Analysis of Food Security Index Predictions in Indonesia using Machine Learning Approach

Frederic Morado Saragih, Wahyu Catur Wibowo

Abstract

Food is one of the basic human needs that should be available at all times. To fulfill the role of in a region, the concept of food security is established to measure sufficiency, availability and quality of food. Food security for a country is expressed using Food Security Index (FSI). FSI score for a country reflects its ability for survival. It is therefore very important to measure the score and be able to predict future score to enable control and improvement. To realize the improvement of Indonesia's food security, a model is needed to predict the Food Security Index in Indonesia. This This paper explores the models using data from the Indonesian Food Security and Vulnerability Atlas (FSVA) at the Regency and City levels in 2018-2024 period with a total of 3,598 records. We evaluated Multiple Linear Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, eXtreme Gradient Boosting, Support Vector Regression, and Ensemble Machine Learning models for predicting the FSI score. The models are evaluated using r-squared (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results shows that the XGBoost method is the best method for predicting the Food Security Index in Indonesia with an R2 value of 0.912, RMSE of 0.053, and MAE of 0.037. Meanwhile, the ensemble machine learning method provides an R2 value of 0.79, RMSE of 0.083, and MAE of 0.063. In addition, the XGBoost method predicts the Food Security Index score in 2025 to be 75.56 and in 2026 to be 75.48.

Keywords

data mining; model evaluation; food security and vulnerability atlas

Full Text:

PDF

References

Ahadian, P., & Parand, K. (2022). Support vector regression for the temperature-stimulated drug release. Chaos, Solitons & Fractals, 165, 112871. https://doi.org/10.1016/j.chaos.2022.112871

Andaiyani, S., Marwa, T., & Nurhaliza, S. (2024). Ekonomi Biru dan Ketahanan Pangan: Studi Empiris Provinsi Kepulauan di Indonesia. Jurnal Ekonomi Indonesia, 13(1), 69–85. https://doi.org/10.52813/jei.v13i1.324

Bapanas. (2023). Peta Ketahanan dan Kerentanan Pangan Tahun 2023. https://fsva.badanpangan.go.id/?910c4c71bfed3c0a49daa1b18116a3c9

Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54, 1937–1967. https://doi.org/10.1007/s10462-020-09896-5

Bühler, D., Hartje, R., & Grote, U. (2018). Matching food security and malnutrition indicators: evidence from Southeast Asia. Agricultural Economics, 49(4), 481–495. https://doi.org/10.1111/agec.12430

Christakis, I., Sarri, E., Tsakiridis, O., & Stavrakas, I. (2024). Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors. Signals, 5(1), 60–86. https://doi.org/10.3390/signals5010004

De Sanctis, V., Soliman, A., Alaaraj, N., Ahmed, S., Alyafei, F., & Hamed, N. (2021). Early and Long-term Consequences of Nutritional Stunting: From Childhood to Adulthood. Acta Bio-Medica : Atenei Parmensis, 92(1), e2021168. https://doi.org/10.23750/abm.v92i1.11346

Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. (2020). A survey on ensemble learning. Frontiers of Computer Science, 14, 241–258. https://doi.org/10.1007/s11704-019-8208-z

Doz, D., Cotič, M., & Felda, D. (2023). Random Forest Regression in Predicting Students’ Achievements and Fuzzy Grades. Mathematics, 11(19). https://doi.org/10.3390/math11194129

Frisnoiry, S., Pratiwi, I. A., Tarigan, N. C. W., & Sulaiman, R. P. (2024). Konsep, Indeks, Pendekatan, dan Strategi Ketahanan Pangan. Esensi Pendidikan Inspiratif, 6(2).

Huang, Y., Xu, W., Sukjairungwattana, P., & Yu, Z. (2024). Learners’ continuance intention in multimodal language learning education: An innovative multiple linear regression model. Heliyon, 10(6). https://doi.org/10.1016/j.heliyon.2024.e28104

Li, Z. (2022). Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost. Computers, Environment and Urban Systems, 96, 101845. https://doi.org/10.1016/j.compenvurbsys.2022.101845

Maharadja, A. N., Maulana, I., & Dermawan, B. A. (2021). Penerapan Metode Regresi Linear Berganda untuk Prediksi Kerugian Negara Berdasarkan Kasus Tindak Pidana Korupsi. Journal of Applied Informatics and Computing, 5(1), 95–102. https://doi.org/10.30871/jaic.v5i1.3184

Manikas, I., Ali, B. M., & Sundarakani, B. (2023). A systematic literature review of indicators measuring food security. Agriculture & Food Security, 12(1), 10. https://doi.org/10.1186/s40066-023-00415-7

Martínez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernández-Orallo, J., Kull, M., Lachiche, N., Ramírez-Quintana, M. J., & Flach, P. (2019). CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories. IEEE Transactions on Knowledge and Data Engineering, 33(8), 3048–3061. https://doi.org/10.1109/TKDE.2019.2962680

Mienye, I. D., & Sun, Y. (2022). A survey of ensemble learning: Concepts, algorithms, applications, and prospects. IEEE Access, 10, 99129–99149. https://doi.org/10.1109/ACCESS.2022.3207287

Özen, F. (2024). Random forest regression for prediction of Covid-19 daily cases and deaths in Turkey. Heliyon, 10(4). https://doi.org/10.1016/j.heliyon.2024.e25746

Perum Bulog. (2014). Ketahanan pangan. Bulog. Retrieved from https://www.bulog.co.id/beraspangan/ketahanan-pangan/

Pristiandaru, D. L. (2023, May 7). Mengenal Tujuan 2 SDGs: Tanpa Kelaparan. Kompas. Retrieved from https://lestari.kompas.com/read/2023/05/07/080000986/mengenal-tujuan-2-sdgs-tanpa-kelaparan

Rincy, T. N., & Gupta, R. (2020). Ensemble learning techniques and its efficiency in machine learning: A survey. 2nd International Conference on Data, Engineering and Applications (IDEA), 1–6. https://doi.org/10.1109/IDEA49133.2020.9170675

Sabarella, Komalasari, W. B., Wahyuningsih, S., Sehusman, Manurung, M., Supriyati, Y., Rinawati, Saida, M. D. N., Seran, K., Arief, M., Firmansyah, R., & Amara, V. D. (2022). Statistik Ketahanan Pangan Tahun 2022. Pusat Data dan Sistem Informasi Pertanian Sekretariat Jenderal Kementerian Pertanian.

Saputra, G. H., Wigena, A. H., & Sartono, B. (2019). Penggunaan Support Vector Regression dalam Pemodelan Indeks Saham Syariah Indonesia dengan Algoritma Grid Search. Indonesian Journal of Statistics and Its Applications, 3(2), 148–160. https://doi.org/10.29244/ijsa.v3i2.172

Saragih, B. (2022). Dapur Tahan Pangan, Sehat dan Bergizi (Pengentasan Kerawanan Pangan dan Gizi di Indonesia). Yogyakarta: Penerbit Deepublish.

Saragih, F. M., & Saragih, B. (2020). Gambaran Kebiasaan Makan Masyarakat Pada Masa Pandemi Covid-19. ResearchGate.

Shopnil, M. M. M., Husna, A., Sultana, S., & Islam, M. N. (2023). An Ensemble ML Model to Predict the Wastage of Food: Towards Achieving the Food Sustainability. 2023 International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM), 1–6. https://doi.org/10.1109/NCIM59001.2023.10212669

Soekarwo. (2021, May 10). Sistem dan Upaya Memperkuat Ketahanan Pangan. Dewan Pertimbangan Presiden. Retrieved from https://wantimpres.go.id/id/2021/05/sistem-dan-upaya-memperkuat-ketahanan-pangan/

Suntoro, J. (2019). Data Mining: Algoritma dan Implementasi dengan Pemrograman PHP. Jakarta: Elex Media Komputindo.

Theobald, O. (2017). Machine Learning For Absolute Beginners (2nd ed.). Greater London, United Kingdom: Scatterplot Press.

United Nations. (2015). Goal 2 | Department of Economic and Social Affairs. United Nations. Retrieved from https://sdgs.un.org/goals/goal2

Utoro, P. A. R., Pujokaroni, A. S., Aini, Q., & Saragih, B. (2025). Factors and comparative analysis of COVID-19’s impact on household food security in rural and urban regions. Nutrición Clínica y Dietética Hospitalaria, 45(1). https://doi.org/10.12873/451utoro

Wijaya, H., Hostiadi, D. P., & Triandini, E. (2024). Optimization of XGBoost Algorithm Using Parameter Tunning in Retail Sales Prediction. Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, 13(3), 769–786. https://doi.org/10.23887/janapati.v13i3.82214

Yulianti, S. E. H., Soesanto, O., & Sukmawaty, Y. (2022). Penerapan Metode Extreme Gradient Boosting (XGBOOST) pada Klasifikasi Nasabah Kartu Kredit. Journal of Mathematics: Theory and Applications, 4(1), 21–26. https://doi.org/10.31605/jomta.v4i1.1792

Zhang, Y., Wang, Q., Chen, X., Yan, Y., Yang, R., Liu, Z., & Jiahong, F. (2022). The Prediction of Spark-Ignition Engine Performance and Emissions Based on the SVR Algorithm. Processes, 10, 312. https://doi.org/10.3390/pr10020312

DOI: https://doi.org/10.37637/ab.v8i2.2302

Refbacks

  • There are currently no refbacks.