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Roberto Kaban
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mechanical Engineering

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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

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. 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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. 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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 Fauziah Fitri Ramadhani Pane Muhamad Irfan Affandi Safarul Ilham Simatupang, Jihan Nadirah Surizar Rahmi Danur Zuhaira Agustari