Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java


  • Khusnia Nurul Khikmah IPB University, Indonesia
  • Bagus Sartono IPB University, Indonesia
  • Budi Susetyo IPB University, Indonesia
  • Gerry Alfa Dito IPB University, Indonesia



Extremely randomized tree, Food insecurity, Gradient boosting, Random forest, Rotation forest


This study aims to compare the classification performance of the random forest, gradient boosting, rotation forest, and extremely randomized tree methods in classifying the food insecurity experience scale in West Java. The dataset used in this research is based on the Socio-Economic Survey by Statistics Indonesia in 2020. The novelty of this research is comparing the performance of the four methods used, which all are the tree ensemble approaches. In addition, due to the imbalance class problem, the authors also applied three imbalance handling techniques in this study. The results show that the combination of the random-forest algorithm and the random-under sampling technique is the best classifier. This approach has a balanced accuracy value of 65.795%. The best classification method results show that the food insecurity experience scale in West Java can be identified by considering the factors of floor area (house size), the number of depositors, type of floor, health insurance ownership status, and internet access capabilities.

Author Biographies

Khusnia Nurul Khikmah, IPB University

Department of Statistics

Bagus Sartono, IPB University

Department of Statistics

Budi Susetyo, IPB University

Department of Statistics

Gerry Alfa Dito, IPB University

Department of Statistics


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