Classification of weather events in Lahat regency using the K-Nearest Neighbor method
DOI:
https://doi.org/10.12928/bamme.v5i2.14613Keywords:
Euclidean , KNN, Manhattan, Minkowski, Weather eventsAbstract
Weather event classification in a region is very important for various purposes, such as in the fields of transportation, health, agriculture, and others. Lahat has varying land elevations ranging from 26-106 meters above sea level in the East Merapi sub-district to 341-3032 meters above sea level in the Tanjung Sakti Pumi sub-district. It greatly affects local temperature, rainfall, and atmospheric pressure, which in turn affects the distribution of weather patterns and disasters such as floods. KNN is a prediction method that uses the concept of distance for a number of k nearest observations in determining the similarity between observations. Several metrics can be used for this prediction purpose. This study aims to predict weather events in Lahat Regency using the KNN method with several different distance metrics and then compare them to obtain the performance of the KNN prediction method. The results show that the Euclidean distance metric used in the KNN method has a better performance measurement, followed by the Manhattan and Minkowski metrics. In the Euclidean metric, the accuracy, precision, recall, f1-score, AUC, and MC value are 92.69%, 88.21%, 85.81%, 86.99%, 88.99%, and 76.37%, respectively.
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Copyright (c) 2025 Endang Sri Kresnawati, Yulia Resti, Ning Eliyati, Des Alwine Zayanti, Novi Rustiana Dewi, Irsyadi Yani
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