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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Elfizar
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Computer Science & IT

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Penerapan Non-negative Matrix Factorization untuk Pemodelan Topik pada Opini Kegiatan Dakwah Rahmad Kurniawan; Aidha Tita Irani; Sukamto; Ilyas Husti; Fatayat; Elfizar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The Indonesian Ulema Council (MUI) of Riau Province faces challenges in objectively evaluating dakwah (Islamic preaching) as its existing Peta Dakwah Cerdas (PDC) system lacks a feature to analyze congregant feedback. This study aims to design and implement a topic modeling model to identify the main hidden themes within congregant opinions. The study utilized 2,581 comments collected from the MUI Riau Smart Evaluation System. The methodology involved text preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) word weighting, and topic modeling using the Non-Negative Matrix Factorization (NMF) algorithm. Toward determine the optimal number of topics (k), the model was evaluated using Coherence Score to measure semantic readability and Silhouette Score to measure the resulting topic separation. The experiment identified two topics (k=2) as the best configuration achieving a high Coherence Score of 0.7023 and a Silhouette Score of 0.0163. The two main topics formed represent (1) Prayers and Greetings for the Preacher, and (2) Congregant Participation and Appreciation for the Dakwah. The application of NMF proved effective in identifying thematic patterns in congregant opinions and can serve as a foundation for MUI Riau to develop a real-time Islamic preaching evaluation system.
Pemodelan Topik Pada Ulasan Kegiatan Dakwah Menggunakan Algoritma Latent Dirichlet Allocation Elfizar; Sherly Fillia; Rahmad Kurniawan; Sukamto; Tisha Melia; Fitra Lestari
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Indonesian Ulema Council (MUI) of Riau Province plays an important role in dakwah (Islamic preaching) development, yet its evaluation methods remain limited. Understanding congregant feedback is crucial, but manually analyzing thousands of comments is ineffective. This research aims to apply topic modeling to automatically identify the main themes within congregant opinions. The algorithm used is Latent Dirichlet Allocation (LDA), analyzing 2,581 comments collected from the MUI Riau Smart Evaluation System. The research phase involved text preprocessing, such as cleaning, case folding, tokenizing, stopword removal, and stemming to produce clean data. This data was then converted into a Bag-of-Words (BoW) representation as input for the LDA model. The optimal number of topics was determined through evaluation using Coherence Score and Perplexity. Experimental results show that a configuration with 16 topics provides the best balance between semantic coherence and model generalizability, with a Coherence Score of 0.5008 and a Perplexity of -7.7787. The identified topics reflect diverse aspects, including prayers, appreciation for preachers, respect, discussions on Islamic values, and spiritual reflections. The LDA method proved effective in extracting thematic patterns from congregant opinions, providing a foundation for developing a real-time evaluation system.
Co-Authors Aidha Tita Irani Fatayat Fitra Lestari Ilyas Husti rahmad Kurniawan Sherly Fillia Sukamto Tisha Melia