CLUSTERING DATA METEOROLOGI WILAYAH INDONESIA TIMUR DENGAN METODE K-MEANS DAN FUZZY C-MEANS

  • Gion Andrian (1) Universitas Tarumanagara
  • Desi Arisandi (2) Universitas Tarumanagara
  • Teny Handhayani (3*) Universitas Tarumanagara

  • (*) Corresponding Author
Keywords: climate change, clustering, fuzzy c-means, k-means, meteorology

Abstract

Climate change is a global issue that affect human life and the environment. Signs of climate change can be observed from long-term meteorological data.  This research uses clustering techniques with the K-Means and Fuzzy C-Means methods to group cities in the Eastern Indonesia region based on numerical daily time series meteorological data from 1 January 2010 to 31 August 2023. The variables are minimum temperature, maximum temperature, temperature average, humidity, rainfall, duration of sunlight, maximum wind speed, and average wind speed. The dataset was collected from 28 meteorological stations. The K-Means and Fuzzy C-Means methods obtained the same results, namely the highest silhouette value of 0.218 with the number of clusters k = 2. In general, the annual trend shows an increase in temperature and a decrease in wind speed which are signs of climate change. This research is an early study of climate change in East Indonesia. The results of this research are expected to contribute to the study of climate change in Indonesia.

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Published
2024-02-01
How to Cite
Andrian, G., Arisandi, D., & Handhayani, T. (2024). CLUSTERING DATA METEOROLOGI WILAYAH INDONESIA TIMUR DENGAN METODE K-MEANS DAN FUZZY C-MEANS. INTI Nusa Mandiri, 18(2), 100-106. https://doi.org/10.33480/inti.v18i2.5039
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