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ABSTRACT
ANALYSIS OF STUDENTS LEARNING INTEREST PATTERNS AT MADRASAH DINIYAH TAKMILIYAH AWALIYAH (MDTA) USING THE K-MEANS ALGORITHM

By: Mica Adella (22.22.0031)

Students’ learning interest plays an important role in supporting the success of the learning process, especially at Madrasah Diniyah Takmiliyah Awaliyah (MDTA). However, the assessment of students’ learning interest at MDTA Qur’an Kisaran is still carried out manually and tends to be subjective, making it difficult for the institution to classify students based on their level of learning interest and to determine appropriate learning strategies. This study aims to analyze and classify students’ learning interest patterns by utilizing the K-Means Clustering algorithm in order to produce a more objective and systematic grouping. The data used in this study include religious academic scores, attendance levels, student activeness, Qur’an memorization, Qur’an reading ability, and moral values. The method applied is data mining with a clustering approach using the K-Means algorithm, through stages of data collection, preprocessing, data transformation, clustering process, and result evaluation. The system is also developed as a web-based application using PHP and MySQL to facilitate data processing and presentation. The results of this study indicate that the K-Means algorithm is able to classify students into three clusters, namely high, medium, and low learning interest clusters. This classification provides a more objective overview of students’ conditions and assists teachers in determining appropriate learning strategies as well as supporting the selection process for activities such as PORSADIN more effectively and accurately.

Keywords: clustering; data mining; k-means; mdta; learning interest