M-Health Service Quality Analysis of Kimia Farma Mobile Application on Google Play Store Using Sentiment Analysis and Topic Modeling Methods
DOI:
https://doi.org/10.38035/dijefa.v6i5.5176Keywords:
KNN, SVM, LDA, KIMIA FARMA MOBILE, M-HEALTH SERVICE QUALITYAbstract
Digital Transformation in the pharmaceutical industry grew rapidly, accelerated by the COVID-19 pandemic, leading to a surge in mobile healthcare service transactions. Kimia Farma Mobile, initiated by PT Kimia Farma Apotek, is a m-health application that provided medicine purchase, consultation, and laboratory services. However, the Kimia Farma Mobile application had a lower rating than its competitors. This study aimed to analyze user satisfaction and dissatisfaction levels based on Google Play Store reviews to understand the root causes of its low rating. Utilizing sentiment analysis, reviews were classified into positive and negative polarities. Latent Dirichlet Allocation (LDA) was then used to identify key factors influencing satisfaction and dissatisfaction within the M-Health Service Quality framework. The Support Vector Machine (SVM) method was chosen for its high accuracy, yielding an F1-score of 97.39%. The results showed that positive reviews were primarily driven by ease of use and helped users in providing optimal services. In contrast, negative reviews were linked to issues in Information Quality, Interaction Quality, and System Quality. This study concludes by providing specific recommendations to improve the application's service quality, operational systems, and overall user experience to enhance satisfaction and boost its rating.
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