IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR (KNN) UNTUK PREDIKSI BENCANA GUNUNG BERAPI
DOI:
https://doi.org/10.36050/betrik.v15i01%20APRIL.250Keywords:
: machine learning, prediction, k-nearest neighbor, volcano disasterAbstract
The potential for natural disasters, particularly volcanic eruptions, is on the rise, necessitating technological innovations to enhance detection and response systems. This study focuses on the application of the K-Nearest Neighbor (KNN) algorithm to identify disasters occurring at Mount Merapi. The data utilized encompass various geophysical and meteorological parameters relevant to volcanic activity. Testing was conducted with the k-nearest value set at k=3, yielding an accuracy of 100%. However, when returning to the general setting (non-specific k), an accuracy of forty-four percent was achieved. The results of this research demonstrate that, by considering environmental and geological elements, the use of KNN can enhance early detection of volcanic disasters. This study makes a significant contribution to the development of more sophisticated disaster detection systems, although additional efforts are needed to improve overall accuracy.
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