Implementation of SVM and GLDA for Gap Analysis on Mobile JKN
Keywords:
Gap Analysis, GLDA, Mobile JKN, SVM, User ReviewsAbstract
This study aims to analyze user perceptions of the Mobile JKN application developed by BPJS Kesehatan through sentiment analysis, topic modeling, and service quality gap analysis (SERVQUAL). Sentiment analysis was conducted using the Support Vector Machine (SVM) algorithm with linear and RBF kernels. The classification results, based on an 80:20 data split, indicate that the majority of user reviews express negative sentiment—2,784 reviews using the linear kernel and 2,847 using the RBF kernel—followed by positive and neutral sentiments. These findings suggest a general dissatisfaction among users regarding the application’s performance. Topic modeling was performed using the Guided Latent Dirichlet Allocation (GLDA) method, successfully grouping reviews into five main topics aligned with the SERVQUAL dimensions: tangibles, assurance, empathy, reliability, and responsiveness. The empathy topic appeared most frequently, while responsiveness was the least represented. The GLDA model achieved a coherence score of 0.64 and a UMass score of -1.99, indicating the model’s interpretability and consistency. Finally, the dimension-by-dimension gap analysis revealed that the assurance dimension had the smallest gap (-0.06), while the reliability dimension had the largest gap (-0.74). The overall SERVQUAL gap score was 0.11, highlighting a notable disparity between user expectations and their actual experiences. These results underline the need for targeted improvements in several service aspects of the Mobile JKN application.
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