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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Sains dan Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi JISICOM (Journal of Information System, Infomatics and Computing) Journal of Comprehensive Science Eduvest - Journal of Universal Studies
Lulu Chaerani Munggaran
Gunadarma University
Aerospace Engineering Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Neuroscience Social Sciences

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Journal : Journal of Comprehensive Science

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Penerapan Analisis Sentimen Berbasis Aspek Menggunakan Mesin Vektor Pendukung pada Aplikasi Layanan Medis Digital Sari Pranasti, Adira; Munggaran, Lulu Chaerani
Journal of Comprehensive Science Vol. 4 No. 2 (2025): Journal of Comprehensive Science (JCS)
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jcs.v4i2.3047

Abstract

One of the digital health service applications that are widely used in Indonesia is Halodoc. User reviews serve as a crucial source of information for developers to improve service quality. This study implements Aspect-Based Sentiment Analysis (ABSA) using Support Vector Machine (SVM) to classify user reviews based on aspects and sentiment. Data was collected through web scraping from the Google Play Store and categorized into four main aspects: Application Performance, Service Quality, Feature Completeness, and Pricing. Sentiment classification includes positive, neutral, and negative categories. The results show that positive sentiment is the most dominant (41.1%), followed by neutral (28.5%) and negative (30.4%). The SVM model achieves the highest F1-score in positive sentiment (0.85), while the Pricing aspect has the lowest accuracy (F1-score 0.46). This study demonstrates that ABSA can help Halodoc better understand user satisfaction, especially in aspects that frequently receive negative reviews, such as service pricing and delayed medicine delivery.
Co-Authors Akrom, Mohamad Akromudin Anindito Yoga Pratama Dela Harmilasari Eko Rudi Suteja Esty Purnamasari Hasana, Uswatun Herawan, Hery Ika Atman Satya Muhammad Khalif Fadhilah Raufani, Hasnadhia Rayhan Gusty Alif Sari Pranasti, Adira Setiawan, Foni Agus Sri Astuti Winarti Winarti Wulandari, Yestika Dian