Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West Java
Random Forest is an ensemble learning algorithm that combines multiple decision trees to generate a more stable and accurate classification model. This study aims to optimize Random Forest parameters for classifying school-age students' eligibility for the Kartu Indonesia Pintar (KIP) in West J...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Department of Mathematics, Universitas Negeri Gorontalo
2025-02-01
|
| Series: | Jambura Journal of Mathematics |
| Subjects: | |
| Online Access: | https://ejurnal.ung.ac.id/index.php/jjom/article/view/28736 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850077374824054784 |
|---|---|
| author | Silfiana Lis Setyowati Asyifah Qalbi Rafika Aristawidya Bagus Sartono Aulia Rizki Firdawanti |
| author_facet | Silfiana Lis Setyowati Asyifah Qalbi Rafika Aristawidya Bagus Sartono Aulia Rizki Firdawanti |
| author_sort | Silfiana Lis Setyowati |
| collection | DOAJ |
| description | Random Forest is an ensemble learning algorithm that combines multiple decision trees to generate a more stable and accurate classification model. This study aims to optimize Random Forest parameters for classifying school-age students' eligibility for the Kartu Indonesia Pintar (KIP) in West Java, based on economic factors. The research uses secondary data from the 2023 National Socio-Economic Survey (SUSENAS) of West Java, with a sample size of 13,044 individuals. To address class imbalance, Synthetic Minority Oversampling Technique (SMOTE) is applied. Hyperparameter tuning through grid search identifies the optimal combination of parameters, including the number of trees (ntree), random variables per split (mtry), and terminal node size (node_size). Model performance is evaluated using balanced accuracy, sensitivity, and specificity. Results indicate that the optimal parameters (mtry = 5, ntree = 674, node_size = 26) yield a balanced accuracy of 65.47%. Significant variables include PKH status, floor area of the house, source of drinking water, and building material type. The model accurately identifies students in need of educational assistance. In conclusion, optimizing Random Forest parameters improves the accuracy of KIP eligibility classification, supporting educational equity policies in West Java. These findings provide a foundation for developing more effective beneficiary selection systems for educational aid. |
| format | Article |
| id | doaj-art-d6bbf8c8c3904894852ae7d88ffe9bf5 |
| institution | DOAJ |
| issn | 2654-5616 2656-1344 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Department of Mathematics, Universitas Negeri Gorontalo |
| record_format | Article |
| series | Jambura Journal of Mathematics |
| spelling | doaj-art-d6bbf8c8c3904894852ae7d88ffe9bf52025-08-20T02:45:49ZengDepartment of Mathematics, Universitas Negeri GorontaloJambura Journal of Mathematics2654-56162656-13442025-02-0171404810.37905/jjom.v7i1.287369097Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West JavaSilfiana Lis Setyowati0Asyifah Qalbi1Rafika Aristawidya2Bagus Sartono3Aulia Rizki Firdawanti4IPB University, Ministry of Higher Education, Science, and TechnologyIPB UniversityIPB UniversityIPB UniversityIPB UniversityRandom Forest is an ensemble learning algorithm that combines multiple decision trees to generate a more stable and accurate classification model. This study aims to optimize Random Forest parameters for classifying school-age students' eligibility for the Kartu Indonesia Pintar (KIP) in West Java, based on economic factors. The research uses secondary data from the 2023 National Socio-Economic Survey (SUSENAS) of West Java, with a sample size of 13,044 individuals. To address class imbalance, Synthetic Minority Oversampling Technique (SMOTE) is applied. Hyperparameter tuning through grid search identifies the optimal combination of parameters, including the number of trees (ntree), random variables per split (mtry), and terminal node size (node_size). Model performance is evaluated using balanced accuracy, sensitivity, and specificity. Results indicate that the optimal parameters (mtry = 5, ntree = 674, node_size = 26) yield a balanced accuracy of 65.47%. Significant variables include PKH status, floor area of the house, source of drinking water, and building material type. The model accurately identifies students in need of educational assistance. In conclusion, optimizing Random Forest parameters improves the accuracy of KIP eligibility classification, supporting educational equity policies in West Java. These findings provide a foundation for developing more effective beneficiary selection systems for educational aid.https://ejurnal.ung.ac.id/index.php/jjom/article/view/28736kartu indonesia pintar (kip)random forestsmoteoptimal parameterhyperparameter tuning |
| spellingShingle | Silfiana Lis Setyowati Asyifah Qalbi Rafika Aristawidya Bagus Sartono Aulia Rizki Firdawanti Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West Java Jambura Journal of Mathematics kartu indonesia pintar (kip) random forest smote optimal parameter hyperparameter tuning |
| title | Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West Java |
| title_full | Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West Java |
| title_fullStr | Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West Java |
| title_full_unstemmed | Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West Java |
| title_short | Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West Java |
| title_sort | optimizing random forest parameters with hyperparameter tuning for classifying school age kip eligibility in west java |
| topic | kartu indonesia pintar (kip) random forest smote optimal parameter hyperparameter tuning |
| url | https://ejurnal.ung.ac.id/index.php/jjom/article/view/28736 |
| work_keys_str_mv | AT silfianalissetyowati optimizingrandomforestparameterswithhyperparametertuningforclassifyingschoolagekipeligibilityinwestjava AT asyifahqalbi optimizingrandomforestparameterswithhyperparametertuningforclassifyingschoolagekipeligibilityinwestjava AT rafikaaristawidya optimizingrandomforestparameterswithhyperparametertuningforclassifyingschoolagekipeligibilityinwestjava AT bagussartono optimizingrandomforestparameterswithhyperparametertuningforclassifyingschoolagekipeligibilityinwestjava AT auliarizkifirdawanti optimizingrandomforestparameterswithhyperparametertuningforclassifyingschoolagekipeligibilityinwestjava |