IMPLEMENTASI RAPIDMINER PADA KLASTERISASI GEMPA BUMI DI INDONESIA BERDASARKAN KEDALAMAN MENGGUNAKAN K-MEANS
DOI:
https://doi.org/10.69714/w0m9zv32Keywords:
Gempa Bumi, RapidMiner, Klasterisasi, Data Mining, K-meansAbstract
Indonesia is a meeting place for four major tectonic plates, namely the Carolina Plate, the Philippine Sea Plate, the Indo-Australian Plate, and the Eurasian Plate. Every year thousands of earthquakes strike, causing material losses, infrastructure damage, and even loss of life. Therefore, understanding the characteristics of earthquakes in Indonesia is a crucial step to mitigate disaster risks and improve community preparedness. The dataset used comes from the Kaggle.com website, the dataset is taken from the Earthquake Repository managed by BMKG. The K-Means algorithm is used as a clustering process method in this study. Clustering using K-Means aims to identify the dominant types of earthquakes that occur in regions of Indonesia. The application of this method to earthquakes that occurred in Indonesia based on the identified depth in cluster_0 shows the least earthquakes with the deepest earthquake type. Cluster_1 shows a shallow earthquake type. While cluster_2 is the earthquake with the most occurrences and shows an earthquake with a moderate depth.
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