Clustering Pospay User Reviews Using Rule-Based Hybrid K-Means and TF-IDF
DOI:
10.33395/sinkron.v10i4.16556Keywords:
Text mining, Rule-Based Hybrid K-Means, TF-IDF Bigram, User Reviews, PospayAbstract
Pospay is a digital financial services application created by PT Pos Indonesia, which offers a variety of financial transaction services. The huge volume of unstructured data and the exponential growth of the number of users’ assessments in Google Play Store makes it impossible to be analyzed manually. This research aims to classify Pospay customer evaluation to identify the main problems experienced by the user and provide recommendation in upgrading application services. This work adopts the Knowledge Discovery in Databases (KDD) technique. The inputs include 11000 user evaluations, of which 10943 are maintained after pre-processing. The input text has been vectorized using TF-IDF Bigram and the ideal number of clusters has been calculated using the Elbow Method and Silhouette Score. Then, a Rule-Based Hybrid K-Means technique was used, which integrated K-Means++ clustering with rule-based refinement to enhance the interpretability of the clusters. The findings produced five primary clusters that are related to verification and identity, system and error, login and account, transaction and balance, and positive reviews. Authentication and identification and transaction and balance were the most talked-about issues among users from these countries, accounting for 31.3% and 26.8% of discussions respectively. PCA visualization and word cloud analysis further supported the interpretation of each cluster. Overall, the proposed approach effectively grouped user reviews into meaningful topics and can assist developers in identifying service priorities to improve the quality and reliability of the Pospay application.
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Copyright (c) 2026 Suci Tamaro Siahaan, Christian Dwi Suhendra, Lion Ferdinand Marini
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