A COMPARISON OF RANDOM FOREST AND DOUBLE RANDOM FOREST: DROPOUT RATES OF MADRASAH STUDENTS IN INDONESIA
Random forest algorithm allows for building better CART models. However, the disadvantage of this method is often underfitting, especially for small node sizes. Therefore, the double random forest method was developed to overcome this problem. The research was conducted by utilising Education Manage...
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Universitas Pattimura
2025-01-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/13346 |
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| author | Arie Purwanto Bagus Sartono Khairil Anwar Notodiputro |
| author_facet | Arie Purwanto Bagus Sartono Khairil Anwar Notodiputro |
| author_sort | Arie Purwanto |
| collection | DOAJ |
| description | Random forest algorithm allows for building better CART models. However, the disadvantage of this method is often underfitting, especially for small node sizes. Therefore, the double random forest method was developed to overcome this problem. The research was conducted by utilising Education Management Information System (EMIS) data, which is related to the incidence of school dropout. The data used consists of 2 data, namely MTs and MA dropout data. The initial testing procedure was carried out using the random forest algorithm for each data set, then the data was evaluated using the double random forest method. From this study, the underfitting case can be overcome well using the double random forest algorithm, while in the fit case, the difference in the goodness-of-fit value of the model is relatively the same. The results obtained show that MTs prioritise school quality more than MA, although family factors are more important at the MA level. Although the total number of factors used is basically the same, it should be noted that the two school levels have different relevance variables. It should be noted that no forecasting was done in this study given that the methodology used two different types of data. |
| format | Article |
| id | doaj-art-ee71b643d04f4524a55b4ba418218bb7 |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-ee71b643d04f4524a55b4ba418218bb72025-08-20T03:41:56ZengUniversitas PattimuraBarekeng1978-72272615-30172025-01-0119122723610.30598/barekengvol19iss1pp227-23613346A COMPARISON OF RANDOM FOREST AND DOUBLE RANDOM FOREST: DROPOUT RATES OF MADRASAH STUDENTS IN INDONESIAArie Purwanto0Bagus Sartono1Khairil Anwar Notodiputro2Mathematic Study Program, Faculty of Teacher Training and Education, Universitas Mercu Buana Yogyakarta, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, IndonesiaRandom forest algorithm allows for building better CART models. However, the disadvantage of this method is often underfitting, especially for small node sizes. Therefore, the double random forest method was developed to overcome this problem. The research was conducted by utilising Education Management Information System (EMIS) data, which is related to the incidence of school dropout. The data used consists of 2 data, namely MTs and MA dropout data. The initial testing procedure was carried out using the random forest algorithm for each data set, then the data was evaluated using the double random forest method. From this study, the underfitting case can be overcome well using the double random forest algorithm, while in the fit case, the difference in the goodness-of-fit value of the model is relatively the same. The results obtained show that MTs prioritise school quality more than MA, although family factors are more important at the MA level. Although the total number of factors used is basically the same, it should be noted that the two school levels have different relevance variables. It should be noted that no forecasting was done in this study given that the methodology used two different types of data.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/13346random forestdouble random forest |
| spellingShingle | Arie Purwanto Bagus Sartono Khairil Anwar Notodiputro A COMPARISON OF RANDOM FOREST AND DOUBLE RANDOM FOREST: DROPOUT RATES OF MADRASAH STUDENTS IN INDONESIA Barekeng random forest double random forest |
| title | A COMPARISON OF RANDOM FOREST AND DOUBLE RANDOM FOREST: DROPOUT RATES OF MADRASAH STUDENTS IN INDONESIA |
| title_full | A COMPARISON OF RANDOM FOREST AND DOUBLE RANDOM FOREST: DROPOUT RATES OF MADRASAH STUDENTS IN INDONESIA |
| title_fullStr | A COMPARISON OF RANDOM FOREST AND DOUBLE RANDOM FOREST: DROPOUT RATES OF MADRASAH STUDENTS IN INDONESIA |
| title_full_unstemmed | A COMPARISON OF RANDOM FOREST AND DOUBLE RANDOM FOREST: DROPOUT RATES OF MADRASAH STUDENTS IN INDONESIA |
| title_short | A COMPARISON OF RANDOM FOREST AND DOUBLE RANDOM FOREST: DROPOUT RATES OF MADRASAH STUDENTS IN INDONESIA |
| title_sort | comparison of random forest and double random forest dropout rates of madrasah students in indonesia |
| topic | random forest double random forest |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/13346 |
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