Prediction of Rice Harvesting During the Rainy Season in Kabupaten Lamongan Using Stochastic Frontier Analysis

Imelda Widya Ningrum; Dwi Arman Prasetya; Trimono Trimono; Anuar bin Mohamed Kassim

doi:10.59395/ijadis.v6i2.1393

Authors

Imelda Widya Ningrum Department of Data Science, University of Pembangunan Nasional Veteran Jawa Timur, Indonesia
Dwi Arman Prasetya Department Master of Information Technology, University of Pembangunan Nasional Veteran Jawa Timur, Indonesia
Trimono Department of Data Science, University of Pembangunan Nasional Veteran Jawa Timur, Indonesia
Anuar bin Mohamed Kassim Faculty of Electrical Engineering Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia

DOI:

https://doi.org/10.59395/ijadis.v6i2.1393

Keywords:

Stochastic Frontier Analysis , Technical Efficiency , Rice Production, prediction, machine learning, classification

Abstract

The agricultural sector plays a critical role in ensuring national food security, yet it faces challenges in achieving technical efficiency due to limited land and input resources. This study aims to model and predict the technical efficiency of rice production in Lamongan Regency during the rainy season using a data science-driven Stochastic Frontier Analysis (SFA) approach. The dataset includes key inputs such as land area, labor, fertilizer, and environmental variables. The methodology involved data preprocessing, feature selection based on Pearson correlation and VIF thresholds, and model validation using metrics like R-squared, MAPE, and log-likelihood. The SFA model demonstrated high predictive capability, with R² values exceeding 0.91 in cross-validation and MAPE under 15%. The low gamma value (? = 0.0100) indicates minimal yet consistent inefficiency. The results suggest that integrating SFA with data science techniques provides an effective framework for identifying inefficiencies and can serve as a decision-support system for evidence-based agricultural policy.

1067 755

Downloads

Download data is not yet available.

References

[1] T. Trimono, S. A. Putri, dan N. Selayanti, “Analisis Perbandingan Tingkat Kemiskinan di Jawa Timur Selama dan Sesudah Krisis Pangan,” Prosiding Seminar Nasional Sains Data, vol. 3, no. 1, pp. 235–240, 2023. DOI: https://doi.org/10.33005/senada.v3i1.118

[2] D. A. Prasetya, A. Sanusi, G. Chandrarin, E. Roikhah, I. Mujahidin, and R. Arifuddin, “Small and Medium Enterprises Problem and Potential Solutions for Waste Management,” J. Southwest Jiaotong Univ., vol. 54, no. 6, 2019. DOI: https://doi.org/10.35741/issn.0258-2724.54.6.21

[3] D. A. Prasetya, A. P. Sari, M. Idhom, and A. Lisanthoni, “Optimizing Clustering Analysis to Identify High-Potential Markets for Indonesian Tuber Exports,” Indones. J. Electron., Electromed. Eng. Med. Inform., vol. 7, no. 1, pp. 113–122, 2025.

[4] A. Lisanthoni and D. A. Prasetya, “Optimizing Clustering Analysis to Identify High-Potential Markets for Indonesian Tuber Exports,” Indones. J. Electron., Electromed. Eng. Med. Inform., vol. 7, no. 1, pp. 113–122, 2025. DOI: https://doi.org/10.35882/skzqbd57

[5] R. A. Siagian and W. Soetjipto, “Cost Efficiency of Rice Farming in Indonesia: Stochastic Frontier Approach,” Agricultural Socio-Economics Journal, vol. 20, no. 1, pp. 7–14, 2020. DOI: https://doi.org/10.21776/ub.agrise.2020.020.1.2

[6] A. Muhaimin, “Implementasi MySQL untuk pengelolaan data besar dalam sistem informasi pertanian,” J. Teknol. Inf. dan Komput., vol. 7, no. 3, pp. 115–121, 2021.

[7] P. A. Riyantoko, “Optimizing User-PC Computing System with Multicore CPU Utilization through Parallel Computing Jobs Distributions,” in Proc. Semin. Nas. Teknol. Komput. dan Informatika (SENATIK), vol. 1, no. 1, pp. 101–106, 2023.

[8] N. Birudaraju, A. Rao, and S. V., “Architecture for Analyzing Agriculture Data Using Data Analytics,” in Big Data for Improved Health Outcomes, IGI Global, 2021, pp. 111–122, doi: 10.4018/978-1-7998-2566-1.ch006. DOI: https://doi.org/10.4018/978-1-7998-2566-1.ch006

[9] J. Singvejsakul, C. Intapan, C. Chaiboonsri, dan R. Permsiri, “Bayesian Stochastic Frontier Analysis of Agricultural Productivity Efficiency in CLMV,” Journal of Physics: Conference Series, vol. 1936, 2021, doi: 10.1088/1742-6596/1936/1/012006. DOI: https://doi.org/10.1088/1742-6596/1936/1/012006

[10] K. M. Hindrayani, “Penerapan Dashboard System (Pendapatan Asli Daerah (PAD) Sektor Pariwisata pada Kabupaten Mojokerto Menggunakan Tableau,” J. Comput. Sci. Technol. (JCS-TECH), vol. 4, no. 1, pp. 14–18, 2024. DOI: https://doi.org/10.54840/jcstech.v4i1.189

[11] D. A. Prasetya and E. F. Armay, “Resolving the Shortest Path Problem using the Haversine Algorithm,” J. Ilm. Teknol. dan Rekayasa, vol. 26, no. 1, pp. 73–80, 2021.

[12] A. Fakhri, “Penerapan Metode Variance Inflation Factor (VIF) untuk Mengatasi Multikolinearitas dalam Model Regresi,” J. Stat. dan Apl., vol. 4, no. 1, pp. 15–22, 2020.

[13] M. Awangga and S. Adinugroho, “Evaluasi Kinerja Model Prediksi Harga Saham dengan Proporsi Data Latih dan Uji yang Berbeda,” J. Ilmu Komput. dan Agri-Informatika, vol. 5, no. 1, pp. 37–44, 2024.

[14] S. Fitriani and A. Kurniawan, “Estimasi Parameter Model Regresi Logistik dengan Metode Maximum Likelihood Estimation (MLE),” J. Mat. dan Aplikasinya, vol. 7, no. 1, pp. 45–52, 2021.

[15] R. Purnamasari and B. Kusuma, “Analisis Efisiensi Teknis Usahatani Padi Sawah di Kabupaten Subang Menggunakan Metode SFA,” J. Apl. Sist. Inf. dan Komput. (JASIEK), vol. 3, no. 2, pp. 112–121, 2022.

[16] P. Damaliana, “Evaluasi model regresi menggunakan R-squared dan log-likelihood dalam analisis efisiensi produksi,” J. Ekon. Pertan., vol. 1, pp. 10–18, 2020.

[17] A. Pramono, T. J. L. Tama, and T. Waluyo, “Analisis Arus Tiga Fasa Daya 197 KVA dengan Menggunakan Metode Uji Normalitas Kolmogorov-Smirnov,” J. RESISTOR (Rek. Sist. Komput.), vol. 4, no. 2, 2021. DOI: https://doi.org/10.31598/jurnalresistor.v4i2.696

[18] M. Kroshko, K. Morin, dan R. Sahgal, "A knowledge-based planning algorithm for lung SBRT with dynamic conformal arc therapy using Bayesian stochastic frontier regression," Journal of Applied Clinical Medical Physics, vol. 23, no. 10, pp. e13766, 2022 DOI: https://doi.org/10.1002/mp.15922

[19] R. Rauf, K. Ayinde, dan A. Ogundipe, "Enhanced methods for multicollinearity mitigation in stochastic frontier analysis using principal component analysis (PCA-SFA)," Heliyon, vol. 10, no. 1, 2024. DOI: https://doi.org/10.46481/jnsps.2024.2091

[20] V. Glinskiy et al., “Modifications to the Jarque–Bera Test,” Mathematics, vol. 12, 2024. DOI: https://doi.org/10.3390/math12162523

[21] M. Awangga and S. Adinugroho, “Evaluasi Kinerja Model Prediksi Harga Saham dengan Proporsi Data Latih dan Uji yang Berbeda,” J. Ilmu Komput. dan Agri-Informatika, vol. 5, no. 1, pp. 37–44, 2024

[22] R. Zulkarnain, A. Djuraidah, dan I. Sumertajaya, “Spatial autoregressive stochastic frontier model with application to Indonesia’s aquaculture,” Journal of Physics: Conference Series, vol. 1863, 2021. DOI: https://doi.org/10.1088/1742-6596/1863/1/012044

[23] H. Afrifa, A. Tauringana, and B. Tingbani, “Working capital management and technical efficiency: A stochastic frontier analysis approach,” Journal of Business flowchart, vol. 149, pp. 37–48, 2022.

[24] D. S. Nugroho and E. Prasetyo, “Analisis Efisiensi Teknis Produksi Jagung di Provinsi Jawa Tengah dengan Pendekatan Stochastic Frontier Analysis,” J. Ekon. Pertan. dan Agribisnis, vol. 6, no. 2, pp. 101–112, 2022.

[25] I. G. Sedana and N. M. Yuliarmi, “Pengaruh Faktor-Faktor Produksi Terhadap Efisiensi Teknis Usahatani Cabai Merah di Bali: Aplikasi Stochastic Frontier Analysis,” J. Agribisnis dan Agrowisata, vol. 9, no. 1, pp. 1–10, 2020.