Evaluasi Model Latent Dirichlet Allocation untuk Topic Modelling pada Ulasan SIMKOPDES Menggunakan Coherence Score dan Perplexity
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1271Keywords:
Latent Dirichlet Allocation; Topic Modelling; SIMKOPDES; Coherence Score; Perplexity; User ReviewAbstract
The digital transformation of Koperasi Desa/Kelurahan Merah Putih is supported through the development of the SIMKOPDES Mobile application as a digital service platform for cooperative members. As the number of users continues to grow, reviews posted on Google Play Store have become an important source of information for evaluating application quality and user satisfaction. The large volume of unstructured textual reviews makes manual analysis inefficient. This study aims to identify the dominant topics in SIMKOPDES Mobile user reviews using Latent Dirichlet Allocation (LDA) and evaluate the quality of the generated topic models based on Coherence Score and Perplexity. User reviews were collected through web scraping, followed by text preprocessing consisting of case folding, tokenization, normalization, stopword removal, and stemming. LDA models were then developed using three different numbers of topics and evaluated using Coherence Score and Perplexity, while Intertopic Distance Map and Word Cloud visualizations were employed to support topic interpretation. The results indicate that the one-topic model achieved the highest Coherence Score of 0.419677, whereas the three-topic model produced the lowest Perplexity value of ?6.2917. Although these evaluation metrics yielded different optimal results, the three-topic model was selected because it generated more distinct, representative, and semantically interpretable topics related to cooperative membership and application benefits, account registration and verification, and user experience. This study demonstrates that selecting the optimal LDA model should not rely solely on statistical evaluation metrics but should also consider semantic interpretability, providing a more comprehensive approach for topic modelling of digital application reviews.
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