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All Journal Edsence: Jurnal Pendidikan Multimedia
Alwafi, Fauzan Maftuh
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Computer Science & IT Education Other

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Comparative Analysis of K-Means and K-Medoids Clustering Methods on Weather Data of Denpasar City Prihasto, Bima; Darmansyah, Darmansyah; Yuda, Dastin Pranata; Alwafi, Fauzan Maftuh; Ekawati, Herliana Nur; Sari, Yustika Perwita
Jurnal Pendidikan Multimedia (Edsence) Volume 5 No 2 (December 2023)
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/edsence.v5i2.65925

Abstract

By applying data mining methods, particularly clustering techniques, the weather data of Denpasar city can be grouped based on similar characteristics. This provides deep insight into weather patterns, useful for more optimised travel planning. This research positively impacts tourism, helping stakeholders understand weather patterns in more detail. Furthermore, in-depth knowledge of weather conditions improves preparedness for potential global climate change. The clustering results can be visualised in a three-dimensional cartesian diagram, providing a clear picture of the various weather conditions using attributes such as temperature, precipitation, and humidity. Through Kaggle's Denpasar Weather Data dataset, with 264,924 data and 32 columns, this study illustrates that cloudy weather dominates in the K-Means and K-Medoids clustering process on rain data within one hour. At three hours, K-Means shows the dominance of cloudy weather and the possibility of rain, while K-means dominates all clusters. At six hours, K-Means dominate in sunny and rainy weather, while K-Medoids dominate evenly in all clusters.
Co-Authors Ekawati, Herliana Nur Prihasto, Bima Sari, Yustika Perwita Solly Aryza Yuda, Dastin Pranata