IMPROVING CLUSTER ACCURACY IN TUITION FEES: A MULTILAYER PERCEPTRON NEURAL NETWORK AND RANDOM FOREST APPROACH
Manual classification of Single Tuition Fees (STF) has a high risk of misclassification due to the need for a more in-depth assessment of students' economic criteria. This research uses Artificial Neural Networks (ANN), specifically the Multilayer Perceptron (NN-MLP) model, to detect and correc...
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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/15112 |
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| author | Sumin Sumin Prihantono Prihantono Khairawati Khairawati |
| author_facet | Sumin Sumin Prihantono Prihantono Khairawati Khairawati |
| author_sort | Sumin Sumin |
| collection | DOAJ |
| description | Manual classification of Single Tuition Fees (STF) has a high risk of misclassification due to the need for a more in-depth assessment of students' economic criteria. This research uses Artificial Neural Networks (ANN), specifically the Multilayer Perceptron (NN-MLP) model, to detect and correct errors in Single Tuition Fee (STF) classification. This study aims to apply the NN model to identify and correct classification errors in the STF clustering of State Islamic Religious Universities in Indonesia (PTKIN). This research was conducted using exploratory methods and quantitative approaches involving a population of PTKIN students throughout Indonesia. A sample of 282 respondents was selected using a simple random sampling method. The results showed that NN-MLP is an effective tool for identifying and correcting misclassification in determining PTKIN tuition fees with significantly improved classification accuracy characterized by an accuracy value of 71.28% and MSE of 0.287; this model can be used as a basis for developing information systems that are fairer and more accurate in managing tuition fees in higher education. This research also proves that the NN method is superior to traditional statistical methods and simple machine learning in handling complex and diverse data. In addition, the Random Forest model successfully identified the most influential input variables in STF classification. Father's occupation, mother's occupation, number of dependents, and utility bills such as water and electricity significantly contributed to the STF classification. In contrast, variables such as vehicle facilities showed a lower contribution. |
| format | Article |
| id | doaj-art-df9c2ed4e5ec4fc99846da9ebddc36af |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitas Pattimura |
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| series | Barekeng |
| spelling | doaj-art-df9c2ed4e5ec4fc99846da9ebddc36af2025-08-20T03:41:56ZengUniversitas PattimuraBarekeng1978-72272615-30172025-01-0119166567410.30598/barekengvol19iss1pp665-67415112IMPROVING CLUSTER ACCURACY IN TUITION FEES: A MULTILAYER PERCEPTRON NEURAL NETWORK AND RANDOM FOREST APPROACHSumin Sumin0Prihantono Prihantono1Khairawati Khairawati2Mathematics Tadris Study Program, Faculty of Tarbiyah and Teacher Training, Institut Agama Islam Negeri Pontianak, IndonesiaMaster of Sharia Economics Study Program, Faculty of Tarbiyah and Teacher Training, Institut Agama Islam Negeri Pontianak, IndonesiaIslamic Religious Education Study Program, Faculty of Tarbiyah and Teacher Training, Institut Agama Islam Negeri Pontianak, IndonesiaManual classification of Single Tuition Fees (STF) has a high risk of misclassification due to the need for a more in-depth assessment of students' economic criteria. This research uses Artificial Neural Networks (ANN), specifically the Multilayer Perceptron (NN-MLP) model, to detect and correct errors in Single Tuition Fee (STF) classification. This study aims to apply the NN model to identify and correct classification errors in the STF clustering of State Islamic Religious Universities in Indonesia (PTKIN). This research was conducted using exploratory methods and quantitative approaches involving a population of PTKIN students throughout Indonesia. A sample of 282 respondents was selected using a simple random sampling method. The results showed that NN-MLP is an effective tool for identifying and correcting misclassification in determining PTKIN tuition fees with significantly improved classification accuracy characterized by an accuracy value of 71.28% and MSE of 0.287; this model can be used as a basis for developing information systems that are fairer and more accurate in managing tuition fees in higher education. This research also proves that the NN method is superior to traditional statistical methods and simple machine learning in handling complex and diverse data. In addition, the Random Forest model successfully identified the most influential input variables in STF classification. Father's occupation, mother's occupation, number of dependents, and utility bills such as water and electricity significantly contributed to the STF classification. In contrast, variables such as vehicle facilities showed a lower contribution.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/15112artificial neural networkssingle tuition feeclassificationrandom forest |
| spellingShingle | Sumin Sumin Prihantono Prihantono Khairawati Khairawati IMPROVING CLUSTER ACCURACY IN TUITION FEES: A MULTILAYER PERCEPTRON NEURAL NETWORK AND RANDOM FOREST APPROACH Barekeng artificial neural networks single tuition fee classification random forest |
| title | IMPROVING CLUSTER ACCURACY IN TUITION FEES: A MULTILAYER PERCEPTRON NEURAL NETWORK AND RANDOM FOREST APPROACH |
| title_full | IMPROVING CLUSTER ACCURACY IN TUITION FEES: A MULTILAYER PERCEPTRON NEURAL NETWORK AND RANDOM FOREST APPROACH |
| title_fullStr | IMPROVING CLUSTER ACCURACY IN TUITION FEES: A MULTILAYER PERCEPTRON NEURAL NETWORK AND RANDOM FOREST APPROACH |
| title_full_unstemmed | IMPROVING CLUSTER ACCURACY IN TUITION FEES: A MULTILAYER PERCEPTRON NEURAL NETWORK AND RANDOM FOREST APPROACH |
| title_short | IMPROVING CLUSTER ACCURACY IN TUITION FEES: A MULTILAYER PERCEPTRON NEURAL NETWORK AND RANDOM FOREST APPROACH |
| title_sort | improving cluster accuracy in tuition fees a multilayer perceptron neural network and random forest approach |
| topic | artificial neural networks single tuition fee classification random forest |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/15112 |
| work_keys_str_mv | AT suminsumin improvingclusteraccuracyintuitionfeesamultilayerperceptronneuralnetworkandrandomforestapproach AT prihantonoprihantono improvingclusteraccuracyintuitionfeesamultilayerperceptronneuralnetworkandrandomforestapproach AT khairawatikhairawati improvingclusteraccuracyintuitionfeesamultilayerperceptronneuralnetworkandrandomforestapproach |