Integrating Water Indicators In A Data-Driven Artificial Intelligence Model For Food Security Classification
DOI:
https://doi.org/10.55314/tsg.v4i5.607Keywords:
Klasifikasi Ketahanan Pangan, Kecerdasan Buatan, Indikator Air, Algoritma XGBoost, Kebijakan BerkelanjutanAbstract
Penelitian ini menjelaskan integrasi indikator air dalam pengklasifikasian ketahanan pangan provinsi di Indonesia. Pentingnya topik ini terkait dengan hubungan yang signifikan antara air, pangan, dan energi dalam mencapai ketahanan pangan berkelanjutan. Penggunaan kecerdasan buatan, khususnya dalam bentuk algoritma XGBoost, merupakan langkah cerdas dalam mengolah data dan melakukan pengklasifikasian ketahanan pangan. Data indikator air dan cut-off point Indeks Ketahanan Pangan digunakan dalam mengembangkan model XGBoost yang bertujuan mengklasifikasikan provinsi-provinsi sebagai high food vulnerability atau "high food security. Metode penelitian melibatkan pengumpulan data, preprocessing data, serta penerapan algoritma XGBoost dengan tuning parameter. Hasil penelitian menunjukkan bahwa model yang dikembangkan memiliki akurasi sebesar 91%, dengan variabel proporsi rumah tangga yang memiliki akses terhadap sumber air minum yang aman (X1) sebagai faktor paling berpengaruh dalam pengklasifikasian ketahanan pangan. Penelitian ini bukan hanya memberikan wawasan penting terkait ketahanan pangan provinsi di Indonesia, tetapi juga menunjukkan potensi besar kecerdasan buatan dalam mengatasi permasalahan kompleks seperti ketahanan pangan. Dengan hasil yang diperoleh, dapat diperkuat argumen pentingnya penerapan teknologi kecerdasan buatan dalam mendukung kebijakan dan tindakan nyata dalam upaya mencapai ketahanan pangan yang lebih baik dan berkelanjutan.
References
Al Azies, H., & Herowati, W. (2023). Unravelling Income Inequality in Indonesia. Jurnal Riset Ilmu Ekonomi, 3(2), 89–100. https://doi.org/10.23969/JRIE.V3I2.63
Anwar, N. (2022). Indonesia’s Regional Food Security In Light Of The Impending Global Food Crisis. Trikonomika, 21(2), 101–110. https://doi.org/10.23969/TRIKONOMIKA.V21I2.7113
Arifin, M., Widowati, W., & Farikhin, F. (2023). Optimization of Hyperparameters in Machine Learning for Enhancing Predictions of Student Academic Performance. Ingenierie Des Systemes d’Information, 28(3), 575–582. https://doi.org/10.18280/ISI.280305
Bernadó-Mansilla, E., & Garrell-Guiu, J. M. (2003). Accuracy-Based Learning Classifier Systems:
Models, Analysis and Applications to Classification Tasks. Evolutionary Computation, 11(3), 209–238. https://doi.org/10.1162/106365603322365289
Budholiya, K., Shrivastava, S. K., & Sharma, V. (2022). An optimized XGBoost based diagnostic system for effective prediction of heart disease. Journal of King Saud University - Computer and Information Sciences, 34(7), 4514–4523. https://doi.org/10.1016/J.JKSUCI.2020.10.013
Çakmakçı, R., Salık, M. A., & Çakmakçı, S. (2023). Assessment and Principles of Environmentally
Sustainable Food and Agriculture Systems. Agriculture 2023, Vol. 13, Page 1073, 13(5), 1073. https://doi.org/10.3390/AGRICULTURE13051073
Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 1–13. https://doi.org/10.1186/S12864-019-6413-7/TABLES/5
Dhaliwal, S. S., Nahid, A. Al, & Abbas, R. (2018). Effective Intrusion Detection System Using XGBoost. Information 2018, Vol. 9, Page 149, 9(7), 149. https://doi.org/10.3390/INFO9070149
Dharmawan, H., Sartono, B., Kurnia, A., Hadi, A. F., & Ramadhani, E. (2022). A study of machine learning algorithms to measure the feature importance in class-imbalance data of food insecurity cases in Indonesia. Commun. Math. Biol. Neurosci., 2022(0), Article ID 101. https://doi.org/10.28919/CMBN/7636
García, S., Luengo, J., & Herrera, F. (2015). Data preparation basic models. Intelligent Systems Reference Library, 72, 39–57. https://doi.org/10.1007/978-3-319-10247-4_3
Gil, J. D. B., Reidsma, P., Giller, K., Todman, L., Whitmore, A., & van Ittersum, M. (2019). Sustainable development goal 2: Improved targets and indicators for agriculture and food security. Ambio,
(7), 685–698. https://doi.org/10.1007/S13280-018-1101-4/TABLES/3
Gupta, A., Sharma, S., Goyal, S., & Rashid, M. (2020). Novel XGBoost Tuned Machine Learning Model for Software Bug Prediction. Proceedings of International Conference on Intelligent Engineering and Management, ICIEM 2020, 376–380. https://doi.org/10.1109/ICIEM48762.2020.9160152
Gupta, D. L., Malviya, A. K., & Singh, S. (2012). Performance Analysis of Classification Tree Learning Algorithms. International Journal of Computer Applications, 55(6), 975–8887.
Ibrahem Ahmed Osman, A., Najah Ahmed, A., Chow, M. F., Feng Huang, Y., & El-Shafie, A. (2021). Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia. Ain Shams Engineering Journal, 12(2), 1545–1556. https://doi.org/10.1016/J.ASEJ.2020.11.011
Kavzoglu, T., & Teke, A. (2022). Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost). Arabian Journal for Science and Engineering, 47(6), 7367–7385. https://doi.org/10.1007/S13369-022-06560-8/FIGURES/12
Liu, J., Mooney, H., Hull, V., Davis, S. J., Gaskell, J., Hertel, T., Lubchenco, J., Seto, K. C., Gleick, P., Kremen, C., & Li, S. (2015). Systems integration for global sustainability. Science, 347(6225). https://doi.org/10.1126/SCIENCE.1258832/ASSET/F0BA4106-BF5F-4AE3-9D77-2E70C1A5C51F/ASSETS/GRAPHIC/347_1258832_FA.JPEG
Luengo, J., García-Gil, D., Ramírez-Gallego, S., García, S., & Herrera, F. (2020). Big Data Preprocessing: Enabling Smart Data. Big Data Preprocessing: Enabling Smart Data, 1–186. https://doi.org/10.1007/978-3-030-39105-8/COVER
Muraina, I. O. (2022). Ideal Dataset Splitting Ratios In Machine Learning Algorithms: General Concerns For Data Scientists And Data Analysts. https://www.researchgate.net/publication/358284895
Nguyen, Q. H., Ly, H. B., Ho, L. S., Al-Ansari, N., Van Le, H., Tran, V. Q., Prakash, I., & Pham, B. T. (2021). Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/4832864
Obiora, C. N., Ali, A., & Hasan, A. N. (2021). Implementing extreme gradient boosting (xgboost) algorithm in predicting solar irradiance. 2021 IEEE PES/IAS PowerAfrica, PowerAfrica 2021. https://doi.org/10.1109/POWERAFRICA52236.2021.9543159
Pahl-Wostl, C. (2019). Governance of the water-energy-food security nexus: A multi-level coordination challenge. Environmental Science & Policy, 92, 356–367. https://doi.org/10.1016/J.ENVSCI.2017.07.017
Pérez-Escamilla, R. (2017). Food Security and the 2015–2030 Sustainable Development Goals: From Human to Planetary Health: Perspectives and Opinions. Current Developments in Nutrition, 1(7). https://doi.org/10.3945/CDN.117.000513
Prayitno, G., Safitri, N., Subagiyo, A., & Riska, B. (2019). Food security index and livelihood assets of Pandaan District, Pasuruan Regency, Indonesia. IOP Conference Series: Earth and Environmental Science, 361(1), 012029. https://doi.org/10.1088/1755-1315/361/1/012029
Rahayu, R. S., Purwaningsih, Y., & Daerobi, A. (2019). Mapping Of Provincial Food Security In Indonesia Using Based Clustering Model. Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi Dan Pembangunan, 20(1), 69–79. https://doi.org/10.23917/JEP.V20I1.7096
Rasul, G. (2021). A Framework for Addressing the Twin Challenges of COVID-19 and Climate Change for Sustainable Agriculture and Food Security in South Asia. Frontiers in Sustainable Food Systems, 5, 679037. https://doi.org/10.3389/FSUFS.2021.679037/BIBTEX
Smajgl, A., Ward, J., & Pluschke, L. (2016). The water–food–energy Nexus – Realising a new paradigm. Journal of Hydrology, 533, 533–540. https://doi.org/10.1016/J.JHYDROL.2015.12.033
Sofaer, H. R., Hoeting, J. A., & Jarnevich, C. S. (2019). The area under the precision-recall curve as a performance metric for rare binary events. Methods in Ecology and Evolution, 10(4), 565–577. https://doi.org/10.1111/2041-210X.13140
Teja, K. U. V. R., Reddy, B. P. V., Likith Preetham, A., Patil, H. Y., & Poorna Chandra, T. (2021).
Prediction of Diabetes at Early Stage with Supplementary Polynomial Features. Proceedings - 1st International Conference on Smart Technologies Communication and Robotics, STCR 2021.
https://doi.org/10.1109/STCR51658.2021.9588849
Widada, A. W., Masyhuri, M., & Mulyo, J. H. (2017). Determinant Factors of Food Security in Indonesia. Agro Ekonomi, 28(2), 205–219. https://doi.org/10.22146/JAE.26245
Yacouby, R., & Axman, D. (2020). Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models. 79–91. https://doi.org/10.18653/V1/2020.EVAL4NLP-1.9
Published
Issue
Section
Copyright (c) 2023 TheJournalish: Social and Government
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Thejournalish: Social and Government This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
