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Salsabila Roiqoh
Universitas Airlangga, Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisis Sentimen Berbasis Aspek Ulasan Aplikasi Mobile JKN dengan Lexicon Based dan Naïve Bayes Salsabila Roiqoh; Badrus Zaman; Kartono Kartono
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6194

Abstract

Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan is a legal entity that provides social health insurance programs for the public released application called Mobile JKN to support various health services activities using users devices. Mobile JKN has not fully received a positive public perception and still has many shortcomings. It is necessary to conduct a deeper evaluation and analysis of the Mobile JKN. This study focuses on aspect-based sentiment analysis of user reviews on the Google Play Store to evaluate the Mobile JKN. The review data used are the last two versions, 4.2.3 and 4.3.0. This study was carried out by modeling aspects/topics using the Latent Dirichlet Allocation method and sentiment analysis using Naïve Bayes and Lexicon-Based methods. This research resulted in 3 aspects, namely Services and Features, Register and Login, and User Satisfaction. This was obtained based on the model with the highest coherence score of 0.6392 obtained in the model looping with the number of topics from 1 to 9, random state = 42, passes =50, and iteration = 60. Meanwhile, based on the sentiment analysis results, the Naïve Bayes method is better than the Lexicon-Based (Inset Lexicon) method. This is evident from performance of the Naïve Bayes with the highest accuracy score of 94.75% and Lexicon Based with Inset Lexicon obtained an accuracy score of 59.99%.
Co-Authors Badrus Zaman Kartono Kartono