Studi Independent Prediksi Gempa Berbasis Website di Orbit Future Academy
DOI:
https://doi.org/10.59431/ajad.v3i3.235Keywords:
K-Means Clustering Algorithm, Independent Study, Orbit Future Academy, Anticipation, EarthquakeAbstract
The Certified Independent Study Program, which is part of the Independent Campus initiative and supported by the Ministry of Education and Culture, Research and Technology, involves many large companies in academic collaboration. One of them is Orbit Future Academy, which provides special programs in the fields of Artificial Intelligence and Data Science. This program is specifically designed to develop web-based applications for earthquake prediction. The methodology used combines advanced Artificial Intelligence techniques in the field of Data Science, with the application of the K-Means Clustering and K-Nearest Neighbors (KNN) algorithms in earthquake prediction systems. The main objective of this program is to improve knowledge and skills in the application of Artificial Intelligence and Data Science for earthquake prediction, especially around real estate investment, thereby contributing to more informed decision-making in this sector.
Downloads
References
Maolida, E.H., Mulyana, A., Adetia, E. and Rizki, A., 2022. Collaborative Planning and Teaching English Vocabulary with Virtual Reality: A Community Service in SD Gunung Batu Cianjur. AJAD: Jurnal Pengabdian kepada Masyarakat, 2(1), pp.73-81. DOI: https://doi.org/10.35870/ajad.v2i1.40.
Wanto, A., Defit, S., & Windarto, A. P. (2021). Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(2), 254-264. DOI: https://doi.org/10.29207/resti.v5i2.3031.
Saikin, S., Fadli, S., & Ashari, M. (2021). Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results. JISA (Jurnal Informatika dan Sains), 4(1), 22-27. DOI: https://doi.org/10.31326/jisa.v4i1.881.
Kusuma, W. A., Widodo, A. P., Santoso, R., Suharto, E., & Wirawan, P. W. (2022, August). Comparison of Machine Learning Algorithms for Rainfall Prediction in Semarang City. In 2022 9th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) (pp. 44-49). IEEE. DOI: https://doi.org/10.1109/ICITACEE55701.2022.9924039.
Manakane, S. E., Latue, P. C., & Rakuasa, H. (2023). Identifikasi Daerah Rawan Longsor Di DAS Wai Batu Gajah, Kota Ambon Menggunakan Metode Slope Morphology Dan Indeks Storie. Gudang Jurnal Multidisiplin Ilmu, 1(1), 29-36.
Tantyoko, H., Sari, D. K., & Wijaya, A. R. (2023). PREDIKSI POTENSIAL GEMPA BUMI INDONESIA MENGGUNAKAN METODE RANDOM FOREST DAN FEATURE SELECTION. IDEALIS: Indonesia Journal Information System, 6(2), 83-89. DOI: https://doi.org/10.36080/idealis.v6i2.3036.
Riza, H., Santoso, E. W., & Tejakusuma, I. G. (2020). UTILIZATION OF ARTIFICIAL INTELLIGENCE TO IMPROVE FLOOD DISASTER MITIGATION PEMANFAATAN KECERDASAN ARTIFISIAL UNTUK MENINGKATKAN MITIGASI BENCANA BANJIR. Jurnal Sains dan Teknologi Mitigasi Bencana, 15(1).
Leksono, A. S., Mustafa, I., Djati, M. S., Mastuti, R., Rumhayati, B., Suryanto, A., ... & Noviantari, Y. (2010). Eco-friendly Technology and Policy on Industrial and Regional Planning for Mitigation of Climate Change. 7th Basic Science National Seminar Proceeding Malang 20 Februari 2010.
Rakuasa, H., & Latue, P. C. (2023). Spatial Analysis of Landscape Suitability of Ambon City for Settlement Using Geographic Information System. Journal of Information Systems and Technology Research, 2(3), 133-140. DOI: https://doi.org/10.55537/jistr.v2i3.696.
Firdausa, F. (2019). Prediksi dan Analisis Data Gempa Bumi di Provinsi Bengkulu dengan Metode Artificial Neural Network. Cantilever: Jurnal Penelitian dan Kajian Bidang Teknik Sipil, 8(2), 45-49. DOI: https://doi.org/10.35139/cantilever.v8i2.5.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 AJAD : Jurnal Pengabdian kepada Masyarakat

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.