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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan MATHunesa: Jurnal Ilmiah Matematika
Riani, Rosalina Agista
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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ANALYSIS OF RAINFALL IN INDONESIA USING A TIME SERIES-BASED CLUSTERING APPROACH Sofro, A'yunin; Riani, Rosalina Agista; Khikmah, Khusnia Nurul; Romadhonia, Riska Wahyu; Ariyanto, Danang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp0837-0848

Abstract

Indonesia has a tropical climate and has two seasons: dry and rainy. Prolonged drought can cause drought disasters, and rain can cause floods and landslides. According to information from the Meteorology, Climatology, and Geophysics Agency (BMKG), natural disasters such as floods and landslides due to heavy rains have been a severe problem in Indonesia for the past five years. Different regional characteristics can affect the intensity of rain that falls in every province in Indonesia. It can be grouped to determine which provinces have similar characteristics to natural disasters due to rainfall. Later, it can provide information to the government and the public so that they are more aware of natural disasters. So, it is necessary to research and classify provinces in Indonesia for rainfall with cluster analysis. The data used is secondary rainfall data taken from the official BMKG website. Cluster analysis of rainfall in 34 provinces in Indonesia used hierarchical and non-hierarchical methods in this study. The approach that is used in this research limits our clustering of the data. Further research with a machine learning approach is recommended. For the clustering method, the agglomerative hierarchical method includes single, average, and complete linkage. The non-hierarchical method includes k-medoids and fuzzy c-means. The cluster analysis results show that the dynamic time warping (DTW) distance measurement method with the average linkage method has the most optimal cluster results with a silhouette coefficient value of 0.813.
PENGELOMPOKAN BERDASARKAN GARIS KEMISKINAN PENDEKATAN TIME SERIES BASED CLUSTERING DI PROVINSI JAWA TIMUR Riani, Rosalina Agista; Sofro, A'yunin
MATHunesa: Jurnal Ilmiah Matematika Vol. 11 No. 3 (2023)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v11n3.p478-488

Abstract

Provinsi Jawa Timur disebut sebagai provinsi terbesar di Pulau Jawa yang memiliki luas wilayah sebesar 48.037 km2 dan banyaknya penduduk 41.416.407 jiwa. Banyaknya penduduk dapat menyebabkan masalah sosial seperti kemiskinan, salah satunya karena pembangunan sarana dan prasana tidak merata. Dalam tolak ukur kemiskinan terdapat faktor garis kemiskinan, yaitu pendapatan minimum yang harus dicapai seseorang untuk memperoleh standar hidup yang layak. Dari faktor tersebut dapat dilakukan pengelompokan untuk mengetahui Kabupaten/Kota manakah yang darurat akan faktor garis kemiskinan. Nantinya dapat menjadi informasi kepada masyarakat dan pemerintah terkait wilayah manakah yang perlu diperhatikan khusus terkait masalah kemiskinan. Sehingga perlu dilakukan penelitian untuk mengelompokan Kabupaten/Kota di Provinsi Jawa Timur terhadap garis kemiskinan dengan analisis cluster. Analisis cluster pada penelitian ini menggunakan metode ukuran jarak Short Time Series (STS) distance, Autocorrelation Function (ACF) distance, dan Dynamic Time Warping (DTW) distance. Untuk metode clustering yang digunakan yaitu metode hirarki agglomerative yang terdiri dari single linkage, average linkage, dan complete linkage. Hasil cluster yang terbentuk pada penelitian ini yaitu sebanyak 5 cluster dengan hasil cluster paling optimal yaitu metode ukuran jarak Autocorrelation Function (ACF) distance dengan metode average linkage yang memiliki nilai koefisien silhouette 0,8161.
Co-Authors Danang Ariyanto Khikmah, Khusnia Nurul Romadhonia, Riska Wahyu Sofro, A'yunin