Application of Support Vector Regression for Rice Production Prediction in Manokwari Regency
DOI:
https://doi.org/10.32664/j-intech.v14i03.2439Keywords:
Machine Learning, Manokwari Regency, Prediction, Rice Production, Support Vector MachineAbstract
Annual fluctuations in rice production present significant challenges for agricultural planning and regional food security, particularly in areas where agricultural productivity is strongly influenced by environmental and production-related factors. Developing an accurate forecasting model is therefore essential to support evidence-based decision-making and improve agricultural resource management. Although Support Vector Regression (SVR) has demonstrated promising performance in agricultural prediction, previous studies have primarily focused on large-scale datasets or relied on default model parameters, limiting their applicability to local agricultural conditions. Therefore, this study aims to develop and evaluate a GridSearchCV-optimized Support Vector Regression model for predicting annual rice production in Manokwari Regency. Secondary data were obtained from the Agricultural Statistics Database maintained by the Indonesian Ministry of Agriculture, rainfall records from Rendani Meteorological Station, and statistical publications issued by the Central Statistics Agency of Manokwari Regency. The proposed framework integrates data preprocessing, systematic hyperparameter optimization using GridSearchCV, and Support Vector Regression modelling. The predictive capability of the model was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The results demonstrate that the selected model employed the Radial Basis Function (RBF) kernel with C = 1000, gamma = 0.01, and epsilon = 0.001, achieving an MAE of 460.15, RMSE of 718.86, MAPE of 5.36%, and an R² value of 0.9578. These findings demonstrate satisfactory predictive performance and suggest that the proposed model has the potential to serve as a decision-support tool for agricultural production planning and regional food security management in Manokwari Regency.
References
[1] M. I. Kharisma S, A. I. Hadiana, and E. Ramadhan, “Model Prediksi Produksi Padi Berdasarkan Curah Hujan dan Suhu Menggunakan Regresi Linier Berganda,” Jurnal Algoritma, vol. 22, no. 2, pp. 704–716, 2025, doi: 10.33364/algoritma/v.22-2.2793.
[2] L. Fatimah, Martanto, A. R. Dikananda, and A. Rifa’i, “Algoritma Regresi Linear untuk Prediksi Hasil Panen dan Strategi Produksi Padi di Kabupaten Cirebon,” Jurnal Informatika Teknologi dan Sains (JINTEKS), vol. 7, no. 2, pp. 464–472, 2025.
[3] H. Putra and N. Ulfa, “Jurnal Nasional Teknologi dan Sistem Informasi Penerapan Prediksi Produksi Padi Menggunakan Artificial Neural Network Algoritma Backpropagation,” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 06, no. 02, pp. 100–107, 2020.
[4] Setiawan Cahyono and Muhammad Imron Rosadi, “Penerapan Artificial Neural Network untuk Prediksi Produksi Padi di Sumatera,” Jurnal Informatika Polinema, vol. 11, no. 4, pp. 487–494, 2025, doi: 10.33795/jip.v11i4.7727.
[5] D. Manurung, B. Zealtiel, and A. H. Lubis, “Prediksi Produksi Tanaman Padi di Indonesia dengan Menggunakan Algoritma Random Forest Regressor,” Journal of Computing and Informatics Research, vol. 4, no. 3, pp. 345–345, 2025, doi: 10.47065/comforch.v4i3.2125.
[6] F. Yasin, M. R. Firmansyah, D. Aldo, and M. A. Amrustian, “Multivariate Forecasting of Paddy Production: A Comparative Study of Machine Learning Models,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 3, pp. 1431–1442, 2025, doi: 10.52436/1.jutif.2025.6.3.4681.
[7] H. Muradi, A. Saefuddin, I. M. Sumertajaya, A. M. Soleh, and D. D. Domiri, “Support Vector Regression (Svr) Method for Paddy Growth Phase Modeling Using Sentinel-1 Image Data,” Media Statistika, vol. 16, no. 1, pp. 25–36, 2023, doi: 10.14710/medstat.16.1.25-36.
[8] R. Yunis, Sudarto, and I. Adiputra Pardosi, “Enhancing Rice Production Prediction: A Comparative Machine Learning Analysis of Climate Variables,” Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), vol. 13, no. 1, pp. 91–104, 2024, doi: 10.23887/janapati.v13i1.71527.
[9] E. E. Sijabat, “Support Vector Regression-Based Prediction of Rice Production Across Provinces in Sumatra Island,” Brilliance: Research of Artificial Intelligence, vol. 5, no. 2, pp. 1199–1206, 2025, doi: 10.47709/brilliance.v5i2.7429.
[10] H. Purnomo, R. Estian Pambudi, O. Arifin, F. Kurniawan Ikhsan, P. Negeri Lampung, and I. Darmajaya Bandar Lampung, “Analisis Prediksi Produksi Tanaman Padi Berdasarkan Variabel Iklim Menggunakan Support Vector Regression,” Aisyah Journal of Informatics and Electrical Engineering, vol. 07, no. 02, pp. 10–16, 2025.
[11] A. W. Ishlah, S. Sudarno, and P. Kartikasari, “Implementasi Gridsearchcv Pada Support Vector Regression (Svr) Untuk Peramalan Harga Saham,” Jurnal Gaussian, vol. 12, no. 2, pp. 276–286, 2023, doi: 10.14710/j.gauss.12.2.276-286.
[12] I. Muhamad Malik Matin, “Hyperparameter Tuning Menggunakan GridsearchCV pada Random Forest untuk Deteksi Malware,” Multinetics, vol. 9, no. 1, pp. 43–50, 2023, doi: 10.32722/multinetics.v9i1.5578.
[13] G. H. Saputra, A. H. Wigena, and B. Sartono, “Penggunaan Support Vector Regression Dalam Pemodelan Indeks Saham Syariah Indonesia Dengan Algoritme Grid Search,” Indonesian Journal of Statistics and Its Applications, vol. 3, no. 2, pp. 148–160, 2019, doi: 10.29244/ijsa.v3i2.172.
[14] Kementerian Pertanian Republik Indonesia, “Basis Data Statistik Pertanian Indonesia.” [Online]. Available: https://bdsp2.pertanian.go.id/bdsp/id/indikator
[15] Badan Pusat Statistik Kabupaten Manokwari, “Jumlah Curah Hujan (mm), 2022.” Accessed: Apr. 21, 2026. [Online]. Available: https://manokwarikab.bps.go.id/id/statistics-table/2/NDEjMg==/jumlah-curah-hujan.html
[16] D. I. P. Desy, A. F. Riza Kholdani, Tri Wahyu Qur’ana, and A. Dharmawati, “Pemodelan Spasial untuk Analisa Produksi Padi Integrasi Machine Learning,” Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, vol. 14, no. 2, pp. 128–137, 2023, doi: 10.31849/digitalzone.v14i2.16256.
[17] S. Rahmah, R. Suhendra, H. Maghfirah, Sanusi, and I. Raziah, “IMPLEMENTASI ALGORITMA SUPPORT VECTOR REGRESSION DALAM MEMPREDIKSI HASIL PRODUKSI TANAMAN PANGAN PADA KABUPATEN ACEH BARAT DAYA,” pp. 19–29, 2025.
[18] C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995, doi: 10.1023/A:1022627411411.
[19] F. Wahyu, D. Wicaksono, and A. B. Rahmat, “Optimasi Peramalan Tingkat Laju Inflasi Indonesia Melalui Pendekatan Ensemble Bagging Pada Algoritma Support Vector Regression,” vol. 12, no. 2, 2024.
[20] D. R. Anamisa, B. D. Satoto, M. K. Sophan, and M. Yusuf, “Mathematical Modelling of Engineering Problems Development of the Support Vector Regression and Genetic Algorithm Hybrid Models for Forecasting of Madura Rice Yield,” vol. 13, no. 1, pp. 181–198, 2026.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 J-INTECH
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
