Computer Science Development, 4(11), 4139–4147. Siddik, M., Lubis, A. P., & Sahren, S. (2023). Optimizing Internet Network Speed in Mts Daarussalam Using the Simple Queue Method. Journal of Science and Social Research, 6(1), 117. https://doi.org/10.54314/jssr.v6i1.1179 Simanjuntak, E. (2021). Analysis of Students' Learning Difficulties in Mixed Calculation Operation Material in Grade IV of Sd Negeri 067246 Medan Academic Year 2020/2021. Siswanto, D. (2021). Implementation of Wireless Mesh Network on Local Area Network (LAN) Network. Journal of Science and Social Research, 4307(1), 20–27. Tarigan, I. S. B. (2020). Analysis of Students' Difficulties in Learning to Listen in Class V of Sdn 048232 Kabanjahe Academic Year 2019/2020. Toyib, R., Wijaya, A., & Apridiansyah, Y. (2024). The implementation of the Point to Point method uses Mikrotik Router Board Type RB411AH for internet network access. Decode: Journal of Information Technology Education, 4(1), 225–238. Yastianto, S. (2021). Design and build a VLAN network using the Routing Information Protocol (RIP) method using a Cisco router in the Department of Computer Engineering of the Police.

Development of an Arduino-Based Truck Load Detection System for Bridge Safety Monitoring Delima Astuti Rambe; Dwi Anggita Ramanda; Husna Juli Gulvira; Muhammad Ilham Siregar; Meiliyani Br Ginting; Ita Margaretta Br Tarigan
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 2 (2026): JCEIT: Journal of Computer Engineering and Information Technology (March 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar

The safety of bridge structures is very important to consider, especially in withstanding the weight of passing trucks. Overloading can cause structural damage that can potentially jeopardize the safety of bridge users. Therefore, a truck load weight detection system is needed that is able to monitor the load in real-time and provide early warning in the event of overloading. This research aims to design and implement an Arduino-based truck load weight detection system installed on the bridge. This system uses a load sensor (load cell) to measure the weight of passing vehicles, where the weight data is then processed by an Arduino arduiuno. The system is equipped with a wireless communication module that allows weight data to be sent directly to the control center or bridge operator. The results of system testing show that this system is able to detect the weight of truck loads with a good level of accuracy. With this system, it is expected to improve bridge safety and provide accurate information related to the distribution of loads passing over the bridge. REFERENCES Arsyad, O. R., & Kartika, K. P. (2021). Design and build safe safety devices using Arduino-based fingerprint sensors. JATI (Student Journal of Informatics Engineering), 5(1), 1–6. Gunawan, G., & Ardiyansyah, M. R. (2023). Design and build an Arduino-based photovoltaic panel performance tester. State Polytechnic at the End of the Line. Handiko, Y. T. (2022). Design and Build a Digital Scale Model Using Load Cell Sensors and IoT-Based Scale Recording. Kartika Riyanti, K. P., Kakaravada, I., & Ahmed, A. A. (2022). An Automatic Load Detector Design to Determine the Strength of Pedestrian Bridges Using Load Cell Sensor Based on Arduino. Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, 4(1), 15–22. https://doi.org/10.35882/ijeeemi.v4i1.3 Kazuya, A. S., Ariyadi, T., Dasmen, R. N., & Fitriani, E. (2024). Design of Digital-Based Scales Equipped with Metal Detectors as Metal Sensors. Journal of Tambusai Education, 8(1), 14261–14277. Khristianto, W. & et al. (2022). Management Information System: The purpose of the Management Information System. In CV. Pena Persada (April Issue). Kurnia, R., Firdaus, R., Lufti, L., & Anshor, M. H. (2019). Load cell sensor automation to overcome vehicle overload. National Journal of Electrical Engineering, 8(2), 81. https://doi.org/10.25077/jnte.v8n2.666.2019 Laili, D. T., & Bahri, S. (2022). Prototype of Car Parking System Using Load Cell Sensor with Android-Based Arduino Mega 2560. Coding Journal of Computers and Applications, 8(1). Nurlaila, N., Paembonan, S., & Suppa, R. (2024). Design Arduino-based vehicle speed detection. Journal of Informatics and Applied Electrical Engineering, 12(3). Pranita, E. (2023). Automatic bridge control uses Arduino-based ultrasonic sensors. ICTEE Journal, 4(2), 13. https://doi.org/10.33365/jictee.v4i2.3143 Rachmawati, P. (n.d.). Digital Scale Simulation Design Using HX711 Sensor with Additional ESP32-Based Buzzer. Ridwan, M., Widiastiwi, Y., Zaidiah, A., Purabaya, R. H., Isnainiyah, I. N., Ardilla, Y., & Rahayu, T. (2021). Management Information Systems. Widina Publisher. Rizal-Alfariski, M., Dhandi, M., & Kiswantono, A. (2022). Automatic Transfer Switch (ATS) Using Arduino Uno, IoT-Based Relay and Monitoring. Journal of Telecommunication Systems, Electronics, Control Systems, Power Systems and Computers, 2(1), 1–8. Rustandi, A. (2020). Monitoring Current and Electrical Power with Notification System from Smartphones in Internet of Things (IoT)-Based Household Electrical Installations. Indonesian Computer University. Sibuea, S., & Saftaji, B. (2020). The design of the vehicle load monitoring system uses load cell sensor technology. Journal of Informatics and Computer Technology, 6(2), 144–156. https://doi.org/10.37012/jtik.v6i2.309 Simanjuntak, R. S. (2023). Design and build "Arduino Nano-based Earthquake Alarm Automatic Switch. Sunardi, R. A., Wijaya, S. H., Hidayat, I., & Noerdyah, P. S. (2024). Design and Build Automatic Door Locks Based on Arduino Arduiunos Using RFID and SIM900 as Security Systems. Journal of Industrial Engineering, Information Systems and Informatics Engineering, 3(1). Widharma, I. G. (2021). Arduiuno Textbook (Chapter Six). Wiguna, A. R. (2020). Analysis of how ultrasonic sensors and servo motors work using Arduino Uno arduiunos for pest control in rice fields. OSF PREPR.

Development of a Student-Centered Digital Mathematics Learning Prototype Using the ADDIE Model to Improve Conceptual Understanding Ita Margaretta Br Tarigan
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 2 (2026): JCEIT: Journal of Computer Engineering and Information Technology (March 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i2.58

Mathematics learning in higher education still faces challenges in the form of low student conceptual understanding due to the dominance of procedural approaches and the suboptimal use of digital technology. Furthermore, there are still gaps in the development of structured, student-centered, and integrated learning designs in the form of prototype-based digital learning systems. This research aims to develop a student-centered digital mathematics learning prototype using the ADDIE model to improve conceptual understanding. This research employed a Research and Development (R&D) method with Analysis and Design stages. The results are a learning prototype design that includes learning objectives, material structure, student-centered learning strategies, digital media, student activities, and an evaluation system. The prototype is designed to support concept visualization, learning interactions, and exploration and problem-solving activities. The results indicate that the developed prototype has the potential to systematically and interactively improve the effectiveness of mathematics learning. In conclusion, this prototype can serve as the basis for developing innovative digital mathematics learning. The novelty of this research lies in the integration of the ADDIE model with a student-centered approach in the design of a comprehensive digital learning prototype. REFERENCES Anisa Ulva Wahyuni & Hasanuddin. (2025). Teknologi Digital dalam pembelajaran Matematika: Tinjauan Bibliometrik terhadap Dampaknya pada Pemahaman Konsep Matematis Siswa. Buana Matematika : Jurnal Ilmiah Matematika Dan Pendidikan Matematika, 15(1), 41–56. https://doi.org/10.36456/buanamatematika.v15i1.10341 Cevikbas, M., Greefrath, G., & Siller, H.-S. (2023). Advantages and challenges of using digital technologies in mathematical modelling education – a descriptive systematic literature review. Frontiers in Education, 8, 1142556. https://doi.org/10.3389/feduc.2023.1142556 Fawns, T., Ross, J., Carbonel, H., Noteboom, J., Finnegan-Dehn, S., & Raver, M. (2023). Mapping and Tracing the Postdigital: Approaches and Parameters of Postdigital Research. Postdigital Science and Education, 5(3), 623–642. https://doi.org/10.1007/s42438-023-00391-y Gourlay, L., Rodríguez-Illera, J. L., Barberà, E., Bali, M., Gachago, D., Pallitt, N., Jones, C., Bayne, S., Hansen, S. B., Hrastinski, S., Jaldemark, J., Themelis, C., Pischetola, M., Dirckinck-Holmfeld, L., Matthews, A., Gulson, K. N., Lee, K., Bligh, B., Thibaut, P., … Knox, J. (2021). Networked Learning in 2021: A Community Definition. Postdigital Science and Education, 3(2), 326–369. https://doi.org/10.1007/s42438-021-00222-y Hafidz, M. A., Herlambang Cahya Pratama, Y., & Maulidya Effendi, P. (2024). Design Thinking: Pengembangan UI/UX Aplikasi Evaluasi Pembelajaran Mata Kuliah Berbasis Web. Jurnal Informatika Polinema, 10(3), 413–420. https://doi.org/10.33795/jip.v10i3.5176 I Gusti Agung Trisna Jayantika & Gaudensia Namur. (2022). PERAN TEKNOLOGI PEMBELAJARAN DALAM MENINGKATKAN LITERASI DIGITAL MATEMATIKA. https://doi.org/10.5281/ZENODO.7033331 Kaban, R., Sembiring, D. J., & Tarigan, I. M. B. (2023). Monitoring System for Student Internships Using the Rapid Application Development (RAD) Method. 15(02). Neef, T., Müller, S., & Mechtcherine, V. (2024). Integrating continuous mineral-impregnated carbon fibers into digital fabrication with concrete. Materials & Design, 239, 112794. https://doi.org/10.1016/j.matdes.2024.112794 Perez, A. S., Nieto-Jalil, J. M., Chim, A. I. T., Huerta, J. M. M., & Cavazos, L. L. (2025). Scientific Research Model Applied to Mathematical Modeling and Prototype Construction. 2025 IEEE Global Engineering Education Conference (EDUCON), 1–7. https://doi.org/10.1109/EDUCON62633.2025.11016324 Sa’dijah, C., Anwar, L., Hidayah, I. R., Abdullah, A. H., & Cahyowati, E. T. D. (2024). Mathematics learning models based on local wisdom of Malang to support critical and creative thinking of secondary school students. 030025. https://doi.org/10.1063/5.0234944 Salinas, P., González-Mendívil, E., Quintero, E., Ríos, H., Ramírez, H., & Morales, S. (2013). The Development of a Didactic Prototype for the Learning of Mathematics through Augmented Reality. Procedia Computer Science, 25, 62–70. https://doi.org/10.1016/j.procs.2013.11.008 Soomro, S. A., Casakin, H., & Georgiev, G. V. (2021). Sustainable Design and Prototyping Using Digital Fabrication Tools for Education. Sustainability, 13(3), 1196. https://doi.org/10.3390/su13031196 Swist, T., Gulson, K. N., & Thompson, G. (2024). Education Prototyping: A Methodological Device for Technical Democracy. Postdigital Science and Education, 6(1), 342–359. https://doi.org/10.1007/s42438-023-00426-4 Syafril, S., Asril, Z., Engkizar, E., Zafirah, A., Agusti, F. A., & Sugiharta, I. (2021). Designing prototype model of virtual geometry in mathematics learning using augmented reality. Journal of Physics: Conference Series, 1796(1), 012035. https://doi.org/10.1088/1742-6596/1796/1/012035 Tarigan, I. M. B., Tarigan, S. J. B., & Ginting, R. B. (2024). Optimalisasi Efektivitas Program MBKM: Sistem Monitoring Berbasis Lokasi dan Analisis aktivitas dengan TF-IDF. 6(1). Tarigan, I. M., Simanjorang, M. M., & Siagian, P. (2022). Analisis Kemampuan Pemecahan Masalah Matematis Siswa Ditinjau dari Perbedaan Gender di SMP N 1 Kuta Buluh. Jurnal Cendekia : Jurnal Pendidikan Matematika, 6(3), 2984–2998. https://doi.org/10.31004/cendekia.v6i3.1791 Taufikurrahman, Budiyono, & Slamet, I. (2021). Development of mathematics module based on meaningful learning. 040032. https://doi.org/10.1063/5.0043239

Real-Time Air Quality Prediction Using Metrologically Calibrated Gas Sensors and Random Forest Algorithm Nurhafiz Ahmad Rangkuti
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 2 (2026): JCEIT: Journal of Computer Engineering and Information Technology (March 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i2.59

The increasing level of urban air pollution requires monitoring system that are capable not only of measurement but also real time prediction. Low coast gas sensor such as MQ-135 are widely used due to their affordability and ease of integration. However, these sensors exhibit limitations in terms of accuracy, signal stability, and drift characteristics. This research proposes a real time air quality prediction model based on gas sensor data using a machine learning approach integrated with metrological calibration. The system consists of a microcontroller base data acquisition module, aserver for data storage, and a predictive model deployed for real time computation. Data were collected over a controlled observation period with fixed sampling intervals. Preprocessing steps included regression based calibration, min max normalization, and noise reduction using a movig avarage filter. Three algorithms were evaluated Linear Regression, Random Forest, and Long Short-Term Memory. Model performance was assessed using Root Mean Square Error, Mean Absolute Error, and coefficient of determination. The results indicate that the Random Forest model achieved the lowest RMSE and demonstrated stable prediction performance under sensor signal fluctuations. The integration of calibration prior to model training significantly improved prediction accuracy compared to models without metrological correction. The proposed system provides reliable real-time air quality prediction and can support intelligent environmental monitoring and local decision-making processes. REFERENCES Alahi, M. E. E., Sukkuea, A., Tina, F. W., & Mukhopadhyay, S. C. (2020). Integration of IoT-enabled technologies for air quality monitoring and prediction. IEEE Internet of Things Journal, 7(10), 9871–9882. https://doi.org/10.1109/JIOT.2020.2994523 Chen, J., Li, X., Wang, Y., & Zhang, H. (2022). Comparative evaluation of machine learning models for air pollution forecasting. Atmospheric Environment, 268, 118804. https://doi.org/10.1016/j.atmosenv.2021.118804 Esposito, E., De Vito, S., Salvato, M., & Bright, V. (2021). Dynamic calibration of low-cost air quality sensors using machine learning techniques. Sensors, 21(12), 3989. https://doi.org/10.3390/s21123989 Gao, L., Zhang, D., & Li, J. (2020). Calibration and drift compensation of gas sensors using data-driven models. Sensors and Actuators B: Chemical, 305, 127451. https://doi.org/10.1016/j.snb.2019.127451 Hernandez, W., & Garcia, R. (2021). Data preprocessing strategies for improving air quality prediction accuracy. Environmental Monitoring and Assessment, 193, 512. https://doi.org/10.1007/s10661-021-09234-5 Khan, M. A., Kumar, R., & Gupta, S. (2023). IoT-based smart air quality monitoring systems: A review of recent developments. Sustainable Computing: Informatics and Systems, 38, 100871. https://doi.org/10.1016/j.suscom.2023.100871 Kim, J., Park, Y., & Lee, K. (2022). Impact of sensor uncertainty on machine learning-based environmental prediction systems. IEEE Transactions on Instrumentation and Measurement, 71, 1–10. https://doi.org/10.1109/TIM.2022.3145678 Kumar, P., Morawska, L., Martani, C., & Biskos, G. (2022). The rise of low-cost sensing for managing air pollution in cities. Environment International, 164, 107253. https://doi.org/10.1016/j.envint.2022.107253 Li, Z., Zhao, Y., Sun, W., & Chen, Q. (2023). Time-series prediction of air quality using LSTM and ensemble learning methods. Environmental Modelling & Software, 162, 105634. https://doi.org/10.1016/j.envsoft.2023.105634 Liu, H., Wei, X., & Zhang, Q. (2023). Hybrid deep learning architecture for spatiotemporal air quality forecasting. Applied Soft Computing, 134, 110029. https://doi.org/10.1016/j.asoc.2023.110029 Maag, B., Zhou, Z., & Thiele, L. (2021). A survey on sensor calibration in air quality monitoring deployments. ACM Computing Surveys, 54(3), 1–36. https://doi.org/10.1145/3448304 Park, S., Kim, D., & Lee, H. (2021). Noise reduction techniques for low-cost environmental sensor data. IEEE Sensors Journal, 21(14), 15947–15956. https://doi.org/10.1109/JSEN.2021.3071123 Rahman, M. M., Islam, M. R., & Hossain, M. S. (2021). Edge-based real-time environmental monitoring using machine learning. Future Generation Computer Systems, 121, 87–97. https://doi.org/10.1016/j.future.2021.03.021 Singh, A., Gupta, R., & Sharma, N. (2022). Ensemble learning models for urban air quality prediction. Environmental Science and Pollution Research, 29, 52312–52325. https://doi.org/10.1007/s11356-022-19654-3 Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., & Bonavitacola, F. (2022). Evaluation of low-cost gas sensors for air quality monitoring applications. Atmospheric Measurement Techniques, 15(2), 475–489. https://doi.org/10.5194/amt-15-475-2022 Torres, J., Martinez, A., & Ruiz, D. (2021). Real-time environmental monitoring framework integrating IoT and AI. Computer Networks, 191, 107977. https://doi.org/10.1016/j.comnet.2021.107977 Wang, T., Li, M., & Chen, L. (2023). Performance comparison of regression algorithms for PM2.5 prediction. Atmospheric Pollution Research, 14(1), 101601. https://doi.org/10.1016/j.apr.2022.101601 World Health Organization. (2023). Global air quality guidelines update 2023. WHO Press. Zhang, Y., Ding, A., Mao, H., & Fu, C. (2021). Machine learning approaches for air pollution prediction: A systematic review. Atmospheric Research, 250, 105348. https://doi.org/10.1016/j.atmosres.2020.105348 Zhou, X., Wang, S., & Liu, J. (2022). Real-time air quality prediction based on hybrid machine learning framework. IEEE Access, 10, 44321–44333. https://doi.org/10.1109/ACCESS.2022.3167890

Performance Evaluation of HTB-Based Bandwidth Management for Campus Networks Using MikroTik Routerboard Sumarlin; Iscar Meriah Zai; Candra Irawan Batee; Naomi Rut M. Siboro; Tuboy Martin Gea; Hotma Mentalita
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 3 (2026): JCEIT: Journal of Computer Engineering and Information Technology (July 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i3.60

The rapid growth of internet usage in campus environments, driven by academic, administrative, and digital learning activities, has led to significant increases in data traffic that can impact network performance and stability. At ITB Indonesia, the computer network relies on MikroTik RouterBoard as a core device for traffic management. However, during peak usage periods, network congestion may occur, necessitating a comprehensive performance analysis. This study aims to evaluate the performance of the MikroTik RouterBoard network in handling surges in data traffic within the ITB Indonesia campus. The research employs a quantitative experimental approach by simulating different traffic conditions, including normal and peak loads. Data were collected using monitoring tools integrated within the MikroTik system, focusing on key performance indicators such as throughput, latency, packet loss, and CPU utilization. The results indicate that the network performs efficiently under normal conditions, maintaining stable throughput and low latency. However, under high traffic loads, performance degradation is observed, including increased latency, higher CPU usage, and slight packet loss. The application of traffic management techniques, such as bandwidth allocation and load balancing, significantly enhances network performance. Therefore, MikroTik RouterBoard remains an effective solution when supported by proper optimization strategies. REFERENCES Adisetiawan, R. A. R. (2025). Transformation of Education in the Era of Digitalization Efforts Towards Technology-Based Education. Karapan Network Journal: Journal Computer Technology And Mobile Ad Hoc Network, 1(01). Aprelyani, S. (2025). Factors that affect network performance: signal quality and bandwidth. Greenation Journal of Engineering Sciences, 3(2), 85–92. Aritonang, M. A. S., & Simanullang, M. J. (2025). Application of a Network Monitoring System based on SNMP for early detection of network disturbances. Journal of Artificial Intelligence Technology Studies, 5(3), 735–742. Aulia, B. W., Rizki, M., Prindiyana, P., & Surgana, S. (2023). The crucial role of computer networks and databases in the digital era. Journal of Information Systems and Information Technology, 1(1), 9–20. Dachi, A. C., & Noprisson, H. (2025). Mikrotik Firewall Implementation Model In Traffic Management And Network Security. Jsai (Journal of Scientific And Applied Informatics), 8(3), 788–793. Dora Sandova, & Cahyo Prihantoro. (2021). Traffic Analysis on the LAN Network. Jsai: Journal of Scientific And Applied Informatics, 4(3), 329–337. Fitrian, H. P., Difa, D. H., Melianti, V. A., & Ramdhani, F. M. (2025). Analysis of factors that affect the performance of computer networks in a multi-user environment. Tamika: Journal of Informatics Management and Computerized Accounting, 5(2), 410–415. Kuncoro, W. A., & Santoso, L. (2025). Network Congestion Due to Social Media: Analyzing Its Impact on the Efficiency of Wireless Data Transmission. Scientific Journal of Information Systems, 4(2), 130–141. Nabawi, M., Permana, D. S., & Subekti, R. (2025). Design and implementation of internet connection management with load balancing and failover methods at Trans Coffee headquarters. National Journal of Informatics (Junif), 4(2), 1–13. Nasrullah, M. G., Heryana, N., & Solehudin, A. (2024). Bandwidth management uses the Token Bucket Hierarchical method on internet speed limiting. Jati (Student Journal of Informatics Engineering), 8(2), 2291–2296. Ndun, Y. J. (2025). The implementation of VLAN (Virtual Local Area Network) to improve the security and efficiency of the Dapodik application network at Oesusu Elementary School, Kupang Regency. Journal of Informatics and Applied Electrical Engineering, 13(3). Octavian, A. (2024). The redundancy network design uses the concept of etherchannel and hsrp with intervlan routing at Pln Uid Jakarta Raya. Journal of Informatics and Applied Electrical Engineering, 12(2). Pragasta, Y. (2025). Application of Load Balancing Technology on Mikrotik Routers with the Peer Connection Classifier Method. Digital Transformation Technology, 5(2), 131–140. Pramudito, T. D., Pranoto, W. J., & Hallim, A. (2025). Analysis of the quality of 4g LTE network services using the Walk Test method and Qos (Quality of Service) measurement at Samarinda Central Plaza. Pendas: Scientific Journal of Basic Education, 10(01), 341–360. Prasetio, D., Yuniar, R. O., Fahlewi, F., Agustina, D., Citra, E. J., & Rahman, A. (2025). Analysis of students' habits of using the internet to study at Baturaja University. Journal of Computer Innovation (Inokom), 1(3), 125–134. Prasetyo, F. H. P., Infitharina, E., & Febriyansyah, M. (2025). Application of the Network Development Life Cycle (NDLC) method in the development of computer networks. Journal Of Informatics And Communication Technology (JCT), 7(1), 80–87. Pratama, N. M. W. P., Fatchurrohman, F., & Susanto, I. B. (2024). Optimization of the internet network by using simple queue and microtik firewall at Malang City State Junior High School. Jati (Student Journal of Informatics Engineering), 8(6), 12828–12835. Rahman, T., Rafi, A. P., & Rifai, B. (2026). Implementation of VLAN on WLAN for optimization of enterprise network management. Technology, 20(1), 207–219. Rivaldi, K., & Purnama, G. (2025). Design and implementation of monitoring of computer network device infrastructure in data centers and informatics facilities through the application of Zabbik Network Engineering. Journal of Adijaya Multidisciplinary, 3(04), 589–611. Rizky, M. A. H., Solehudin, A., & Nurkifli, E. H. (2024). Bandwidth optimization on the internet network uses the simple queue and peer connection queue methods. Jati (Student Journal of Informatics Engineering), 8(4), 7856–7863. Subektiningsih, S., Renaldi, R., & Ferdiansyah, P. (2022). Comparative analysis of Tiphon Standard Qos parameters on wireless networks in the application of the PCQ method. Explore, 12(1), 57–63. Sufa, A. M., & Nasution, M. I. P. (2025). Perceptions and Barriers to the Use of Web-Based Academic Information Systems at the State Islamic University of North Sumatra. Scientific Journal of Economics, Management, Business and Accounting, 2(3), 347–357. Utomo, A. A. S., Supandi, S., & Rozzaqi, A. R. (2025). Analysis of wireless network performance based on Qos (throughput, delay, packet loss) parameters on variations in traffic during operational hours in users in the school environment at SMP Negeri 1 Ngaringan. Sibatik Journal: Scientific Journal of Social, Economic, Cultural, Technological, and Educational, 4(9), 2691–2970.

Implementation and Optimization of a Voucher-Based Internet Access System Using MikroTik RB941 for RT/RW Net Networks Adelia Amanda; Alfridom Wau; Dede Priyandi Bawamenewi; Siti Rahmayani Sitorus; Romulo P Aritonang; Meiliyani Br Ginting
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 3 (2026): JCEIT: Journal of Computer Engineering and Information Technology (July 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i3.61

The increasing demand for stable, secure, and affordable internet access in RT/RW Net environments requires more effective network management strategies. Although MikroTik-based hotspot systems have been widely implemented, many existing studies primarily focus on basic hotspot deployment without integrating bandwidth optimization and voucher-based user authentication into a unified network management framework. Consequently, network administrators continue to encounter challenges related to unequal bandwidth allocation, inefficient user management, and limited access control. This study aims to implement and optimize a voucher-based internet access system using the MikroTik RB941 router to improve bandwidth management, user authentication, and network administration in RT/RW Net networks. The research employed an experimental approach involving network design, router configuration, hotspot implementation, voucher generation, bandwidth management using the Per Connection Queue (PCQ) method, and system testing. The implementation utilized MikroTik RouterOS, Winbox, and Mikhmon to configure and manage the network services. The proposed system successfully established centralized user authentication, automated voucher management, and more organized bandwidth allocation under the implemented testing scenario. The novelty of this study lies in the integration of Time-Based and Volume-Based voucher management with PCQ bandwidth allocation within a single MikroTik RB941 framework, providing a more comprehensive network management approach than conventional hotspot implementations. The findings indicate that the proposed configuration provides a structured and practical solution for managing internet access in RT/RW Net environments and establishes a foundation for future evaluations of network performance using Quality of Service (QoS) parameters. REFERENCES Anwar, I. D., & Akbar, Y. (2024). Network Bandwidth Management with Per Connection Queue (PCQ) Method on Mikrotik at Masterpiece Family Karaoke Tebet. Indonesian Journal: Informatics and Communication Management, 5(3), 3125–3137. Aulia, B. W., Rizki, M., Prindiyana, P., & Surgana, S. (2023). The crucial role of computer networks and databases in the digital era. Journal of Information Systems and Information Technology, 1(1), 9–20. Darmawan, M. D., & Rudianto, R. (2025). Design and build internet billing based on using Mikhmon-based wifi vouchers. Jati (Journal of Informatics Engineering Students), 9(6), 9373–9381. Ernawati, D., & Setiawan, A. (2025). Implementation of a web-based Mikrotik hotspot voucher sales management information system to improve the efficiency of isk-net ISP services. Journal of Bhinneka Community Service, 4(2), 2406–2418. Faiha, H. (2024). Network Security Analysis Using Mikrotik Router OS to Optimize Computer Network Security in Improving the Quality of Public Services at the Lubuk Sikaping Religious Court Office with Port Knocking and ACL (Access Control List) methods. Fajaruddin, H., & Kurniawan, A. (2025). The use of wireless fidelity (Wi-Fi) technology in the development of RT-RW Net to grow the digital economy. National Journal of Informatics (June), 4(2), 42–49. Fauzi, R., Zainy, A., Lubis, I. S., Haqi, A. B., Akhir, A. Z., Kumana, B., Simamora, N., & Juliana, R. (2023). Installation of Mikrotik on Virtualbox and connection between Mikrotik in Virtualbox and Winbox at Smk S Teruna Padang Sidempuan. ADAM Journal: Journal of Community Service, 2(1), 106–118. Gunawan, G. G. I., & Alijoyo, F. A. (2024). Design and build a wifi voucher purchase application with a payment gateway and radius. Journal of Business Information Technology and Systems, 6(2), 310–321. Haqi, A. B., Siregar, D. A., Mutiara, M., Lubis, N. F., & Akhir, A. Z. (2023). Introduction to Basic Computer Networks at SMK Negeri 1 Batang Onang. ADAM Journal: Journal of Community Service, 2(2), 293–303. Ikhwandi, M. I., & Azinar, A. W. (2025). Wireguard implementation as a connection using mikrotik routing. Smatics Journal, 15(02), 292–301. Ismawati, I., & Softianto, S. (2025). Implementation of Mikrotik RouterOS-Based Voucher Hotspot System for Internet Access Management on LAN Networks. Karapan Network Journal: Journal of Computer Technology and Mobile Ad Hoc Network, 1(01). Lestyaningrum, I. K. M., Trisiana, A., Safitri, D. A., Pratama, A. Y., & Wahana, T. P. (2022). Global education based on digital technology in the millennial era. Unisri Press. Lukito, K. D., Liliana, L., & Palit, H. N. (n.d.). Creation of crowdsource applications for Android-based household services. Manuaba, I. B. K., Agus, F., Ramayu, I. M. S., Saputro, V. A., Pakpahan, A. V., Skawanti, J. R., & Judijanto, L. (2025). Computer Network. PT. Sonpedia Publishing Indonesia. Masruha, M. (2022). Islamic Law Perspective on the Voucher System at KPRI Nusa Indah. JURISY: Sharia Scientific Journal, 2(1). Mojasa, B. W. (2024). Criminal liability for misuse of RT/RW Net by using resold broadband internet. Journal of Legal Compilation, 9(2), 223–234. Nugroho, P. A. (2022). The design of the RT/RW Net computer network uses a Power Line (PLC) communication line in Taman Berdikari Sentosa housing. JEIS: Swadharma Journal of Electrical and Informatics, 2(1), 9–14. Prameswari, I. A., Noviyanti, Y., & Susilowati, T. (2024). Introduction to Information Systems. NEM Publishers. Putri, D. L., Pratama, A. R., & Nurjanah, L. (2025). Optimizing Digital Access through Internet Network Planning in Dawuhan Village. JUPAMU: Journal of Multidisciplinary Community Service, 1(1), 25–36. Rahman, R. (2024). Implementation of Mikrotik-Based RT RW Net Network Network and Hotspot Networks with Mikhmon Feature in Fast. Net. Rusyunizal, D., & Karim, H. A. (2025). Transparency of Web-Based Islamic Education Management System in the Digital Era. Journal of Educational Management, 10(4), 3319–3331. Sanjaya, R. (2020). 21 Reflections on Online Learning in Emergency Times. SCU Knowledge Media. Sari, A. I., Syaifuddin, M., & Andriani, T. (2023). Optimizing the strategic management of educational infrastructure. Journal of Multidisciplinary Sciences, 1(4), 814–822. Ulfa, H., Basuki, A. I., Suranegara, G. M., & Fauzi, A. (2024). DDoS Protection System for SDN Network Based on Multi Controller and Load Balancer. Systematic: Journal of Information Systems, 13(2), 555–571. Yusman, Y., Putra, R. R., & Sinaga, I. (2024). The application of information systems to improve governance and public services in the digital era. Compatible with Media Technology.

Design of Augmented Reality-Based Learning Media Using the TPACK Framework for Visualizing Graph Concepts in Discrete Mathematics Ita Margaretta Br Tarigan
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 3 (2026): JCEIT: Journal of Computer Engineering and Information Technology (July 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i3.62

The advancement of educational technology has created opportunities to develop innovative learning media that can enhance students’ understanding of complex and abstract concepts. In the Discrete Mathematics course, graph theory is considered one of the most challenging topics because it involves abstract relationships among vertices and edges, making concepts such as paths, cycles, trees, and spanning trees difficult for students to comprehend through conventional two-dimensional learning media. Therefore, this study aims to design an Augmented Reality (AR)-based learning media using the Technological Pedagogical Content Knowledge (TPACK) framework to support graph concept visualization in higher education. This study employed the Design and Development Research (DDR) method, consisting of problem identification, literature review, needs analysis, TPACK analysis, learning media design, system architecture design, user interface design, prototype development, and expert validation. The results produced a conceptual model of AR-based learning media that integrates technological, pedagogical, and content knowledge within a unified framework. The proposed design includes learning materials, AR-based three-dimensional graph visualization, practice exercises, evaluations, and user guidance features. The practical implication of this study is the provision of a systematic design framework that can serve as a reference for the development of AR-based educational applications in mathematics and computer science education. In conclusion, the integration of AR technology and the TPACK framework has the potential to facilitate the visualization of abstract graph concepts and support more interactive learning experiences. Further research is recommended to implement and evaluate the effectiveness of the proposed design in actual learning environments. REFERENCES Adipat, S. (2021). Developing Technological Pedagogical Content Knowledge (TPACK) through Technology-Enhanced Content and Language-Integrated Learning (T-CLIL) Instruction. Education and Information Technologies, 26(5), 6461–6477. https://doi.org/10.1007/s10639-021-10648-3 Belda-Medina, J., & Calvo-Ferrer, J. R. (2022). Integrating augmented reality in language learning: Pre-service teachers’ digital competence and attitudes through the TPACK framework. Education and Information Technologies, 27(9), 12123–12146. https://doi.org/10.1007/s10639-022-11123-3 Bödding, R., & Maier, G. W. (2026). Investigating the Modality and Signalling Principles in Immersive Augmented Reality Learning Environments. Journal of Computer Assisted Learning, 42(1), e70183. https://doi.org/10.1002/jcal.70183 Chen, H.-C., & Wong, L.-H. (2026). The MTA-TPACK Dynamic Collaboration Spiral: Making Pedagogical Thinking Visible in Human–AI Scientific Visualization for Sustainable Teacher Innovation. Sustainability, 18(6), 2718. https://doi.org/10.3390/su18062718 Choi, B., & Young, M. F. (2021). TPACK-L: Teachers’ pedagogical design thinking for the wise integration of technology. Technology, Pedagogy and Education, 30(2), 217–234. https://doi.org/10.1080/1475939X.2021.1906312 Irwanto, I. (2021). Research Trends in Technological Pedagogical Content Knowledge (TPACK): A Systematic Literature Review from 2010 to 2021. European Journal of Educational Research, volume–10–2021(volume–10–issue–4–october–2021), 2045–2054. https://doi.org/10.12973/eu-jer.10.4.2045 Kim, W., & Ok, M. W. (2021). A Comparison of Special and General Educators’Usage, Perceptions, and TPACK Levels Regarding the Use of Augmented and Virtual Reality. Journal of Special Education : Theory and Practice, 22(2), 15–44. https://doi.org/10.19049/JSPED.2021.22.2.02 Lachner, A., Fabian, A., Franke, U., Preiß, J., Jacob, L., Führer, C., Küchler, U., Paravicini, W., Randler, C., & Thomas, P. (2021). Fostering pre-service teachers’ technological pedagogical content knowledge (TPACK): A quasi-experimental field study. Computers & Education, 174, 104304. https://doi.org/10.1016/j.compedu.2021.104304 Nilsson, P. (2024). From PCK to TPACK - Supporting student teachers’ reflections and use of digital technologies in science teaching. Research in Science & Technological Education, 42(3), 553–577. https://doi.org/10.1080/02635143.2022.2131759 Nusroh, H., Khalif, M. A., & Saputri, A. A. (2022). Developing Physics Learning Media Based on Augmented Reality to Improve Students’ Critical Thinking Skills. Physics Education Research Journal, 4(1), 23–28. https://doi.org/10.21580/perj.2022.4.1.10912 Prihantini, P., Rukmini, A., Mahardhani, A. J., & Mustika, A. (2026). Mapping the Progress of Technological Pedagogical and Content Knowledge Research: Bibliometric Analysis. Jurnal Pembelajaran, Bimbingan, Dan Pengelolaan Pendidikan, 6(2), 12. https://doi.org/10.17977/um065.v6.i2.2026.12 Putri, N. P. D. M., Suharta, I. G. P., & Astawa, I. W. P. (2022). DEVELOPMENT OF AUGMENTED REALITY BASED GEOMETRY LEARNING MEDIA ORIENTED TO BALINESE ARCHITECTURE TO IMPROVE ABILITY STUDENT MATHEMATICS SPATIAL. International Journal of Engineering Technologies and Management Research, 9(10), 26–42. https://doi.org/10.29121/ijetmr.v9.i10.2022.1183 Santos, J. M., & Castro, R. D. R. (2021). Technological Pedagogical content knowledge (TPACK) in action: Application of learning in the classroom by pre-service teachers (PST). Social Sciences & Humanities Open, 3(1), 100110. https://doi.org/10.1016/j.ssaho.2021.100110 Schwajda, D., Friedl, J., Pointecker, F., Jetter, H.-C., & Anthes, C. (2023). Transforming graph data visualisations from 2D displays into augmented reality 3D space: A quantitative study. Frontiers in Virtual Reality, 4, 1155628. https://doi.org/10.3389/frvir.2023.1155628 Sriadhi, S., Hamid, A., Sitompul, H., & Restu, R. (2022). Effectiveness of Augmented Reality-Based Learning Media for Engineering-Physics Teaching. International Journal of Emerging Technologies in Learning (iJET), 17(05), 281–293. https://doi.org/10.3991/ijet.v17i05.28613 Tarigan, I. M. B. (2026). Development of a Student-Centered Digital Mathematics Learning Prototype Using the ADDIE Model to Improve Conceptual Understanding. Journal of Computer Engineering and Information Technology, 2(1). Tarigan, I. M., Simanjorang, M. M., & Siagian, P. (2022). Analisis Kemampuan Pemecahan Masalah Matematis Siswa Ditinjau dari Perbedaan Gender di SMP N 1 Kuta Buluh. Jurnal Cendekia : Jurnal Pendidikan Matematika, 6(3), 2984–2998. https://doi.org/10.31004/cendekia.v6i3.1791 Yang, L., Susanti, W., Hajjah, A., Marlim, Y. N., & Tendra, G. (2022). Perancangan Media Pembelajaran Matematika Menggunakan Teknologi Augmented Reality. Edukasi: Jurnal Pendidikan, 20(1), 122–136. https://doi.org/10.31571/edukasi.v20i1.3830 Yanuarto, W. N., & Iqbal, A. M. (2022). The Augmented Reality Learning Media to Improve Mathematical Spatial Ability in Geometry Concept. Edumatica : Jurnal Pendidikan Matematika, 12(01), 30–40. https://doi.org/10.22437/edumatica.v12i01.17615 Yeh, Y.-F., Chan, K. K. H., & Hsu, Y.-S. (2021). Toward a framework that connects individual TPACK and collective TPACK: A systematic review of TPACK studies investigating teacher collaborative discourse in the learning by design process. Computers & Education, 171, 104238. https://doi.org/10.1016/j.compedu.2021.104238

Analysis of Accuracy and Computational Efficiency of Android-Based Palm Maturity Classification System Using K-Nearest Neighbor Method Ahmad Ridwan
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 3 (2026): JCEIT: Journal of Computer Engineering and Information Technology (July 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i3.63

Accurately determining the ripeness of oil palm Fresh Fruit Bunches (FFB) is crucial to maximizing the quality of Crude Palm Oil (CPO). Conventional methods rely on visual assessment or laboratory tests that are destructive, expensive, and inefficient at the field scale. This study proposes an android-based, non-destructive FFB ripeness classification system that uses color feature extraction and the K-Nearest Neighbors (K-NN) algorithm. A total of 65 FFB images directly from the tree are divided into training data (50 images) and test data (15 images) with three ripeness classes: raw, ripe, and overripe. Features are extracted through multilevel color thresholding segmentation, then calculated using RGB color averages, RGB normalization, and four Vegetation Indices (NDVI, SAVI, EVI, VARI). The test results show that the combination of Vegetation Indices with K-NN achieves the highest accuracy, 98% on the training data and 93.33% on the test data, with only one classification error. The system runs on-device with an average computation time of 3.3 seconds per image, demonstrating sufficient efficiency for real-time applications in plantations. This study concludes that the mobile approach based on K-NN and the Vegetation Index is worthy of adoption as a fast, accurate, and non-destructive harvest decision-support tool. However, further lighting optimization and dataset expansion are still needed for broader generalization. REFERENCES Alfatni, M. S. M., Mohamed Shariff, A. R., Ben Saaed, O. M., Albhbah, A. M., & Mustapha, A. (2020). Colour Feature Extraction Techniques for Real Time System of Oil Palm Fresh Fruit Bunch Maturity Grading. IOP Conference Series: Earth and Environmental Science, 540(1). https://doi.org/10.1088/1755-1315/540/1/012092 Bannari, A., Asalhi, H., & Teillet, P. M. (2002). Transformed difference vegetation index (TDVI) for vegetation cover mapping. IEEE International Geoscience and Remote Sensing Symposium, 5, 3053–3055 vol.5. https://doi.org/10.1109/IGARSS.2002.1026867 Barrera, K., Rodellar, J., Alférez, S., & Merino, A. (2023). Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks. Computer Methods and Programs in Biomedicine, 240. https://doi.org/10.1016/j.cmpb.2023.107629 Boucetta, C., Hussenet, L., & Herbin, M. (2023). Improved Euclidean Distance in the K Nearest Neighbors Method. In U. R. Krieger, G. Eichler, C. Erfurth, & G. Fahrnberger (Eds.), the 23rd International Conference on Innovations for Community Services (pp. 315–324). Springer Nature Switzerland. Burge, M. J. (2022). Digital Image Processing: An Algorithmic Introduction. Springer Nature. Cherie, D., Herodian, S., Ahmad, U., Mandang, T., & Makky, M. (2015). Optical characteristics of oil palm fresh fruits bunch (FFB) under three spectrum regions influence for harvest decision. International Journal on Advanced Science, Engineering and Information Technology, 5(3), 255–263. https://doi.org/10.18517/ijaseit.5.3.534 Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/https://doi.org/10.1016/0034-4257(88)90106-X Khan, A. I., & Al-Habsi, S. (2020). Machine Learning in Computer Vision. Procedia Computer Science, 167(2019), 1444–1451. https://doi.org/10.1016/j.procs.2020.03.355 Makky, M. (2016). Trend in non-destructive quality inspections for oil palm fresh fruits bunch in Indonesia. International Food Research Journal, 23(1), 81–90. https://doi.org/10.4149/neo_2010_01_079 Makky, M., Soni, P., & Salokhe, V. M. (2014). Automatic non-destructive quality inspection system for oil palm fruits. International Agrophysics, 28(3), 319–329. https://doi.org/10.2478/intag-2014-0022 Naji, S., Jalab, H. A., & Kareem, S. A. (2019). A survey on skin detection in colored images. Artificial Intelligence Review, 52(2), 1041–1087. https://doi.org/10.1007/s10462-018-9664-9 Pertanian RI, K. (2023). Statistik Perkebunan Kelapa Sawit Indonesia 2022. In Direktorat Jenderal Perkebunan. https://ditjenbun.pertanian.go.id Purbolingga, Y., Ridwan, A., & Putri, D. M. (2025). A Machine Learning-Based Ambiguous Alphabet Recognition for Indonesian Sign Language System (SIBI). CogITo Smart Journal, 11(1), 1–14. https://doi.org/10.31154/cogito.v11i1.816.1-14 Resta, F. S. A., Setiawan, R., Rivai, M., Arif, R. El, Natawijaya, A., & Hadad, A. G. Al. (2026). Multimodal Radar-Vision for Oil Palm Fresh Fruit Bunch Ripeness Classification. IEEE Access, 14, 42975–42991. https://doi.org/10.1109/ACCESS.2026.3675310 Ridwan, A., Purbolingga, Y., & Hanisah, H. (2024). Utilizing Convolutional Neural Network for Learning Web-Based Braille Letter Classification System. Journal of Computer Networks, Architecture and High Performance Computing, 6(1). https://doi.org/10.47709/cnahpc.v6i1.3386 Saad, B., Ling, C. W., Jab, M. S., Lim, B. P., Mohamad Ali, A. S., Wai, W. T., & Saleh, M. I. (2007). Determination of free fatty acids in palm oil samples using non-aqueous flow injection titrimetric method. Food Chemistry, 102(4), 1407–1414. https://doi.org/https://doi.org/10.1016/j.foodchem.2006.05.051 Srivastava, S., & Sadistap, S. (2018). Data processing approaches and strategies for non-destructive fruits quality inspection and authentication: a review. Journal of Food Measurement and Characterization, 12(4), 2758–2794. https://doi.org/10.1007/s11694-018-9893-2 Suharjito, Elwirehardja, G. N., & Prayoga, J. S. (2021). Oil palm fresh fruit bunch ripeness classification on mobile devices using deep learning approaches. Computers and Electronics in Agriculture, 188, 106359. https://doi.org/https://doi.org/10.1016/j.compag.2021.106359 Wang, A. X., Chukova, S. S., & Nguyen, B. P. (2023). Ensemble k-nearest neighbors based on centroid displacement. Information Sciences, 629, 313–323. https://doi.org/https://doi.org/10.1016/j.ins.2023.02.004 Yan, K., Gao, S., Yan, G., Ma, X., Chen, X., Zhu, P., Li, J., Gao, S., Gastellu-Etchegorry, J. P., Myneni, R. B., & Wang, Q. (2025). A global systematic review of the remote sensing vegetation indices. International Journal of Applied Earth Observation and Geoinformation, 139(November 2024), 104560. https://doi.org/10.1016/j.jag.2025.104560

Early Warning System for Student Academic Risk Prediction Using Gradient Boosting with SMOTE-Based Class Balancing Roberto Kaban; Jamaludin Bin Sallim; Susmanto Susmanto; Rozlina Binti Mohamed
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 3 (2026): JCEIT: Journal of Computer Engineering and Information Technology (July 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i3.64

This study proposes a machine learning-based Early Warning Academic System for predicting student academic risk levels using historical academic performance data from ITB Indonesia. The aim of this research is to identify at-risk students at an early stage using routinely collected academic indicators, including Quiz Score, Attendance, Assignment Score, and Midterm Score. The proposed framework integrates Gradient Boosting as the classification model, Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance, and Stratified 5-Fold Cross Validation to ensure robust performance evaluation. The model classifies students into three categories, namely Safe, Warning, and Risk. Experimental results show that the proposed model achieves an accuracy of 80.00%, macro F1-score of 78.24%, Cohen’s Kappa of 0.6306, and Matthews Correlation Coefficient of 0.6311, indicating strong and reliable predictive performance under imbalanced data conditions. ROC-AUC analysis further confirms strong discriminative capability, particularly for identifying Risk students. Feature importance analysis reveals that Midterm Score is the most influential predictor (43.49%), followed by Quiz Score (25.60%) and Assignment Score (21.99%), while Attendance contributes 8.93%. These findings indicate that academic performance indicators, especially midterm assessments, play a critical role in early risk detection. The results demonstrate that effective early warning prediction can be achieved using a limited set of routinely available academic variables, providing a practical and scalable approach to support early intervention strategies in higher education institutions. REFERENCES Akello, E. F., Ijiga, O. M., & Idoko, I. P. (2026). Sequence-Aware Learning Analytics for Early Identification of At-Risk Academic Trajectories in Higher Education Using Transformer Models. International Journal of Innovative Science and Research Technology, 818. https://doi.org/10.38124/ijisrt/26jan563 Albreiki, B., Habuza, T., & Zaki, N. (2023). Extracting topological features to identify at-risk students using machine learning and graph convolutional network models. International Journal of Educational Technology in Higher Education, 20(1), 23. https://doi.org/10.1186/s41239-023-00389-3 Almalawi, A., Soh, B., Li, A., & Samra, H. (2024). Predictive Models for Educational Purposes: A Systematic Review. Big Data and Cognitive Computing, 8(12), 187. https://doi.org/10.3390/bdcc8120187 Alomari, S. A., Aldiabat, K., Hashim, F. A., Zitar, R. A., Liu, Z., Migdady, H., Hu, G., Smerat, A., & Abualigah, L. (2025). Supervised Learning: Teaching Machines with Labeled Data. In L. Abualigah, Mastering the Minds of Machines (1st ed., pp. 26–33). CRC Press. https://doi.org/10.1201/9781003516385-4 Aluso, L., & Enyejo, J. O. (2025). Using XGBoost and Time-Series Forecasting to Predict Student Academic Trajectories in Educational Analytics Platforms. International Journal of Innovative Science and Research Technology, 143. https://doi.org/10.38124/ijisrt/25dec159 Angeioplastis, A., Aliprantis, J., Konstantakis, M., & Tsimpiris, A. (2025). Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques. Computers, 14(3), 83. https://doi.org/10.3390/computers14030083 Aslam, Z., Missen, M. M. S., Ghaffar, A. A., Mehmood, A., Villar, M. G., Alvarado, E. S., & Ashraf, I. (2025). Advancing fake news combating using machine learning: A hybrid model approach. Knowledge and Information Systems, 67(12), 12137–12177. https://doi.org/10.1007/s10115-025-02588-y Bagawade, R. P., & Thirupurasundari. (2025). Ensemble Machine Learning Techniques. International Journal of Emerging Technology and Advanced Engineering, 15(4), 15–23. https://doi.org/10.46338/ijetae0425_02 Călin, S. (2025). Handling Imbalanced Data: The SMOTE Technique. 2025 17th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 1–5. https://doi.org/10.1109/ECAI65401.2025.11095450 Charaya & Sonia. (2026). Towards Accurate Student Performance Prediction: An Assessment of Machine Learning Models and Metrics. International Journal of Latest Technology in Engineering Management & Applied Science, 15(1), 600–610. https://doi.org/10.51583/IJLTEMAS.2026.150100053 Chukwura. (2023). A comparative study of several classification metrics and their performances on data. World Journal of Advanced Engineering Technology and Sciences, 8(1), 308–314. https://doi.org/10.30574/wjaets.2023.8.1.0054 Elreedy, D., Atiya, A. F., & Kamalov, F. (2024). A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning. Machine Learning, 113(7), 4903–4923. https://doi.org/10.1007/s10994-022-06296-4 Ersozlu, Z., Taheri, S., & Koch, I. (2024). A review of machine learning methods used for educational data. Education and Information Technologies, 29(16), 22125–22145. https://doi.org/10.1007/s10639-024-12704-0 Ganage, S. S. (2026). Machine Learning Techniques for Student Performance Prediction and Academic Analytics. International Journal of Engineering Research and Science & Technology, 22(1(S)), 311–314. https://doi.org/10.62643/ijerst.2026.v22.i1(S).2064 Ge, X., Fang, C., Li, X., Sun, W., Wu, D., Zhai, J., Lin, S.-W., Zhao, Z., Liu, Y., & Chen, Z. (2025). Machine Learning for Actionable Warning Identification: A Comprehensive Survey. ACM Computing Surveys, 57(2), 1–35. https://doi.org/10.1145/3696352 Giap, N., Nghiem, T. L., Ngo, T. H., Nguyen, M. T. L., & Phung, H. Q. (2024). Increment of Academic Performance Prediction of At‐Risk Student by Dealing With Data Imbalance Problem. Applied Computational Intelligence and Soft Computing, 2024(1), 4795606. https://doi.org/10.1155/2024/4795606 Idhmad, A., Kaicer, M., Nejjar, C., & Benjouad, A. (2024). Intelligent Bankruptcy Prediction Models Involving Corporate Governance Indicators, Financial Ratios and SMOTE. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 12(1), 233–244. https://doi.org/10.52549/ijeei.v12i1.5241 Kaban, R., Rachman, A. I., Sari, I. P., Zulherry, A., Basri, M., Fadilah, N., Maulidiyah, N., Sembiring, D. J., Pratama, G. U., Ronal, R., & others. (2026). Pengantar Artificial Intelligence, Machine Learning, dan Big Data. Yayasan Kita Menulis. Kabumba, R. K., Mbayandjambe, A. M., Tashev, T., Slavov, V., Kambale, W. V., Ngoie, R.-B. M., Kyamakya, K., & Kasereka, S. K. (2025). Predictive Modeling of Academic Success Using Machine Learning: A Comparative Analysis with SMOTE-Based Class Balancing. Procedia Computer Science, 272, 242–250. https://doi.org/10.1016/j.procs.2025.10.202 Li, G., & Sheng, H. (2025). A hybrid machine learning framework for predicting aircraft scaled sound pressure levels: A comparative study. Journal of Engineering and Applied Science, 72(1), 163. https://doi.org/10.1186/s44147-025-00714-9 Ling, Q. (2023). Machine learning algorithms review. Applied and Computational Engineering, 4(1), 91–98. https://doi.org/10.54254/2755-2721/4/20230355 Liu, H. (2025). Machine Learning Approaches for Predicting and Intervening in Students’ Academic Status. Applied and Computational Engineering, 183(1), 102–109. https://doi.org/10.54254/2755-2721/2025.BJ27254 Lokanan, M. (2024). Exploring Resampling Techniques in Credit Card Default Prediction. In Review. https://doi.org/10.21203/rs.3.rs-4087259/v1 Lu, X. (2024). Modern Education: Advanced Prediction Techniques for Student Achievement Data. International Journal of Advanced Computer Science and Applications, 15(1). https://doi.org/10.14569/IJACSA.2024.01501126 Lünich, M., Keller, B., & Marcinkowski, F. (2024). Fairness of Academic Performance Prediction for the Distribution of Support Measures for Students: Differences in Perceived Fairness of Distributive Justice Norms. Technology, Knowledge and Learning, 29(2), 1079–1107. https://doi.org/10.1007/s10758-023-09698-y Mujahid, M., Kına, E., Rustam, F., Villar, M. G., Alvarado, E. S., De La Torre Diez, I., & Ashraf, I. (2024). Data oversampling and imbalanced datasets: An investigation of performance for machine learning and feature engineering. Journal of Big Data, 11(1), 87. https://doi.org/10.1186/s40537-024-00943-4 Nafea, A. A., Mishlish, M., Haban Shaban, A. M., AL-Ani, M. M., Alheeti, K. M. A., & Mohammed, H. J. (2023). Enhancing Student’sPerformance Classification Using Ensemble Modeling. Iraqi Journal for Computer Science and Mathematics, 4(4). https://doi.org/10.52866/ijcsm.2023.04.04.016 Nguyen, C., Thi Tran, T., Thanh Thi Nguyen, T., Ha Thi Nguyen, T., Astarkhanova, T. S., Van Vu, L., Tai Dau, K., Nguyen, H. N., Pham, G. H., Nguyen, D. D., Prakash, I., & Pham, B. (2024). Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam. Journal of Hydroinformatics, 26(1), 72–87. https://doi.org/10.2166/hydro.2023.327 Osborne, J. B., & Lang, A. S. I. D. (2023). Predictive Identification of At-Risk Students: Using Learning Management System Data. Journal of Postsecondary Student Success, 2(4), 108–126. https://doi.org/10.33009/fsop_jpss132082 Ozyurt, O., Ozyurt, H., & Mishra, D. (2023). Uncovering the Educational Data Mining Landscape and Future Perspective: A Comprehensive Analysis. IEEE Access, 11, 120192–120208. https://doi.org/10.1109/ACCESS.2023.3327624 Pan, J., Zhao, Z., & Han, D. (2025). Academic Performance Prediction Using Machine Learning Approaches: A Survey. IEEE Transactions on Learning Technologies, 18, 351–368. https://doi.org/10.1109/TLT.2025.3554174 Paul, C., & Devi, A. (2025). AI-Driven Early Warning Systems for Dropout Risk and Passed-Out Prediction in Higher Education. In Dr. Mohd. A. Gandhi, Dr. A. Dutt, Dr. V. Saravanan, & K. T. Umare (Eds.), AI, Machine Learning, and IoT Applications for Academic Performance Prediction, Faculty Well-Being, and Educational Outcomes in Higher Education (2025th ed., pp. 89–110). RADemics Research Institute. https://doi.org/10.71443/9789349552258-04 Rahmi, N. A., Defit, S., & Okfalisa. (2024). Enhancing Classification Performance: A Study on SMOTE and Ensemble Learning Techniques. 2024 International Conference on Future Technologies for Smart Society (ICFTSS), 63–68. https://doi.org/10.1109/ICFTSS61109.2024.10691339 Rajasekhar, C., & Fernandes, A. S. H. D. (2026). Student Performance Prediction Using Machine Learning and Deep Learning. In R. Shanthi, Artificial Intelligence–Driven Intelligent Learning Systems for Language Proficiency, Student Success, and Well-Being in Higher Education (2026th ed., pp. 275–297). RADemics Research Institute. https://doi.org/10.71443/9789349552401-12 Rakitin, I. V., Rakitina-Qureshi, E. V., Arabadzhieva-Kalcheva, N. A., Todorova, M. P., Penev, I. P., & Petrova, D. I. (2025). Predicting Student Placement Status Using Tree-Based Ensemble Classifiers: A Comparative Analysis of Random Forest and Gradient Boosting Models. 2025 International Conference Automatics and Informatics (ICAI), 53–58. https://doi.org/10.1109/ICAI67591.2025.11325082 Rawat, B., & Pant, H. (2025). Boosting vs. Traditional Algorithms for Student Performance Prediction. 2025 International Conference on Sustainable Communication Networks and Application (ICSCN), 1998–2003. https://doi.org/10.1109/ICSCN67106.2025.11308228 Rico-Juan, J. R., Cachero, C., & Macià, H. (2024). Study regarding the influence of a student’s personality and an LMS usage profile on learning performance using machine learning techniques. Applied Intelligence, 54(8), 6175–6197. https://doi.org/10.1007/s10489-024-05483-1 Rochmadi, T. (2025). Addressing Class Imbalance in Predicting Student Academic Outcomes: A Comparative Study of Resampling Techniques with Machine Learning Classifiers in Higher Education. Artificial Intelligence in Learning, 1(3), 211–227. https://doi.org/10.63913/ail.v1i3.32 Sathvik & Nagaveni Biradar. (2025). Student Performance Detection Using Machine Learning Techniques. International Research Journal on Advanced Engineering Hub (IRJAEH), 3(09), 3513–3516. https://doi.org/10.47392/IRJAEH.2025.0516 Sharanya, B. (2024). An Efficient Ensemble Machine Learning Model for Cardiovascular Disease Prediction Using Digital Health Records. International Journal for Research in Applied Science and Engineering Technology, 12(3), 2598–2609. https://doi.org/10.22214/ijraset.2024.58888 Sharma, Y. P., & Sasikala, K. (2026). Early Warning Systems for Student Dropout Risk Prediction. In R. Shanthi, Artificial Intelligence–Driven Intelligent Learning Systems for Language Proficiency, Student Success, and Well-Being in Higher Education (2026th ed., pp. 298–321). RADemics Research Institute. https://doi.org/10.71443/9789349552401-13 Sujon, K. M., Hassan, R., Khairudin, A. R., Moi, S. H., Mohd Shafie, M. L., Saringat, Z., & Erianda, A. (2024). The Effects of Imbalanced Datasets on Machine Learning Algorithms in Predicting Student Performance. JOIV : International Journal on Informatics Visualization, 8(3–2), 1599. https://doi.org/10.62527/joiv.8.3-2.2449 Teresa, & et. al. (2025). Predictive Modelling of Student Outcomes Using Ensemble Regression and Classification Methods. https://doi.org/10.5281/ZENODO.17599070 Tiwari, M., & Jain, N. (2024). STUDENT PERFORMANCE PREDICTION USING MACHINE LEARNING ALGORITHMS. ShodhKosh: Journal of Visual and Performing Arts, 5(6). https://doi.org/10.29121/shodhkosh.v5.i6.2024.4552 Villar, A., & Andrade, C. R. V. (2024). Supervised machine learning algorithms for predicting student dropout and academic success: A comparative study. Discover Artificial Intelligence, 4(1), 2. https://doi.org/10.1007/s44163-023-00079-z Yu, L., Zhao, X., Huang, J., Hu, H., & Liu, B. (2023). Research on Machine Learning with Algorithms and Development. Journal of Theory and Practice of Engineering Science, 3(12), 7–14. https://doi.org/10.53469/jtpes.2023.03(12).02 Yuningsih, L., Pradipta, G. A., Hermawan, D., Ayu, P. D. W., Hostiadi, D. P., & Huizen, R. R. (2023). IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification. Emerging Science Journal, 7(5), 1501–1516. https://doi.org/10.28991/ESJ-2023-07-05-04

Modeling and Performance Analysis of a Series-Parallel AC Circuit under Supply Voltage Variations Using Simulation Eka Feby Ronauli Lubis; Nurhafiz Ahmad Rangkuti; Nirwan Sinuhaji; Indah Mawati Giawa
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 3 (2026): JCEIT: Journal of Computer Engineering and Information Technology (July 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i3.65

Series-parallel Alternating Current (AC) circuits are widely used in electrical power distribution and electronic systems because their operating characteristics are strongly influenced by supply voltage variations. Although simulation-based circuit analysis using Multisim has been widely applied in engineering education, studies systematically investigating the operating characteristics of series-parallel AC circuits over a broad range of supply voltages remain limited. This study aims to analyze the effect of gradual supply voltage variation on the operating characteristics of a series-parallel AC circuit using Multisim simulation. A quantitative experimental approach based on computer simulation was employed. The simulated circuit consisted of two resistors (220 Ω and 230 Ω) and two lamps connected as electrical loads. The supply voltage was gradually varied from 100 V to 320 V to observe changes in the operating conditions of the loads. The simulation showed that increasing the supply voltage progressively changed the lamp operating conditions, ranging from no illumination at 100 V, low brightness at 130 V, gradual increases in brightness at intermediate voltage levels, maximum brightness at 300 V, and lamp failure at 320 V due to excessive applied voltage. These observations are consistent with the theoretical relationships described by Ohm's Law, where increasing supply voltage results in greater electrical power delivered to resistive loads. The findings suggest that Multisim provides a practical environment for the preliminary evaluation of circuit behavior under different supply voltage conditions. However, the conclusions of this study are limited to simulation-based observations and require further validation through quantitative electrical measurements and hardware experiments. REFERENCES Anindya et al. (2025). Analisis Arus Dan Tegangan Pada Rangkaian Seri Dan Paralel Berdasarkan Hukum Ohm. Phydagogi : Jurnal Fisika Dan Pembelajarannya, 7(2), 1–8. Bhowmic, A. (2024). Verification of Electrical Circuit Simulation Results Using Multisim. Dewi Tiyas Saputri, Ayu Widiana Putri, & Aisyiyah Marfa Berliana Buanasari. (2025). Pengaruh Tegangan Terhadap Besar Kuat Arus Listrik Pada Pengukuran Hukum OHM Berbasis Simulasi Phet HTML5. Jurnal Teknik Mesin, Industri, Elektro Dan Informatika, 4(1), 321–331. https://doi.org/10.55606/jtmei.v4i1.4843 Dewy, M. S., & Isnaini, M. (2022). Pengembangan Modul Pembelajaran Berbasis Software Simulasi Ni Multisim Pada Mata Kuliah Rangkaian Listrik Dc. 9(1). Fan, C., Yang, M., Zhao, Y., Geng, X., & An, J. (2026). Research and Practice of a Virtual Simulation Circuit Experiment Platform based on LabVIEW and Multisim. In Academic Journal of Science and Technology (Vol. 19, Number 1). Halim, A., Alinda, M., Mahzum, E., & Wahyuni, A. (2024). Impact of Using PhET and NI Multisim Simulation on Understanding Electrical Circuit Concepts. https://doi.org/10.21009/1 Hilma, & Malik, A. (2024). Analisis Pengaruh Tegangan dan Hambatan Terhadap Kuat Arus dengan Menggunkan Phet Simulation. JFT: Jurnal Fisika Dan Terapannya, 10(2), 76–85. https://doi.org/10.24252/jft.v10i2.39275 Mabruroh, F., Talakua, P., Suhendi, H. Y., & Maipauw, M. M. (2025). The Utilization of Virtual Reality in Ohm’s Law Experiment Simulation. Jurnal Pendidikan Dan Ilmu Fisika, 5(1), 25–39. https://doi.org/10.52434/jpif.v5i1.42510 Malik, A., Asyidik, D., Nursamsika, K. H., Khotimah, R. N., & Fitriyani, R. (2020). Learning Ohm’s Law through Electric Puzzle Media. https://doi.org/10.21009/1 Nasab, M. R., Cometa, R., Bruno, S., Giannoccaro, G., & Scala, M. La. (2024). Power Systems Simulation and Analysis: A Review on Current Applications and Future Trends in DRTS of Grid-Connected Technologies. In IEEE Access (Vol. 12, pp. 121320–121345). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2024.3452468 Nuryanto. (2017). Analisa Rangkaian Elektronika Dengan Menggunakan Program Simulasi Spice. http://www.orcad.com/pspicead.aspx. Putri et al. (2025). Analisis Hubungan Tegangan dan Arus dalam Rangkaian Seri dan Paralel Berdasarkan Hukum Ohm. Jurnal Fisika Dan Pembelajarannya, 7(2), 201–206. Rangkuti, N. A. (2026). Analisis Topologi Rangkaian Seri-Paralel Berbasis Simulasi Multisim Untuk Evaluasi Distribusi Arus Dan Tegangan. Hadron Jurnal Fisikadan Terapan, 8, 1–6. Ridwan, R., & Kembuan, D. R. E. (2021). Efektivitas Penggunaan Simulasi dengan Multisim Berbantuan Virtual Laboratory untuk Meningkatkan Kemampuan Berpikir Kritis Mahasiswa Jurusan Pendidikan Teknik Elektro. Jurnal Kiprah, 9(1), 39–47. https://doi.org/10.31629/kiprah.v9i1.3235 Rupawanti, N. (2018). J E : Electronic Control, Telecomunication, Computer Information and Power Systems Studi Karakteristik Transformator Daya Listrik dengan Multisim 12.0. 3, 5–8. Saha, A. K. (2022). A Real-Time Simulation-Based Practical on Overcurrent Protection for Undergraduate Electrical Engineering Students. IEEE Access, 10, 52537–52550. https://doi.org/10.1109/ACCESS.2022.3175813 Sidiq & Sukandar. (2023). Analisis Perbandingan Karakteristik Arus Dan Tegangan Pada Rangkaian Listrik Seri Dan Paralel. 1–4. Surahmat, A., & Dedy Fu’ady, T. (2020). Simulasi Rangkaian Seven Segment Menggunakan Multisim Pada Pembelajaran Rangkaian Elektronika Analog Dan Digital Di Smks Informatika Sukma Mandiri. Sya’ban, A. N., Faiza, D., Thamrin, & Jasril, I. R. (2024). “METRIKA”: Electronics Trainer Media for Supporting Learning in the Application of Ohm’s Law and Kirchhoff’s Law. Journal of Hypermedia & Technology-Enhanced Learning, 2(1), 63–79. https://doi.org/10.58536/j-hytel.v2i1.112 Zhang, M., & Pan, S. (2023). Flipped Classroom of Electrical and Electronic Technology based on Multisim Simulation. In Academic Journal of Science and Technology (Vol. 5, Number 1).

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Roberto Kaban

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itgeek.id@gmail.com

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Karya Techno Solusindo Berkala ASRI Blok R No. 10, Jalan Kapiten Purba IIDesa /Kelurahan Mangga, Kecamatan Medan Tuntungan, Medan, Sumatera Utara 20141

Location
Kota medan,

Sumatera utara

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Roberto Kaban

Contact Email itgeek.id@gmail.com

Phone +6281260329842

Journal Mail Official jceit@jurnal.ktsi.my.id

Editorial Address Karya Techno Solusindo Berkala ASRI Blok R No. 10, Jalan Kapiten Purba IIDesa /Kelurahan Mangga, Kecamatan Medan Tuntungan, Medan, Sumatera Utara 20141

Location Kota medan, Sumatera utara INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Roberto Kaban

Contact Email
itgeek.id@gmail.com

Phone
+6281260329842

Journal Mail Official
jceit@jurnal.ktsi.my.id

Editorial Address
Karya Techno Solusindo Berkala ASRI Blok R No. 10, Jalan Kapiten Purba IIDesa /Kelurahan Mangga, Kecamatan Medan Tuntungan, Medan, Sumatera Utara 20141

Location
Kota medan,

Sumatera utara

INDONESIA