A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools
The rapid advancement of information technology has transformed education by providing tools to accurately predict students' academic performance. This study aims to develop a system for predicting academic achievement using the Random Forest algorithm, with a case study at SMAN 1 Aceh Barat Da...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Batam
2025-06-01
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| Series: | Journal of Applied Informatics and Computing |
| Subjects: | |
| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9460 |
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| author | Rifa Andriani Saputri Asrianda Asrianda Lidya Rosnita |
| author_facet | Rifa Andriani Saputri Asrianda Asrianda Lidya Rosnita |
| author_sort | Rifa Andriani Saputri |
| collection | DOAJ |
| description | The rapid advancement of information technology has transformed education by providing tools to accurately predict students' academic performance. This study aims to develop a system for predicting academic achievement using the Random Forest algorithm, with a case study at SMAN 1 Aceh Barat Daya and SMAN 3 Aceh Barat Daya. Data from 632 student report cards for grades X and XI in the second semester of the 2023/2024 academic year were used, covering subjects such as Mathematics, Indonesian Language, and others, divided into 80% training data (506 samples) and 20% test data (136 samples). The research methodology involved data preprocessing, training the Random Forest model using entropy and information gain to construct decision trees, and performance evaluation using metrics such as accuracy, precision, and recall. The implementation resulted in a web-based application using Python and Flask, featuring an interactive interface and decision tree visualization. Testing on 136 test samples achieved an accuracy of 87.40%, with 111 correct predictions, 16 false positives, and 0 false negatives, demonstrating the model's reliability in identifying high-achieving students without missing potential. This research is expected to assist schools in identifying outstanding students, making data-driven decisions, and designing more effective educational strategies. |
| format | Article |
| id | doaj-art-abcbece5d4d54830bcb623a635f5c996 |
| institution | DOAJ |
| issn | 2548-6861 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-abcbece5d4d54830bcb623a635f5c9962025-08-20T03:09:13ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612025-06-01931042104910.30871/jaic.v9i3.94607005A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High SchoolsRifa Andriani Saputri0Asrianda Asrianda1Lidya Rosnita2Teknik Informatika, Universitas MalikussalehTeknik Informatika, Universitas MalikussalehTeknik Informatika, Universitas MalikussalehThe rapid advancement of information technology has transformed education by providing tools to accurately predict students' academic performance. This study aims to develop a system for predicting academic achievement using the Random Forest algorithm, with a case study at SMAN 1 Aceh Barat Daya and SMAN 3 Aceh Barat Daya. Data from 632 student report cards for grades X and XI in the second semester of the 2023/2024 academic year were used, covering subjects such as Mathematics, Indonesian Language, and others, divided into 80% training data (506 samples) and 20% test data (136 samples). The research methodology involved data preprocessing, training the Random Forest model using entropy and information gain to construct decision trees, and performance evaluation using metrics such as accuracy, precision, and recall. The implementation resulted in a web-based application using Python and Flask, featuring an interactive interface and decision tree visualization. Testing on 136 test samples achieved an accuracy of 87.40%, with 111 correct predictions, 16 false positives, and 0 false negatives, demonstrating the model's reliability in identifying high-achieving students without missing potential. This research is expected to assist schools in identifying outstanding students, making data-driven decisions, and designing more effective educational strategies.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9460achievement accuracy, entropy , education, prediction. |
| spellingShingle | Rifa Andriani Saputri Asrianda Asrianda Lidya Rosnita A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools Journal of Applied Informatics and Computing achievement accuracy, entropy , education, prediction. |
| title | A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools |
| title_full | A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools |
| title_fullStr | A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools |
| title_full_unstemmed | A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools |
| title_short | A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools |
| title_sort | random forest based predictive model for student academic performance a case study in indonesian public high schools |
| topic | achievement accuracy, entropy , education, prediction. |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9460 |
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