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All Journal Jurnal TIMES Scientific Journal of Informatics JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JUKI : Jurnal Komputer dan Informatika Jurnal Dinamika Informatika (JDI) Jurnal Informatika dan Perancangan Sistem (JIPS) LOFIAN: Jurnal Teknologi Informasi dan Komunikasi LOGIC : Jurnal Ilmu Komputer dan Pendidikan Jurnal Ilmiah Research Student Blantika : Multidisciplinary Journal Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Journal Of Computer Engineering And Information Technology System Information and Computer Technology (SYNCTECH) Jurnal Ilmu Komputer dan Teknik Informatika
Roberto Kaban
Institut Teknologi dan Bisnis Indonesia
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Other

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 1 
Naive Bayes Classification for Construction Workforce Requirement Prediction Nuzaima Agustari; Roberto kaban; Safarul Ilham
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.v2i2.47

Abstract

The determination of construction worker requirements at the Langkat Regency Public Works and Spatial Planning Office (PUPR) is still conducted manually, often resulting in inaccurate workforce allocation and inefficiencies in project implementation. Therefore, a data-driven approach is needed to improve prediction accuracy. This study aims to apply the Naive Bayes algorithm to predict construction worker needs based on historical project data. This research uses a quantitative approach with a classification method. The dataset includes variables such as project type, budget, duration, and previous labor usage. Data processing involves preprocessing, training, and testing, with a data split of 80% for training and 20% for testing. The Naive Bayes model is then evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the Naive Bayes algorithm achieves an accuracy of 86.7%, precision of 84.5%, recall of 87.2%, and F1-score of 85.8%. The model is capable of classifying workforce needs into low, medium, and high categories effectively. In conclusion, the Naive Bayes algorithm provides a reliable and efficient method for predicting construction worker requirements, supporting better decision-making and workforce planning at the Langkat Regency PUPR Office. REFERENCES Agustina, F. D., Arif, M., & Ahmad, S. (2025). Systematic Literature Review atas Kinerja Algoritma KNN, Naïve Bayes, dan Decision Tree pada Berbagai Studi Prediksi dan Klasifikasi. Jurnal Jawara Sistem Informasi, 3(1). Andika, A., Syarli, S., & Sari, C. R. (2022). Data Mining Klasifikasi Kelulusan Mahasiswa Menggunakan Metode Naïve Bayes. 4(1), 423–428. Anggreni, A., Hakim, A., Rahman, A., & Dinata, M. I. (2026). Klasifikasi Partisipasi Pemilih pada Pemilihan Walikota Bima Tahun 2024 Menggunakan Metode Naive Bayes Classifier. Jurnal Sains Natural, 4(1), 1–13. Azhar, S., Informatika, J. T., & Teknik, F. (2019). Decision Tree Dalam Memprediksi Kelulusan. (September), 1–8. Azizah, S. N. (2025). Kecerdasan Buatan dalam Pengelolaan SDM: Tantangan dan Peluang. Penerbit NEM. Fatimah, S., Adys, A. K., & Rahim, S. (2021). Strategi Dinas Pekerjaan Umum dan Penataan Ruang Dalam Perbaikan Infrastruktur Jalan di Kabupaten Bone. KIMAP: Kajian Ilmiah Mahasiswa Administrasi Publik, 2(4), 1412–1426. Fitria, R. (2025). Perancangan Sistem Informasi Akademik Berbasis Website Di Sdn 21 Tulang Bawang Udik Kabupaten Tulang Bawang Barat. Hadi, R., Pivin, N. L. G., Kusuma, I. G. N. A., Saryanti, I. G. A. D., & Novayanti, P. D. (2025). Analisis Perbandingan Algoritma K-Nearest Neighbor (Knn) Dan Support Vector Machine (Svm) Dalam Klasifikasi Data Perbankan. Jurnal Informasi Dan Komputer, 13(01), 167–173. Jariah, A., & Mufarrohah, H. (2025). Penerapan Probabilitas Bayes Dalam Pengambilan Keputusan Akademik Siswa. AL-BAHTS: Jurnal Ilmu Sosial, Politik, Dan Hukum, 2(3), 28–40. Kamuri, K. J., Manongga, I. R., Anabuni, A. U., Benu, Y. S. I. P., & Siahaan, M. Y. (2025). Manajemen Sumber Daya Manusia (MSDM) Era Digital. Penerbit Buku Indonesia (PBI). Macfud, A. Z., Kusuma, A. P., & Puspitasari, W. D. (2023). Analisis Algoritma Naive Bayes Classifier ( Nbc ). 7(1), 87–94. Mahbubi, M. F. (2025). Efektivitas Tugas Kelurahan Di Bidang Pemberdayaan Masyarakat Kelurahan Pekan Kuala Kecamatan Kuala Kabupaten Langkat. Mamuriyah, N., Haeruddin, H., & Hero, H. (2024). Pembangunan Chatbot Interaktif Dengan Menggunakan Algoritma Naive Bayes. Informatika: Jurnal Teknik Informatika Dan Multimedia, 4(2), 82–94. Mardizal, J. (2025). Manajemen Proyek Konstruksi: Strategi Dan Teknik Untuk Sukses. Rajawali Pers. Masgode, M. B., Hidayat, A., Laksmi, I. A. C. V., Triatmika, I. N. A., Puspayana, I. P. A. I., Iskandar, A. A., Syarif, M., Rachman, R. M., Herlambang, A. R., & Dirgantara, A. (2024). Dinamika Industri Konstruksi di Indonesia. Tohar Media. Mita Febri Anika. (2022). Peran Bidang Program Pada Dinas Pupr Kabupaten Aceh Barat Dalam Pembangunan Jalan. Jurnal Ilmiah Teknik Unida, 3(1), 37–41. https://doi.org/10.55616/jitu.v3i1.205 Ningsih, W., & Abdullah, F. (2021). Analisis Perbedaan Pencari Kerja dan Lowongan Kerja Sebelum dan Pada Saat Pandemi Covid-19 di Kota Malang. Journal of Regional Economics Indonesia, 2(1), 42–56. https://doi.org/10.26905/jrei.v2i1.6181 Pangestu, A., Geni, B. Y., & Dinanti, P. A. (2025). Perancangan Sistem Prediksi Kesempatan Kerja Mahasiswa Berdasarkan Profil Akademik dan Pengalaman Kerja. JATI (Jurnal Mahasiswa Teknik Informatika), 9(6), 9731–9738. Prastyo, E. H. A., Suhartono, S., Faisal, M., Yaqin, M. A., & Firdaus, R. A. J. (2024). Naive Bayes Classification Untuk Prediksi Cacat Perangkat Lunak. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 9(2), 782–791. Rachman, A., Arbi, R., Giola, Y., Zubeidi, S., & Araujo, A. L. (2024). Perencanaan sumber daya manusia. Tohar Media. Rismayadi, D. A., SI, S., Kom, M., Faira, F., Adjani, K., Kom, S., & Kom, M. (2025). Algoritma dan Data Driven Decision Making dalam Bisnis. Alungcipta. Sembiring, T. B., & SH, M. (2022). Pengelolaan Daerah Aliran Sungai: Studi Di Kawasan Das Kabupaten Langkat. Penerbit Adab. Shelvira, H. P. (2025). Perbandingan Model Na¨ Ive Bayes Dan Random Forest Dalam Prediksi Klasifikasi Masa Studi Sarjana Matematika Universitas Lampung. Shoheh, M. (2025). Pengantar Ilmu Futurologi. Penerbit A-Empat. Sihite, H. M. (2024). Implementasi Metode Naïve Bayes Terhadap Minat Masyarakat Rantau Utara Memilih Pakai Kartu Sim Telkomsel.
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.
Mapping Research Trends of Query Expansion in Information Retrieval: A Bibliometric Analysis Roberto Kaban
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.v2i2.57

Abstract

This study aims to analyze the development of research on query expansion in the field of information retrieval using a bibliometric approach to understand research trends, distribution, and current research focus. The data were obtained from 676 publications indexed in Scopus during the period from 2020 to February 2026. The research method involves quantitative analysis of annual publication trends, distribution of subject areas, document types, and keyword analysis using VOSviewer to map keyword relationships through co-occurrence analysis, overlay visualization to identify keyword trends, and density visualization to observe the concentration of research topics. The results show fluctuations in the number of publications with a peak occurring in 2025 with 141 publications. The research is dominated by the Computer Science field with 596 publications, and the majority of documents are conference papers with 369 publications. Keyword analysis identifies core topics such as information retrieval with 483 occurrences, query expansion with 354 occurrences, and search engines with 221 occurrences. Recent research trends include large language models, word embedding, and retrieval-augmented generation. The keyword network visualization indicates a shift from traditional methods such as relevance feedback toward modern approaches based on artificial intelligence and machine learning, which are increasingly relevant for improving the effectiveness of information retrieval systems. These findings provide both quantitative and qualitative insights into the evolution of query expansion research. The results also highlight the integration of modern technologies in retrieval practices and provide a foundation for new researchers to identify trends, research gaps, and opportunities for future innovation. REFERENCES Ahmed, M. (2024). Bibliometrix: An Easy Yet Powerful Approach for Quantitative and Qualitative Analyses of Scholarly Literature. Information Research Communications, 1(1), 43–45. https://doi.org/10.5530/irc.1.1.7 Al-Lahham, Y. (2024). Improved Arabic Query Expansion using Word Embedding. https://doi.org/10.21203/rs.3.rs-4065010/v1 Allahim, A., Cherif, A., & Imine, A. (2025). Semantic approaches for query expansion: Taxonomy, challenges, and future research directions. PeerJ Computer Science, 11, e2664. https://doi.org/10.7717/peerj-cs.2664 Baumann, O., & Schoenfeld, M. (2024). PSQE: Personalized Semantic Query Expansion for user-centric query disambiguation. https://doi.org/10.21203/rs.3.rs-4178030/v1 Bernard, N., & Balog, K. (2025). A Systematic Review of Fairness, Accountability, Transparency, and Ethics in Information Retrieval. ACM Computing Surveys, 57(6), 1–29. https://doi.org/10.1145/3637211 Breuer, T., Frihat, S., Fuhr, N., Lewandowski, D., Schaer, P., & Schenkel, R. (2025). Large Language Models for Information Retrieval: Challenges and Chances. Datenbank-Spektrum, 25(2), 71–81. https://doi.org/10.1007/s13222-025-00503-x Ganti, L., Persaud, N. A., & Stead, T. S. (2025). Bibliometric analysis methods for the medical literature. Academic Medicine & Surgery. https://doi.org/10.62186/001c.129134 Hambarde, K. A., & Proença, H. (2023). Information Retrieval: Recent Advances and Beyond. IEEE Access, 11, 76581–76604. https://doi.org/10.1109/ACCESS.2023.3295776 Hidri, M. (2024). Learning-Based Models for Building User Profiles for Personalized Information Access. Interdisciplinary Journal of Information, Knowledge, and Management, 19, 010. https://doi.org/10.28945/5275 Kaban, R., Sihombing, P., Efendi, S., & Lydia, M. S. (2025a). Enhancing Retrieval Performance in Social Media Using Corpus-Based Query Expansion. 2025 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), 1–6. https://doi.org/10.1109/AIMS66189.2025.11229497 Kaban, R., Sihombing, P., Efendi, S., & Lydia, M. S. (2025b). Enhancing retrieval performance in social media with corpus-based query expansion using bidirectional encoder representations from transformers. Eastern-European Journal of Enterprise Technologies, 5(2 (137)), 70–83. https://doi.org/10.15587/1729-4061.2025.340258 Kumar, R. (2025). Bibliometric Analysis: Comprehensive Insights into Tools, Techniques, Applications, and Solutions for Research Excellence. Spectrum of Engineering and Management Sciences, 3(1), 45–62. https://doi.org/10.31181/sems31202535k Meliukh, V., Potapova, E., Nalyvaichuk, M., & Dychka, A. (2025). Query expansion based on context-dependent sentiment analysis in databases with domain-specific filtering. Eastern-European Journal of Enterprise Technologies, 1(2 (133)), 6–17. https://doi.org/10.15587/1729-4061.2025.322120 Naamha, E. Q., & Abdulmunim, M. E. (2024). Web Page Ranking Based on Text Content and Link Information Using Data Mining Techniques. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 12(1), 29–40. https://doi.org/10.14500/aro.11397 Pan, M., Liu, Y., Chen, J., Huang, E. A., & Huang, J. X. (2024). A multi-dimensional semantic pseudo-relevance feedback framework for information retrieval. Scientific Reports, 14(1), 31806. https://doi.org/10.1038/s41598-024-82871-0 Pan, M., Xiong, W., Zhou, S., Gao, M., & Chen, J. (2025). LLM-Based Query Expansion with Gaussian Kernel Semantic Enhancement for Dense Retrieval. Electronics, 14(9), 1744. https://doi.org/10.3390/electronics14091744 Patel, V., Hiran, D., & Dangarwala, K. (2024). Recent Trends of Information Retrieval System: Review Based on IR Models and Applications. In V. K. Gunjan & J. M. Zurada (Eds.), Proceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications (Vol. 873, pp. 619–629). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-9442-7_51 Peikos, G., & Pasi, G. (2024). A systematic review of multidimensional relevance estimation in information retrieval. WIREs Data Mining and Knowledge Discovery, 14(5), e1541. https://doi.org/10.1002/widm.1541 Raj, G. D., Mukherjee, S., Robin, C. R. R., & Jasmine, R. L. (2025). An Intelligent Feature Concatenation Process-Based Effective Query Expansion for Patent Retrieval Approach Using Optimal Bi-clustering and Enhanced Social Engineering Optimizer. International Journal of Computational Intelligence Systems, 18(1), 259. https://doi.org/10.1007/s44196-025-00963-9 Roberts, K. (2024). Information Retrieval. In H. Xu & D. Demner Fushman (Eds.), Natural Language Processing in Biomedicine (pp. 195–230). Springer International Publishing. https://doi.org/10.1007/978-3-031-55865-8_8 Stathopoulos, E. A., Karageorgiadis, A. I., Kokkalas, A., Diplaris, S., Vrochidis, S., & Kompatsiaris, I. (2023). A Query Expansion Benchmark on Social Media Information Retrieval: Which Methodology Performs Best and Aligns with Semantics? Computers, 12(6), 119. https://doi.org/10.3390/computers12060119 Venkatachalam, C., & Venkatachalam, S. (2023). Optimal Intelligent Information Retrieval and Reliable Storage Scheme for Cloud Environment And E-Learning Big Data Analytics. In Review. https://doi.org/10.21203/rs.3.rs-2545685/v1 Vishwakarma, D., & Kumar, S. (2025). Fine-Tuned BERT Algorithm-Based Automatic Query Expansion for Enhancing Document Retrieval System. Cognitive Computation, 17(1), 23. https://doi.org/10.1007/s12559-024-10354-5 Vladlenov, D. (2023). MODERN METHODS OF APPLYING SCIENTIFIC THEORIES. Proceedings of the X International Scientific and Practical Conference, 1–481. https://doi.org/10.46299/ISG.P.2023.1.10 Wang, L., Yang, N., & Wei, F. (2023). Query2doc: Query Expansion with Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 9414–9423. https://doi.org/10.18653/v1/2023.emnlp-main.585 Wang, Z., & Pei, Q. (2024). Dense Retrieval Systems with LLM-Based Query Expansion. 2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 682–686. https://doi.org/10.1109/WI-IAT62293.2024.00110 Ye, F., Fang, M., Li, S., & Yilmaz, E. (2023). Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting. Findings of the Association for Computational Linguistics: EMNLP 2023, 5985–6006. https://doi.org/10.18653/v1/2023.findings-emnlp.398 Yıldız, M., & Karakuş, T. (2024). Bibliometric Analysis in Scientific Research Using R: A Review of Scopus and Web of Science Databases. Journal of Data Applications, 0(2), 31–46. https://doi.org/10.26650/JODA.1462396 Zahhar, S., Mellouli, N., & Rodrigues, C. (2025). Leveraging Sentence-Transformers to Overcome Query-Document Vocabulary Mismatch in Information Retrieval. In M. Barhamgi, H. Wang, X. Wang, E. Aïmeur, M. Mrissa, B. Chikhaoui, K. Boukadi, R. Grati, & Z. Maamar (Eds.), Web Information Systems Engineering – WISE 2024 PhD Symposium, Demos and Workshops (Vol. 15463, pp. 101–110). Springer Nature Singapore. https://doi.org/10.1007/978-981-96-1483-7_8 Zhang, L., Wu, Y., Yang, Q., & Nie, J.-Y. (2024). Exploring the Best Practices of Query Expansion with Large Language Models (arXiv:2401.06311). arXiv. https://doi.org/10.48550/arXiv.2401.06311
Earthquake Detection and Tsunami Disaster Management Using Vibration Sensors Jihan Nadirah Simatupang; Fauziah; Fitri Ramadhani Pane; M. Irfan Affandi; Roberto Kaban; Surizar Rahmi Danur
JCEIT: Journal of Computer Engineering and Information Technology Vol. 1 No. 3: JCEIT: Journal of Computer Engineering and Information Technology (July 2025)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v1i3.16

Abstract

An earthquake is a vibration or tremor that occurs on the Earth's surface due to a sudden release of energy from within that creates seismic waves. The frequency of an area refers to the type and size of earthquakes experienced over a period of time. Along with the development of earthquake detection system technology provides a solution to minimize the impact of earthquake events. Natural disasters that often occur in the country of Indonesia, one of the natural disasters that often occur is earthquakes. And many people do not know when an earthquake will come.  So an earthquake detection tool was made with Arduino Uno which is a tool that can detect earthquake vibrations. With this tool using a vibration sensor sensor that can detect vibrations. REFERENCES Agustanti, Sri Primaini, Hartini Hartini, Nurhayani Nurhayani, and Dimas Dibya Hartanto. 2022. “Aplikasi Mikrokontroler Arduino Uno Dalam Rancang Bangun Kunci Pintu Menggunakan E-Ktp.” Jusikom : Jurnal Sistem Komputer Musirawas 7(1):74–88. doi: 10.32767/jusikom.v7i1.1611. Al-Ali, A. R., Beheiry, S., Alnabulsi, A., Obaid, S., Mansoor, N., Odeh, N., & Mostafa, A. (2024). An IoT-Based Road Bridge Health Monitoring and Warning System. Sensors, 24(2), 469. https://doi.org/10.3390/s24020469 Ayuningtyas, D., Windiarti, S., Hadi, M. S., Fasrini, U. U., & Barinda, S. (2021). Disaster Preparedness and Mitigation in Indonesia: A Narrative Review. Iranian Journal of Public Health. https://doi.org/10.18502/ijph.v50i8.6799 Basid, A., Mahardika, I. K., Subchan, W., & Astutik, S. (2021a). 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Implementation Of Machine Learning For Web-Based Stroke Probability Prediction Zuhaira Agustari; Roberto Kaban; Safarul Ilham
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 1 (2025): JCEIT: Journal of Computer Engineering and Information Technology (Nov 2025)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i1.36

Abstract

In an effort to enhance early detection and prevention of stroke, the implementation of web-based machine learning provides a promising solution. This study focuses on applying machine learning algorithms to predict the likelihood of stroke occurrence based on patient medical data collected online. By using the developed prediction model, the system efficiently analyzes historical data and health risk factors to provide stroke risk estimates. This implementation aims to improve diagnostic accuracy, enable better early detection, and offer appropriate preventive recommendations. The results of this study are expected to assist healthcare professionals and patients in stroke prevention efforts through the utilization of web-based technology. REFERENCES Akmaluddin, M., & Dewayanto, T. (2023). Systematic Literature Review: Implementasi Artificial Intelligence dan Machine Learning pada bidang akuntansi manajemen. Diponegoro Journal of Accounting, 12(4), 1–11. http://ejournal-s1.undip.ac.id/index.php/accounting Byna, A., & Basit, M. (2020). Penerapan Metode Adaboost untuk Mengoptimasi Prediksi Penyakit Stroke dengan Algoritma Naïve Bayes. 09(November), 407–411. Cahyono, D. S., Nugrahanti, F., & Hendrawan, A. T. (2019). Aplikasi pemasaran berbasis website pada percetakan Morodadi Komputer Magetan. Prosiding Seminar Nasional Teknologi Informasi dan Komunikasi (SENATIK), 2(1), 129–134. Fahrizal, Reynaldi, F. O., & Hikmah, N. (2020). Implementasi machine learning pada sistem pets identification menggunakan Python berbasis Ubuntu. JISICOM (Journal of Information System, Informatics and Computing), 4(1), 86–91. Hasibuan, E., Informasi, S., Ilmu, F., Informasi, T., Gunadarma, U., Margonda, J., No, R., Cina, P., & Jawa, D. (2022). Implementasi machine learning untuk prediksi harga mobil bekas dengan algoritma regresi linear berbasis web. Jurnal Ilmiah Komputasi, 21(4), 595–602. https://doi.org/10.32409/jikstik.21.4.3327 Igfirly Mustaib, R., Dwiyansaputra, R., Muaidi, M., Desa Sandik Jl Pariwisata, K., & Layar, B. (n.d.). Sistem informasi company profile Kantor Desa Sandik berbasis website (Website based information system of company profile for Sandik Village). Kusuma, A. S., & Nita, S. (2019). Rancang bangun media pembelajaran pengenalan tumbuhan bagi penyandang tuna rungu pada SDLB Manisrejo Kota Madiun. Seminar Nasional Teknologi Informasi dan Komunikasi 2019, 281–286. Metode, M., Di, R. A. D., & Ahmad, S. (2022). No Title, 11(1), 79–85. Prediksi, A., Stroke, D., & Pendekatan, D. (2022). Analisis prediksi deteksi stroke dengan pendekatan EDA dan perbandingan algoritma machine learning. 02, 355–367. Purwono, P., Dewi, P., Wibisono, S. K., Dewa, B. P., Informatika, P., Bangsa, U. H., Keperawatan, P., & Bangsa, U. H. (2022). Model prediksi otomatis jenis penyakit hipertensi dengan pemanfaatan algoritma machine learning Artificial Neural Network. 7(2), 82–90. Putra, A. I., & Santika, R. R. (2020). Implementasi machine learning dalam penentuan rekomendasi musik dengan metode Content-Based Filtering. Edumatic: Jurnal Pendidikan Informatika, 4(1), 121–130. https://doi.org/10.29408/edumatic.v4i1.2162 Stacyana Jesika, S., Ramadhani, S., & Putri, Y. P. (2023). Implementasi model machine learning dalam mengklasifikasi kualitas air. Jurnal Ilmiah dan Karya Mahasiswa, 1(6), 382–396. https://doi.org/10.54066/jikma.v1i6.1162 Ula, M., Ulva, A. F., & Mauliza, M. (2021). Implementasi machine learning dengan model Case Based Reasoning dalam mendiagnosa gizi buruk pada anak. Jurnal Informatika Kaputama (JIK), 5(2), 333–339. https://doi.org/10.59697/jik.v5i2.267 Utama, T. P., & Haibuan, M. S. (2023). Penerapan algoritma Naïve Bayes dan Forward Selection untuk prediksi penyakit stroke. 17, 351–357.  
Co-Authors Ainun Aminah Alexander Silitonga Andara Cantika Effendy Angga Pradana Asprina Br. Surbakti Darwin Panjaitan David Jumpa Malem Sembiring Egi Mutiara Br Sitepu Eriansyah Saputra H Fahmil Triaga Fajrillah, Fajrillah Fandy Syahputra Fauziah Fitri Ramadhani Pane Humaira Humaira Ita Margaretta Tarigan Jihan Nadirah Simatupang Junaidy Laila Syafira M. Irfan Affandi Mahdianta Pandia Maulana Farras Meiliyani Br Ginting Muhammad Fauzan Muhammad Junaidi Muhammad Rizky Fajar Muhammad Yusra Nurdilla Nurdilla Nurhafiz Ahmad Rangkuti Nuzaima Agustari Poltak Sihombing Purwanto Simamora Rizky Adha Safarul Ilham Silvia Hanum Suata Wan Kelispa Halawa Surizar Rahmi Danur Tri Indah Prasasti Wan Saddam Hasan Yudi Abdul Halim Zahwa Aura Mutiara Hsb Zuhaira Agustari