Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java
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 Stati...
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Department of Informatics, UIN Sunan Gunung Djati Bandung
2024-06-01
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| Series: | JOIN: Jurnal Online Informatika |
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| Online Access: | https://join.if.uinsgd.ac.id/index.php/join/article/view/1012 |
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| author | Khusnia Nurul Khikmah Bagus Sartono Budi Susetyo Gerry Alfa Dito |
| author_facet | Khusnia Nurul Khikmah Bagus Sartono Budi Susetyo Gerry Alfa Dito |
| author_sort | Khusnia Nurul Khikmah |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2c8328f1c8ae4abeb2058d5a57e04d8c |
| institution | OA Journals |
| issn | 2528-1682 2527-9165 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Department of Informatics, UIN Sunan Gunung Djati Bandung |
| record_format | Article |
| series | JOIN: Jurnal Online Informatika |
| spelling | doaj-art-2c8328f1c8ae4abeb2058d5a57e04d8c2025-08-20T02:06:06ZengDepartment of Informatics, UIN Sunan Gunung Djati BandungJOIN: Jurnal Online Informatika2528-16822527-91652024-06-019112813710.15575/join.v9i1.1012829Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West JavaKhusnia Nurul Khikmah0Bagus Sartono1Budi Susetyo2Gerry Alfa Dito3IPB UniversityIPB UniversityIPB UniversityIPB UniversityThis 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.https://join.if.uinsgd.ac.id/index.php/join/article/view/1012extremely randomized treefood insecuritygradient boostingrandom forestrotation forest |
| spellingShingle | Khusnia Nurul Khikmah Bagus Sartono Budi Susetyo Gerry Alfa Dito Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java JOIN: Jurnal Online Informatika extremely randomized tree food insecurity gradient boosting random forest rotation forest |
| title | Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java |
| title_full | Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java |
| title_fullStr | Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java |
| title_full_unstemmed | Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java |
| title_short | Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java |
| title_sort | performance comparative study of machine learning classification algorithms for food insecurity experience by households in west java |
| topic | extremely randomized tree food insecurity gradient boosting random forest rotation forest |
| url | https://join.if.uinsgd.ac.id/index.php/join/article/view/1012 |
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