Pengelompokan Zona Musim di Indonesia Berdasarkan Data Curah Hujan Menggunakan Time Series Based Clustering

Setiawan, Dedi (2018) Pengelompokan Zona Musim di Indonesia Berdasarkan Data Curah Hujan Menggunakan Time Series Based Clustering. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Curah hujan merupakan unsur cuaca yang sangat penting bagi perkembangan iklim di Indonesia, dan mempunyai variasi yang tinggi antar wilayah dan waktu. BMKG merupakan lembaga yang bertugas untuk mengamati curah hujan di Indonesia, saat ini BMKG memiliki jumlah stasiun pengamatan sebanyak 116. BMKG menggunakan data satelit TRMM untuk memprediksi curah hujan permukaan pada wilayah yang tidak terjangkau oleh stasiun pengamatan. Perlu dilakukan verifikasi untuk mengetahui sebarapa baik data satelit TRMM digunakan sebagai prediksi curah hujan permukaan. Kriteria verifikasi yang digunakan dalam penelitian ini adalah probability of detection (PoD), bias score, dan false alarm ratio (FAR). Didapatkan hasil bahwa data TRMM tidak bisa langsung digunakan untuk memprediksi curah hujan ekstrim dan hujan lebat diseluruh wilayah Indonesia karena mempunyai performa yang rendah berdasarkan kriteria PoD, Bias Score, dan FAR. Pengolompokan Zona musim di Indonesia didapatkan cluster optimum menggunakan jarak euclidean pada data agregrat bulanan terbentuk 3 zona musim dengan nilai silhouette sebesar 0,495. ==================================================================================================== Rainfall is an important element of weather for climate development in Indonesia, and it has a high variation across regions and time. BMKG is an institution that responsible for observing rainfall in Indonesia, currently BMKG has 116 observation stations. BMKG uses TRMM satellite data to predict surface rainfall in areas did not covered by observation stations. Verification is needed to find out how well the TRMM satellite data is used as a predictable surface rainfall. The verification criteria used in this study are probability of detection (PoD), bias score, and false alarm ratio (FAR). The result is that the TRMM data can not be directly used to predict extreme rainfall and heavy rain in all area of Indonesia because it has low performance based on PoD, Bias Score, and FAR criteria. Clustering zone of seasons in Indonesia based on clustering obtained the optimum cluster using euclidean distance on monthly rainfall average data with 3 seasons formed with the value of silhouette of 0,495.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: BMKG, Cluster Time Series, TRMM, Verifikasi, Zona Musim
Subjects: Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry)
Q Science > QA Mathematics > QA278.55 Cluster analysis
Q Science > QA Mathematics > QA76.9.D343 Data mining
T Technology > TD Environmental technology. Sanitary engineering > TD171.75 Climate change mitigation
Divisions: Faculty of Mathematics, Computation, and Data Science > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User: Dedi Setiawan
Date Deposited: 01 Jul 2021 07:17
Last Modified: 01 Jul 2021 07:17
URI: https://repository.its.ac.id/id/eprint/56777

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