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Aura Firdausiyah
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Computer Science & IT

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Implementation of K-Means Clustering with Attribute Adjustment and Cluster Validation in a Web-Based TPQ Student Assessment Information System Aura Firdausiyah; Sri Widoyoningrum
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i02.2386

Abstract

This study aims to implement K-Means Clustering with attribute-level adjustment in a web-based TPQ student assessment information system and evaluate the quality of the resulting clustering results. The study was conducted at TPQ Ashfiya' Kauman Ngoro Jombang using a dataset of 40 students at the Al-Qur'an level obtained from learning assessment data. The variables used include fashohah, tajwid, ghorib and musykilat, as well as voice and melody. The clustering process was carried out using the K-Means algorithm to group students based on similarity in scores, while attribute adjustment was applied in the system design to prevent unused assessment components from being interpreted as low ability scores. Evaluation of cluster quality was carried out using the Sum of Squared Error (SSE), Silhouette Score, and Davies-Bouldin Index (DBI). Based on the evaluation results, K=3 was selected because it formed an interpretable grouping structure and was supported by the Elbow Method. The results show that the application of the K-Means method in the information system can help group students' abilities in a more structured and objective manner and support assessment data management by TPQ teachers. However, the results of this study are a case study in the TPQ environment studied and have not been intended for generalization to a wider population.
Co-Authors Sri Widoyoningrum