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Earthquake Detection and Tsunami Disaster Management Using Vibration Sensors Simatupang, Jihan Nadirah; 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

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). Mapping risk levels of earthquake damage as disaster mitigation efforts: Case studies in West Java, Central Sulawesi and Lombok. 040007. https://doi.org/10.1063/5.0037540 Chen, H., Li, G., Fang, R., & Zheng, M. (2021). Early Warning Indicators of Landslides Based on Deep Displacements: Applications on Jinping Landslide and Wendong Landslide, China. Frontiers in Earth Science, 9, 747379. https://doi.org/10.3389/feart.2021.747379 Clements, T. (2023). Earthquake Detection with tinyML. Seismological Research Letters. https://doi.org/10.1785/0220220322 Dhira, Y., Meilano, I., & Dudy, D. W. (2021). Analysis of Tectonic Plate Velocity Variations in the Sunda Strait Based on GPS Time-series Data. IOP Conference Series: Earth and Environmental Science, 873(1), 012084. https://doi.org/10.1088/1755-1315/873/1/012084 Esposito, M., Palma, L., Belli, A., Sabbatini, L., & Pierleoni, P. (2022a). Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review. Sensors, 22(6), 2124. https://doi.org/10.3390/s22062124 Fajri, A. H., Murti, M. A., & Priramadhi, R. A. (2021). Design of earthquake early warning system based OMRON D7S vibrate sensor. IOP Conference Series: Materials Science and Engineering, 1098(4), 042099. https://doi.org/10.1088/1757-899X/1098/4/042099 Fuady, M., Munadi, R., & Fuady, M. A. K. (2021). Disaster mitigation in Indonesia: Between plans and reality. IOP Conference Series: Materials Science and Engineering, 1087(1), 012011. https://doi.org/10.1088/1757-899X/1087/1/012011 Gupta, T., & Roy, S. (2024). Applications of Artificial Intelligence in Disaster Management. Proceedings of the 2024 10th International Conference on Computing and Artificial Intelligence, 313–318. https://doi.org/10.1145/3669754.3669802 Harahap, Partaonan, Benny Oktrialdi, and Rahmad Fauzi Siregar. 2023. “Implementasi Alat Pendeteksi Gempa Berbasis Arduino Uno Dengan Memanfaatkan Sensor Getar (Vibration).” Seminar Nasional Teknik Elektro. Howard, S. K., Schrum, L., Voogt, J., & Sligte, H. (2021). Designing research to inform sustainability and scalability of digital technology innovations. Educational Technology Research and Development, 69(4), 2309–2329. https://doi.org/10.1007/s11423-020-09913-y Ira, A. P., & Purwantara, S. (2021). School Community Preparedness in Anticipation of Earthquake and Tsunami Threats in Temon Sub-District. IOP Conference Series: Earth and Environmental Science, 884(1), 012046. https://doi.org/10.1088/1755-1315/884/1/012046 Köhli, M., Weimar, J., Schmidt, S., Schmidt, F. P., Lambertz, A., Weber, L., Kaminski, J., & Schmidt, U. (2024). Arduino-Based Readout Electronics for Nuclear and Particle Physics. Sensors, 24(9), 2935. https://doi.org/10.3390/s24092935 Komarizadehasl, S., Mobaraki, B., Ma, H., Lozano-Galant, J.-A., & Turmo, J. (2021). Development of a Low-Cost System for the Accurate Measurement of Structural Vibrations. Sensors, 21(18), 6191. https://doi.org/10.3390/s21186191 Laia, Firdaus, Tobias Duha, Mitranikasih Laia, Amirudin Khorul Huda, and Agung Jasuma. 2023. “Klasifikasi Data Gempa Bumi Di Pulau Sumatera Menggunakan Algoritma Naïve Bayes.” Jurnal Informatika 2(1):23–27. doi: 10.57094/ji.v2i1.840. Liu, T., Zhang, M., Li, Z., Dou, H., Zhang, W., Yang, J., Wu, P., Li, D., & Mu, X. (2025). Machine learning-assisted wearable sensing systems for speech recognition and interaction. Nature Communications, 16(1), 2363. https://doi.org/10.1038/s41467-025-57629-5 Mamahit, Calvin. 2024. “Rumah Pintar Dengan Lampu Kontrol Suara Menggunakan Arduino Uno R3.” Electrician : Jurnal Rekayasa Dan Teknologi Elektro 18(2):144–52. doi: 10.23960/elc.v18n2.2567. Mar’atuzzulfa, Salma, Rastri Prathivi, and Susanto. 2025. “Klasifikasi Gempa Bumi Berdasarkan Magnitudo Menggunakan Metode Logistic Regression.” KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen) 6(1):227–34. Martin, K., and D. Susandi. 2022. “Perancangan Dan Implementasi Sistem Irigasi Kabut Otomatis Tanaman Edelweis Menggunakan Mikrokontroler Arduino Uno.” Jurnal IKRA-ITH INFORMATIKA 6(103):57–66. McBride, S. K., Smith, H., Morgoch, M., Sumy, D., Jenkins, M., Peek, L., Bostrom, A., Baldwin, D., Reddy, E., De Groot, R., Becker, J., Johnston, D., & Wood, M. (2022). Evidence-based guidelines for protective actions and earthquake early warning systems. GEOPHYSICS, 87(1), WA77–WA102. https://doi.org/10.1190/geo2021-0222.1 Nagasa, M. M., & Johnson, P. L. D. (2025). Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network. Sensors, 25(8), 2348. https://doi.org/10.3390/s25082348 Patel, S. C., & Allen, R. M. (2022). The MyShake App: User Experience of Early Warning Delivery and Earthquake Shaking. Seismological Research Letters, 93(6), 3324–3336. https://doi.org/10.1785/0220220062 Ramdhan, M., Palgunadi, K. H., Mukti, M. M., Librian, V., Daniarsyad, G., Muttaqy, F., Hidayat, E., Syuhada, S., Hanif, M., Mursitantyo, A., Lühr, B.-G., Nugraha, A. D., Widiyantoro, S., Setyonegoro, W., & Febriani, F. (2025). Aftershock sequence of the Yogyakarta earthquake 2006 (Mw ~ 6.4), Indonesia, based on analysis of hypocenter relocation, static, and dynamic stress. Natural Hazards. https://doi.org/10.1007/s11069-025-07440-8 Romanssini, M., De Aguirre, P. C. C., Compassi-Severo, L., & Girardi, A. G. (2023). A Review on Vibration Monitoring Techniques for Predictive Maintenance of Rotating Machinery. Eng, 4(3), 1797–1817. https://doi.org/10.3390/eng4030102 Sekine, K., & Hayakawa, K. (2022). Development of Vibration Measurement System using a Microcontroller. EPI International Journal of Engineering, 5(2), 98–103. https://doi.org/10.25042/epi-ije.082022.04 Setiawan, B., Rizal, M., Yunita, H., Saidi, T., Hasan, M., & Zulkifli, Z. (2022). Validating a low-cost seismometer using a shaking table. E3S Web of Conferences, 340, 02009. https://doi.org/10.1051/e3sconf/202234002009 Silalahi, Andri, Deddy Hartama, Ika Okta Kirana, Indra Gunawan, and Sumarno Sumarno. 2022. “Rancang Bangun Alat Pendeteksi Kebocoran Pada Tabung Gas Menggunakan Arduino Berbasis Sms.” Jurnal Krisnadana 1(3):48–58. doi: 10.58982/krisnadana.v1i3.178. Simanjuntak, T., & Ririmasse, M. (2021). Archaeology of disaster in Indonesia: Where are we now? Berita Sedimentologi, 47(3), 17–21. https://doi.org/10.51835/bsed.2021.47.3.351 Sinaga, G. H. D., Loeqman, A., Siagian, R. C., & Sinaga, M. P. (2022). Analysis of Coulomb Stress Changes in Aceh Earthquake on Sibayak Volcano. Jurnal Pendidikan Fisika Dan Teknologi, 8(2), 217–227. https://doi.org/10.29303/jpft.v8i2.4409 Tansa, Salmawaty, Nur’aeni Latekeng, Raghel Yunginger, and Iskandar Z. Nasibu. 2024. “Monitoring Kualitas Air Sungai (Kekeruhan, Suhu, TDS,PH) Menggunakan Mikrokontroler Atmega328.” Jambura Journal of Electrical and Electronics Engineering 6(1):70–75. doi: 10.37905/jjeee.v6i1.23315. Tian, B., Liu, W., Mo, H., Li, W., Wang, Y., & Adhikari, B. R. (2023). Detecting the Unseen: Understanding the Mechanisms and Working Principles of Earthquake Sensors. Sensors, 23(11), 5335. https://doi.org/10.3390/s23115335 Witjaksana, Budi, and Andi Syaiful Amal. 2021. “Penerapan Sensor Pedeteksi Dini Gempa Bumi Pada Bangunan Konstruksi.” Seminar Keinsinyuran Program Studi Program Profesi Insinyur 1(1):26–32. doi: 10.22219/skpsppi.v1i0.4216. Zhukovsky, V., Rusu, I., Zavoloka, M., & Grynyova, I. (2021). The concept of an automatic system for preliminary entry of hazardous facility into emergency mode with an approaching earthquake. IOP Conference Series: Materials Science and Engineering, 1141(1), 012023. https://doi.org/10.1088/1757-899X/1141/1/012023

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

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.

Studi Literatur Information Retrieval System Semantik Untuk Pencarian Produk E-Commerce Maulana Farras; Silvia Hanum; Roberto Kaban
LOFIAN: Jurnal Teknologi Informasi dan Komunikasi Vol 5 No 2 (2026): Pebruari
Publisher : Universitas Mandiri Bina Prestasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58918/lofian.v5i2.291

This research aims to analyze the development of the Semantic Information Retrieval System (Semantic IRS) approach in e-commerce product search based on descriptions through a literature study of 15 scientific articles consisting of national and international publications. The results of the analysis show that 33% of articles use the semantic IR and dense retrieval approaches as the basis for semantic mapping between queries and product documents. The late interaction and multimodal semantic retrieval approaches were each applied in 27% of articles, indicating an increasing research focus on token-level semantic interaction modeling and the integration of textual and visual information. Additionally, 13% of articles utilized query expansion and semantic relation modeling as supporting methods to improve search relevance. In terms of methodology, 80% of article used a quantitative experimental approach with information retrieval system metric-based evaluation, and 67% of articles adopted neural models. Overall, these quantitative findings indicate that neural model-based Semantic IR, late interaction, and multimodal approaches are the dominant and most relevant directions for handling long and unstructured description-based product searches in modern e-commerce systems.

Strategi Pengembangan Dan Implementasi Sistem Pendaftaran Berbasis Web Meningkatkan Partisipasi Calon Atlet Riyan’s Taekwondo Club Fatimah Zahira Chaniago; Roberto Kaban; Dewi Yohana Br Ginting
JISTI: Jurnal Ilmu Komputer, Sistem Informasi dan Teknologi Informasi Vol. 1 No. 4 (2026): Januari 2026
Publisher : Institut Teknologi dan Bisnis Indonesia

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

The increase in prospective athletes' participation has become one of the main challenges in the modern sports world. To address this challenge, this study developed and implemented a web-based online registration information system aimed at simplifying the registration process and attracting more prospective athletes. This system is designed using an iterative and collaborative software development approach to ensure flexibility and responsiveness to user needs. The key features implemented in this system include online registration, automatic data verification, and real-time registration status notifications. The evaluation of this system showed a significant increase in the number of registrants, as well as improved user satisfaction due to better ease of use and accessibility. The results of this study provide a significant contribution to the technology development strategy in the sports field, with the potential for wider application.

Analisis Sentimen Publik Terhadap RUU KUHAP di Platform X Menggunakan Metode TF-IDF dan Naïve Bayes Junaidy; Muhammad Fauzan; Roberto Kaban
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.518

The rapid development of social media has established Platform X as one of the primary channels for the public to express opinions on public policy issues, including the Draft Criminal Procedure Code (RUU KUHAP). This study aims to analyze public sentiment toward the RUU KUHAP based on tweet data collected from Platform X. A total of 2,273 valid data points were obtained and utilized in this research. The selected data underwent several preprocessing stages, including case folding, cleansing, tokenizing, stopword removal, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, while the sentiment classification process employed the Multinomial Naive Bayes algorithm, with the dataset split into training and testing sets. Model performance was evaluated using a confusion matrix alongside precision, recall, and F1-score metrics. The results indicate that public sentiment toward the RUU KUHAP is dominated by negative sentiment at 45.5%, followed by neutral sentiment at 32.0%, and positive sentiment at 22.5%. Performance evaluation shows that for the negative class, the model achieved a precision of 0.71, recall of 0.93, and F1-score of 0.80. For the neutral class, the precision was 0.74, recall 0.44, and F1-score 0.55, while the positive class reached a precision of 0.85, recall 0.80, and F1-score 0.82. Overall, the model achieved an accuracy of 74.07%, demonstrating that the application of TF-IDF and Naïve Bayes is effective in classifying public sentiment, despite persistent limitations in identifying neutral sentiment.

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