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p-Index From 2021 - 2026
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All Journal The Journal of Enhanced Studies in Informatics and Computer Applications
Carli Apriansyah Hutagalung
State University of Jakarta
Computer Science & IT

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Aspect-Based Sentiment Analysis of Indonesian Healthcare Application Reviews Using Latent Dirichlet Allocation and IndoBERT Carli Apriansyah Hutagalung; Muhammad Arib Alwansyah; Tiara Husnul Khotimah
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 3 No. 2 (2026): JESICA Vol. 3 No. 2 2026
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v3i2.47

Abstract

The increasing use of digital healthcare applications has generated large volumes of user reviews that reflect user experiences and satisfaction. This study aimed to perform Aspect-Based Sentiment Analysis (ABSA) of Indonesian healthcare application reviews using Latent Dirichlet Allocation (LDA) and IndoBERT. A total of 3,000 reviews were collected from Halodoc, Alodokter, and SATUSEHAT Mobile through the Google Play Store. After preprocessing, 1,460 valid reviews were analyzed. LDA was applied to identify discussion aspects, while IndoBERT was used to classify review sentiments into positive, neutral, and negative categories. The analysis identified seven major aspects related to consultation services, doctor responsiveness, medication delivery, payment processes, and system accessibility. Negative sentiment dominated the dataset (51.78%), followed by positive (40.82%) and neutral (7.40%) sentiments. Doctor Responsiveness and Service Quality achieved the highest positive sentiment (86.57%), whereas Login, OTP, and System Access Problems showed the highest negative sentiment (89.71%). The findings indicated that users appreciated healthcare professionals but frequently reported technical and operational issues. The study concluded that improvements in system reliability, authentication mechanisms, payment services, and medication delivery processes are essential to enhance user satisfaction with Indonesian healthcare applications
A Systematic Review of Machine Learning Approaches for K-Pop Concert Sentiment Analysis on X Yunia Putri Adifa; Carli Apriansyah Hutagalung
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 3 No. 2 (2026): JESICA Vol. 3 No. 2 2026
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v3i2.48

Abstract

K-pop concerts in Indonesia generate intensive digital discussion on X/Twitter, yet studies that directly combine concert-related objects, X data, and machine-learning-based sentiment classification remain limited. This study conducts a systematic literature review to map research objects, methods, preprocessing techniques, evaluation results, and research gaps in K-pop sentiment analysis. The selection process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. A total of 5,674 records were collected, 4,618 duplicates were removed, 1,056 records were screened, and 20 articles were included in the final synthesis. The findings show that X is the dominant platform. Previous studies more frequently examine K-pop groups, fandom, the Korean Wave, and cyberbullying rather than direct concert experiences. Naive Bayes remains widely used because it is simple, efficient, and suitable for high-dimensional text data, although Support Vector Machine and transformer-based models often provide stronger performance in specific settings. Classification quality is strongly affected by non-standard language normalization, multilingual content, class balance, feature weighting, and labeling consistency. The main gap is the absence of an Indonesian K-pop concert sentiment-analysis design that combines domain-aware preprocessing, per-class evaluation, and aspect-level interpretation of ticketing, promoters, venues, safety, and audience experience.
Co-Authors Muhammad Arib Alwansyah Tiara Husnul Khotimah Yunia Putri Adifa