Klasterisasi Topik Khotbah Pendeta Di GBI MPI Palembang Dengan Metode DBSCAN

Authors

  • Kristian Fernando Universitas Multi Data Palembang
  • Hafiz Irsyad Universitas Multi Data Palembang

DOI:

https://doi.org/10.37859/jf.v15i3.10641
Keywords: DBSCAN, teaching evaluation, text clustering, text mining, TF-IDF

Abstract

Comprehensive evaluation of the teaching curriculum proportion at GBI Rayon 15 Musi Palem Indah (MPI) Palembang is a fundamental element in ensuring the doctrinal health of the congregation. However, the current evaluation process is inefficient due to reliance on manual mapping of ever-growing sermon archives. This conventional method carries a high risk of subjectivity bias, making it difficult for church leadership to objectively observe teaching theme trends. This study addresses this issue by developing an automated document clustering system based on Text Mining to process 406 sermon summary documents from the 2023-2025 period. The methodology includes preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) weighting to highlight distinctive theological terms, and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DBSCAN was specifically selected for its superiority in handling data with varying densities and its ability to isolate outliers without requiring a static cluster count parameter. Test results indicate an optimal configuration at Epsilon 0.3 and MinPts 3, yielding very high internal validity with a Silhouette Coefficient of 0.8888 and forming 32 core topic clusters. Significant findings reveal a high noise ratio (71%), which effectively separates incidental topics, such as holiday celebrations, from regular material. Practically, these results serve as an early warning system mechanism for the church to detect doctrinal imbalances or material gaps, providing a strategic data-driven foundation for holistic curriculum improvement.

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Published

2025-12-31

Issue

Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)

Section

Articles

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Copyright (c) 2025 Kristian Fernando, Hafiz Irsyad

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