Sistem prediksi produksi padi di Pulau Jawa menggunakan long short term memory berbasis website

Shafa Zahira Nailatus Syahida; Hadiwiyatno; Hendro Darmono

doi:10.33795/eltek.v24i2.10868

Authors

Shafa Zahira Nailatus Syahida Politeknik Negeri Malang
Hadiwiyatno Politeknik Negeri Malang
Hendro Darmono Politeknik Negeri Malang

DOI:

https://doi.org/10.33795/eltek.v24i2.10868

Keywords:

Long Short Term Memory, Prediksi, Produksi Padi

Abstract

Padi merupakan komoditas pangan utama di Indonesia sehingga kestabilan produksinya penting bagi ketahanan pangan nasional. Selama periode 2021–2024, produksi padi di Pulau Jawa menurun sebesar 6,16%, sedangkan jumlah penduduk meningkat 2,71%. Penelitian ini bertujuan mengembangkan model Long Short Term Memory untuk memprediksi produksi padi empat bulan ke depan pada enam provinsi di Pulau Jawa dalam sistem berbasis web. Model menggunakan data produksi padi, luas panen, dan variabel iklim periode Januari 2018–Desember 2025. Penelitian meliputi preprocessing data, pengujian 216 kombinasi hyperparameter, training dengan optimizer Adam, serta evaluasi menggunakan galat absolut rata-rata, akar galat kuadrat rata-rata, dan persentase galat absolut rata-rata. Hasil penelitian menunjukkan bahwa setiap provinsi memiliki konfigurasi hyperparameter terbaik yang berbeda. Berdasarkan persentase galat absolut rata-rata, model menghasilkan akurasi sangat baik di Jawa Timur, baik di Jawa Tengah, Jawa Barat, dan Daerah Istimewa Yogyakarta, cukup di Banten, serta buruk di Daerah Khusus Ibukota Jakarta.

ABSTRACT

Rice is Indonesia's primary staple crop, making production stability essential for maintaining national food security. During the period of 2021–2024, rice production on Java Island decreased by 6.16%, while the population increased by 2.71%. This study aims to develop a Long Short-Term Memory model to forecast rice production for the next four months across six provinces on Java Island through a web-based system. The model utilizes historical data on rice production, harvested area, and climate variables from January 2018 to December 2025. The research includes data preprocessing, evaluation of 216 hyperparameter combinations, model training using the Adam optimizer, and performance evaluation using mean absolute error, root mean squared error, and mean absolute percentage error. The results indicate that each province requires a different optimal hyperparameter configuration. Based on the mean absolute percentage error, the model achieved very good accuracy in East Java, good accuracy in Central Java, West Java, and the Special Region of Yogyakarta, fair accuracy in Banten, and poor accuracy in Jakarta.

References

Badan Pusat Statistik, “Produksi Padi dan Beras Menurut Provinsi.” Accessed: Jan. 07, 2026. [Online].Available:https://www.bps.go.id/id/statisticstable/3/ZDNaak0yODBUVTlGYW5sa2REUkVUVVY1YVZkbmR6MDkjMyMwMDAw/produksi-padi-sup-1--sup--dan-beras-menurut-provinsi.html

Republik Indonesia, “Undang-Undang Republik Indonesia Nomor 18 Tahun 2012 tentang Pangan.” Accessed: Feb. 09, 2026. [Online]. Available: https://peraturan.bpk.go.id/Home/Details/39100

Badan Pusat Statistik Kota Bitung, “BPS Rilis Data Produksi Padi melalui Metode KSA,” 2018, Accessed: Feb. 03, 2026. [Online]. Available: https://bitungkota.bps.go.id/id/news/2018/10/24/47/bps-rilis-data-produksi-padi-melalui-metode-ksa.html

S. Rahma, Haslindah, and M. Muis, “Dampak perubahan iklim terhadap produktivitas pertanian dan kesejahteraan petani Muslim Bugis di Desa Ulaweng Riaja Kabupaten Bone,” Indonesian Journal of Intellectual Publication, vol. 5, no. 3, pp. 257–266, Jul. 2025, doi: 10.51577/ijipublication.v5i3.710.

P. C. Adhany, C. Wulandari, B. Intan, and B. Santoso, “Prediksi Padi Menggunakan Algoritma Long Short Term Memory,” Journal of Informatics Management and Information Technology, vol. 5, no. 2, pp. 120–127, Apr. 2025, doi: 10.47065/jimat.v5i2.496.

International Rice Research Institute (IRRI), “Growth stages of the rice plant,” Rice Knowledge Bank, n.d. [Online]. Available: https://www.knowledgebank.irri.org. Accessed: Feb. 7, 2026.

A. Fatimah, B. Badieah, and S. F. C. Haviana, “Implementation of Bidirectional Long Short Term Memory for Stock Entity Identification,” Jurnal Teknik Informatika dan Sistem Informasi (JuTISI), vol. 11, no. 1, pp. 78–90, Apr. 2025, doi: 10.28932/jutisi.v11i1.9775.

A. Aslam and S. Farhan, “Enhancing rice yield prediction: A deep fusion model integrating ResNet50-LSTM with multi source data,” PeerJ Computer Science, vol. 10, Art. no. e2219, 2024, doi: 10.7717/peerj-cs.2219

A. Zamahzari and Puryantoro, “Forecasting produksi padi dan konsumsi beras di Provinsi Jawa Timur,” Cemara, vol. 20, no. 1, pp. 27–38, Mei 2023.

Badan Pusat Statistik, Luas Panen dan Produksi Padi di Indonesia 2024. Jakarta, Indonesia: Badan Pusat Statistik, Aug. 2025

A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd ed. Sebastopol, CA, USA: O'Reilly Media, 2022.

Scikit-learn Developers, “MinMaxScaler,” Scikit-learn Documentation, n.d. Accessed: Jun. 27, 2026. [Online].Available:https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html

R.J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd ed. Melbourne, Australia: OTexts, 2021. Accessed: Jun. 27, 2026. [Online]. Available: https://otexts.com/fpp3/

T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not,” Geoscientific Model Development, vol. 15, no. 14, pp. 5481–5487, 2022, doi: 10.5194/gmd-15-5481-2022.

Kadir, K., & Prasetyo, O. R. (2021). Peramalan luas panen padi Indonesia dengan model ETS (Error, Trend, Seasonal). Jurnal MSA (Matematika Dan Statistika Serta Aplikasinya), 9(1), 7-15.

C. D. Lewis, Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. London, U.K.: Butterworth Scientific, 1982.