IMPROVING CLUSTER ACCURACY IN TUITION FEES: A MULTILAYER PERCEPTRON NEURAL NETWORK AND RANDOM FOREST APPROACH
Abstract
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.
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