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Journal Of Computer Engineering And Information Technology
Published by Karya Techno Solusindo
ISSN : -     EISSN : 3089106X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mechanical Engineering
Journal of Computer Engineering and Information Technology (JCEIT) published by karya Techno Solusindo which has been published since 2024. The aim of this journal is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of computer science. Journal of Computer Engineering and Information Technology (JCEIT) is consistently published two times a year in July and January. This journal covers original article in computer science that has not been published. The article can be research papers, research findings, review articles, analysis and recent applications in computer science. The scope of Journal of Computer Engineering and Information Technology (JCEIT) covers, but is not limited to the following areas: 1. Software engineering 2. Information System 3. Data Mining 4. Image Processing 5. Digital Forensics 6. Artificial Intelegence 7. Decision Support System
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 34 Documents
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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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

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