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ANALISIS POLA CURAH HUJAN WILAYAH PESISIR TIMUR SUMBAGSEL BERBASIS MACHINE LEARNING SEBAGAI KONTRIBUSI MATA KULIAH FISIKA KOMPUTASI

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Machine learning-based analysis of monsoon rainfall in the eastern coastal region of South Sumatra has been completed. The aim of this research is to analyze monsoonal rainfall in the eastern coastal region of South Sumatra based on machine learning. The research method used in this research is quantitative analysis using secondary data. The rainfall data used is from the Stations of the Meteorology, Climatology and Geophysics Agency, namely the Sultan Mahmud Badaruddin II Meteorological Station, the Radin Inten II Meteorological Station, and the Depati Amir Meteorological Station for the 1991-2020 period. The data analysis carried out was annual rainfall analysis, rainfall pattern analysis, climate change index analysis, analysis of factors that influence rainfall, namely ENSO and IOD, correlation analysis, and linear regression analysis using machine learning based on Python and Google Colab. Based on the analysis, the results show that rainfall in monsoon areas varies from 1,000 mm per year to more than 2,500 mm/year. Rainfall in monsoon areas has a unimodial pattern (one peak of the rainy season). Rainfall in the East Coast region of South Sumatra is influenced by ENSO and IOD. Combining the two together causes changes in rainfall. Both have an impact on annual rainfall and the values of the climate change indices PRCPTOT, SDII, CWD, and CDD.

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Inventory Code Barcode Call Number Location Status
2407002318T142723T1427232024Central Library (References)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1427232024
Publisher
Indralaya : Prodi Pendidikan Fisika, Fakultas Keguruan dan Ilmu Pendidikan Universitas Sriwijaya.,
Collation
xv, 69 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
538.07
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Prodi Ilmu Fisika
Specific Detail Info
-
Statement of Responsibility
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