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All Journal KOMUNIKA JSSF : Journal of Sport Science and Fitness Jurnal Meteorologi Klimatologi dan Geofisika Jurnal At-Taghyir : Jurnal Dakwah dan Pengembangan Masyarakat Desa Jurnal Al-Irsyad: Jurnal Bimbingan Konseling Islam TADBIR: Jurnal Manajemen Dakwah FDIK IAIN Padangsidimpuan Web of Scientist: International Scientific Research Journal Komunikasia: Journal of Islamic Communication and Broadcasting Journal of Geosciences and Environmental Studies Communicative : Jurnal Komunikasi dan Penyiaran Islam Journal of Computation Physics and Earth Science
Widodo, Anton
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Religion Humanities Astronomy Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Journal : Journal of Computation Physics and Earth Science

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Analisis Perbandingan Model Regresi Linier dan XGBoost untuk Mengkaji Dampak ENSO terhadap Curah Hujan di Kota Ternate Tahun 2023 Amra, Firman Almaliky Gapri; Widodo, Anton; Nugraha, Muchamad Rizqy
Journal of Computation Physics and Earth Science (JoCPES) Vol 5 No 1 (2025): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63581/JoCPES.v5i1.02

Abstract

The purpose of this study is to evaluate how well two prediction models—linear regression and XGBoost—perform in assessing how ENSO (El Niño-Southern Oscillation) affects rainfall in Ternate City in 2023. The Meteorology, Climatology, and Geophysics Agency (BMKG) provided monthly rainfall data, while the Bureau of Meteorology (BOM) in Australia provided ENSO index data. Performance indicators such Pearson correlation analysis, the coefficient of determination (R-squared), and mean squared error (MSE) were used in the evaluation. According to the findings, the two models perform differently when it comes to capturing the pattern of the link between rainfall and ENSO; XGBoost is more adaptable but has a tendency to overfit on small amounts of data, whereas linear regression obtains a better R-squared value.
Co-Authors Amra, Firman Almaliky Gapri Anak Agung Gede Sugianthara Anantama, Agam Anggie Sinthiya, Ida Ayu Putu Deni Septiadi Dwi Nopriyana, Rahma Fathur Rohman, Fathur Hadi Suntoko Hemlan Elhany Lumbu, Aliyandi Muhajir Muhajir, Muhajir Noor, Riza Arian Nugraha, Muchamad Rizqy Nurislamiah, Mia Pangestu, Dimas Yoga Rachman, Evy eptiana Rachman, Evy Septiyana Rahmad, Andi Riyadh UB, Ahmad Siti Baitul Mukarromah UB, Ahmad Riyadh