Analisis Curah Hujan Menggunakan Machine Learning Metode Regresi Linier Berganda Berbasis Python dan Jupyter Notebook

Main Article Content

jesi pebralia

Abstract

Indonesia is a country located on the equator. As a result, Indonesia has a dry season and a rainy season. Rainfall prediction is very useful in various fields. The prediction method that is currently developing rapidly is the prediction method using artificial intelligence (AI) techniques. Machine learning is a subset of AI. The application of multiple linear regression algorithms in machine learning can be used to predict a dependent variable with various types of independent variables that affect it. In this study, rainfall prediction has been carried out involving three independent variables, namely wind speed, maximum air temperature, and minimum air temperature with dataset obtained from the kaggle.com site. The dataset used is 6,574 data, where the data is grouped into training data as much as 80% and test data as much as 20%. Multiple linear regression algorithm is written in Python programming language and implemented using jupyter notebook. In this study, a multiple linear regression model was produced with the equation ,, MSE value was 14.02, RMSE was 3.74, and MAE was 2.27.

Downloads

Download data is not yet available.

Article Details

How to Cite
“Analisis Curah Hujan Menggunakan Machine Learning Metode Regresi Linier Berganda Berbasis Python Dan Jupyter Notebook”. Jurnal Ilmu Fisika dan Pembelajarannya (JIFP) 6, no. 2 (December 29, 2022): 23–30. Accessed April 3, 2025. https://jurnal.radenfatah.ac.id/index.php/jifp/article/view/13958.
Section
Artikel

How to Cite

“Analisis Curah Hujan Menggunakan Machine Learning Metode Regresi Linier Berganda Berbasis Python Dan Jupyter Notebook”. Jurnal Ilmu Fisika dan Pembelajarannya (JIFP) 6, no. 2 (December 29, 2022): 23–30. Accessed April 3, 2025. https://jurnal.radenfatah.ac.id/index.php/jifp/article/view/13958.

References

Adiguno, S., Syahra, Y., & Yetri, M. (2022). Prediksi Peningkatan Omset Penjualan Menggunakan Metode Regresi Linier Berganda. Jurnal Sistem Informasi Triguna Dharma (JURSI TGD), 1(4), 275-281.
Afkarina, N. K., Widodo, A. W., & Furqon, M. T. (2019). Implementasi Regresi Linier Berganda Untuk Prediksi Jumlah Peminat Mata Kuliah Pilihan. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 2548, 964X.
Amrin, A. (2016). Data Mining Dengan Regresi Linier Berganda Untuk Peramalan Tingkat Inflasi. Techno Nusa Mandiri: Journal of Computing and Information Technology, 13(1), 74-79.
Budiman, I., & Akhlakulkarimah, A. N. (2016). Aplikasi Data Mining Menggunakan Multiple Linear Regression Untuk Pengenalan Pola Curah Hujan. Klik-Kumpulan Jurnal Ilmu Komputer, 2(1), 34-33.
Erfiana, D., Prabowo, A., Tripena, A., & Riyadi, S. (2020). Penentuan Harga Premi Asuransi Pertanian Berbasis Indek Curah Hujan Dengan Model Black-Scholes.
Fadholi, A. (2013). Persamaan regresi prediksi curah hujan bulanan menggunakan data suhu dan kelembapan udara di Ternate. Statistika, 13(1).
Faisal, M.R, dan Nugrahadi, D.T. 2019. Belajar Data Science: Klasifikasi dengan Bahasa Pemrograman R. Banjarbaru: Scripta Cendekia.
Id, Ibnu Daqiqil. 2021. Machine Learning: Teori, Studi Kasus, dan Implementasi Menggunakan Python. Riau: UR Press.
Kusuma, Purba Daru. 2020. Machine Learning Teori, Program, dan Studi Kasus. Yogyakarta: Deepublish.
Laksono, S. S., & Nurgiyatna, N. (2020). Sistem Pengukur Curah Hujan sebagai Deteksi Dini Kekeringan pada Pertanian Berbasis Internet of Things (IoT). Emitor: Jurnal Teknik Elektro, 20(2), 117-121.
Nafi'iyah, N., & Maulidi, N. F. (2022). Linear regression for discounting presentation recommendations (kaggle dataset). Jurnal teknologi informasi dan komunikasi, 13(2), 67-73.
Nafi'iyah, N. (2019). Prediksi jumlah penjualan pada toko makmur jaya elektronik dengan regresi linier. RESEARCH: Journal of Computer, Information System & Technology Management, 2(2), 47-50.
Putri, E. R. S., Novianti, F., Yasmin, Y. R. A., & Novitasari, D. C. R. (2021). prediksi kasus aktif kumulatif covid-19 di indonesia menggunakan model regresi linier berganda. Transformasi: Jurnal Pendidikan Matematika Dan Matematika, 5(2), 567-577.
Subakti, dkk. 2022. Artificial Intelligence. Bandung: Media Sains Indonesia.
Surmaini, E., Runtunuwu, E., & Las, I. (2011). Upaya sektor pertanian dalam menghadapi perubahan iklim. Jurnal Litbang Pertanian, 30(1), 1-7.
YUDA, A. (2011). Analisa Pembiayaan Budidaya Lebah Madu Apis Mellifera pada Periode Musim Hujan di Kecamatan Tumpang Kabupaten Malang (Doctoral dissertation, University of Muhammadiyah Malang).