Utilization Of The K-Means Clustering Method To Analyze The Level Of Students Memorization Progress At One Of The Al Educational Institutions In Bandung City

Abstract

Evaluation of students' memorization abilities at an Al-Quran educational institution in Bandung was previously carried out manually, making it difficult to monitor memorization progress comprehensively and making data management inefficient. This study aims to develop a web-based information system that employs the K-Means Clustering technique to classify pupils based on their memorization proficiency. The system is designed to aid educators in categorizing students into distinct groups, including Fast Memorization Students, Normal Progress Students, Students Needing Guidance, Exceptional Memorization Students, Consistent Students, and Students Needing Motivation, to facilitate more targeted coaching strategies. The system development methodology employs an object-oriented software engineering technique, utilizing a Model-View-Controller (MVC) architecture and incorporating the Laravel framework and a MySQL database. The technology enables instructors to enter the number of memorized verses. It automatically analyzes this information utilizing the K-Means method to generate cluster divisions based on similarities in memorization values. The clustering method is executed iteratively until ideal centroids are attained. Testing is performed using Black-Box Testing techniques to verify that all system functionalities comply with user requirements. The test results demonstrate that the system operates efficiently, encompassing data input, processing via the K-Means method, and presenting outcomes in a clustered format. This technique aims to assist educators in assessing student retention more effectively and facilitate a more focused coaching approach.