PREDIKSI CURAH HUJAN MENGGUNAKAN METODE ARIMA
(Studi kasus: Kabupaten Sleman, Semarang, dan Surabaya)
Abstract
Unpredictable rainfall can cause various negative impacts, especially in regions highly dependent on agriculture and infrastructure, such as Sleman Regency, Semarang, and Surabaya. Accurate weather predictions are crucial for anticipating disaster risks like floods, landslides, and droughts, as well as maintaining the sustainability of these vital sectors. This study aims to forecast rainfall in these three regions using the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model was chosen for its flexibility in adapting to existing data patterns and its ability to provide accurate and economical predictions. Historical rainfall data from these three regions were analyzed using various ARIMA parameters (p, d, q) to identify the most optimal model. The results indicate that the ARIMA model can provide reasonably accurate rainfall predictions across all three regions with varying degrees of error. Significant differences were found in the model’s performance across the regions, influenced by local geographical and climatic characteristics.
Downloads
References
Al’afi, A. M., Widiarti, Kurniasari, D., & Usman, M. (2020). Peramalan Data Time Series Seasonal Menggunakan Metode Analisis Spektral Berdasarkan data yang tersedia diperoleh model terbaik untuk peramalan penumpang pesawat di Bandar Udara Raden Intan II adalah Seasonal ARIMA (0. In Jurnal Siger Matematika (Vol. 01, Issue 01).
Alexander, & Harahab, S. (2009). LAPORAN TUGAS AKHIR PERENCANAAN EMBUNG TAMBABOYO KABUPATEN.
Aulia, N. N., Gunawan, P. H., & Rohmawati, A. A. (2018). Prediksi Curah Hujan Menggunakan Gerak Brown dan Rataan Tahunan Data Pada Missing Values. Indonesian Journal on Computing (Indo-JC), 3(2), 71. https://doi.org/10.21108/indojc.2018.3.2.233
Ayuni, R., & Saputri, F. (2019). PENERAPAN METODE FUZZY TIME SERIES UNTUK PREDIKSI PENJUALAN BERBASIS WEB PADA TOKO GROSIR 3 RODA SENGKALING. In Jurnal Mahasiswa Teknik Informatika (Vol. 3, Issue 1).
Fadlan, A., Safril, A., Suwandi, Veanti, D. P. O., Nugraheni, I. R., Septiadi, D., Harahap, D., Nuraini, N., & Munawar. (2022). Pengetahuan Tentang Iklim dan Cuaca Untuk Kemajuan Pertanian di Kabupaten Indramayu Jawa Barat.
Haghshenas, S. S., Pirouz, B., Haghshenas, S. S., Pirouz, B., Piro, P., Na, K. S., Cho, S. E., & Geem, Z. W. (2020). Prioritizing and analyzing the role of climate and urban parameters in the confirmed cases of COVID-19 based on artificial intelligence applications. International Journal of Environmental Research and Public Health, 17(10). https://doi.org/10.3390/ijerph17103730
Herlina, N., & Prasetyorini, A. (2020). Pengaruh Perubahan Iklim pada Musim Tanam dan Produktivitas Jagung (Zea mays L.) di Kabupaten Malang (Effect of Climate Change on Planting Season and Productivity of Maize (Zea mays L.) in Malang Regency). Jurnal Ilmu Pertanian Indonesia (JIPI), Januari, 25(1), 118–128. https://doi.org/10.18343/jipi.25.1.118
Hutasuhut, A. H., Anggraeni, W., & Tyasnurita, R. (2014). Pembuatan aplikasi pendukung keputusan untuk peramalan persediaan bahan baku produksi plastik blowing dan inject menggunakan metode ARIMA (Autoregressive Integrated Moving Average) di CV. Asia. Jurnal Teknik ITS, 3(2), A169-A174.
Khan, M. Y., Qayoom, A., Nizami, M. S., Siddiqui, M. S., Wasi, S., & Raazi, S. M. K. U. R. (2021). Automated Prediction of Good Dictionary EXamples (GDEX): A Comprehensive Experiment with Distant Supervision, Machine Learning, and Word Embedding-Based Deep Learning Techniques. Complexity, 2021. https://doi.org/10.1155/2021/2553199
Laia, M. L., & Setyawan, Y. (2020). Perbandingan hasil klasifikasi curah hujan menggunakan metode SVM dan NBC. Jurnal Statistika Industri Dan Komputasi, 5(02), 51-61.
Masum, S. J. H. (2019). Climatic Hazards in Bangladesh-A Literature Review. https://doi.org/10.13140/RG.2.2.10239.94882
Nur, I., & Astuti, S. P. (2006). Mengolah data statistik dengan mudah menggunakan minitab 14. Yogyakarta: Andi.
Polawan, S. S. M., & Alam, F. (2019). MEMAHAMI BENCANA BANJIR DAN LONGSOR. https://www.researchgate.net/publication/346678807
Rokach, L., & Maimon, O. (2006). Decision Trees. In Data Mining and Knowledge Discovery Handbook (pp. 165–192). Springer-Verlag. https://doi.org/10.1007/0-387-25465-x_9
Safar, N. Z. M., Ramli, A. A., Mahdin, H., Ndzi, D., & Khalif, K. M. N. K. (2019). Rain prediction using fuzzy rule based system in North-West Malaysia. Indonesian Journal of Electrical Engineering and Computer Science, 14(3), 1572–1581. https://doi.org/10.11591/ijeecs.v14.i3.pp1572-1581
Shumway, R. H., & Stoffer, D. S. (2000). Time series regression and ARIMA models. Time Series Analysis and Its Applications, 89-212. https://doi.org/https://doi.org/10.1007/978-1-4757-3261-0_2
Copyright (c) 2025 Hafidh Adiyatma Ramadhan, Irving Vitra Paputungan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Jurnal allows anyone to compose, correct, and do derivative works, even for commercial purposes, as long as they credit for the original work. This license is the freest. It is recommended for maximum distribution and use of licensed material.
The submitted paper is assumed not to contain any proprietary materials that are not protected by patent rights or patent applications; The responsibility for technical content and protection of proprietary materials rests with the authors and their organizations and not the responsibility of journal or its editorial staff. The primary (first/appropriate) author is responsible for ensuring that the article has been viewed and approved by all other authors. The author's responsibility is to obtain all necessary copyright waivers to use any copyrighted material in the manuscript before submission.
Jurnal Pendidikan, Sains dan Teknologi allows the author(s) to hold the copyright without restrictions and allow the author(s) to retain publishing rights without restrictions. Jurnal Pendidikan, Sains dan Teknologi CC-BY-SA or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work. Jurnal Pendidikan, Sains dan Teknologi allows the author(s) to hold the copyright without restrictions and allow the author(s) to retain publishing rights without restrictions. Jurnal Pendidikan, Sains dan Teknologi CC-BY-SA or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work.
In developing strategy and setting priorities Jurnal Pendidikan, Sains dan Teknologi recognize that free access is better than priced access, libre access is better than free access, and libre under CC-BY-SA or the equivalent is better than libre under more restrictive open licenses. We should achieve what we can when we can. We should not delay achieving free in order to achieve libre, and we should not stop with free when we can achieve libre.
Jurnal Pendidikan, Sains dan Teknologi is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
You are free to:
- Share a copy and redistribute the material in any medium or format
- Adapt a remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.