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Julianthy, Denissya
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Computer Science & IT

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Pengelompokkan Pola Perubahan Cuaca Menggunakan Metode K-Medoids dan Gap Statistic Julianthy, Denissya; Hadiana, Asep Id; Ramadhan, Edvin
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7824

Abstract

Clustering daily weather patterns is an important process for understanding complex weather variations. However, commonly used methods such as K-Means have weaknesses due to their sensitivity to outliers and the need for manual clustering. This study proposes a combination of the K-Medoids and Gap Statistics methods to produce more stable and accurate clusters. Semarang's daily weather data from 2017 to 2023 was processed through cleaning, standardization with Standard Scaler, and dimensionality reduction using PCA. The Gap Statistics results indicate the optimal number of clusters is three: rainy, sunny, and cloudy. The clustering evaluation yielded a Silhouette Score of 0.3793, a Calinski-Harabasz Index of 1490.5604, and a Davies-Bouldin Index of 0.9031. These results indicate a fairly good cluster structure, although there is still room for improvement, especially in the separation between clusters.
Co-Authors Edvin Ramadhan Hadiana, Asep Id