Article Per Year (5 Year)
p-Index From 2020 - 2025
2.319
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sistem Informasi dan Bisnis Cerdas Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengembangan Riset dan Observasi Teknik Informatika KOMPUTEK Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Sains dan Teknologi Asian Journal of Community Services (AJCS) INOVTEK Polbeng - Seri Informatika
Anief Fauzan Rozi, Anief Fauzan
Universitas Mercu Buana Yogyakarta
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : INOVTEK Polbeng - Seri Informatika

 1 
Sentiment Analysis and Classification of User Reviews on the Redbus Application Using Logistic Regression And SVM Burrhanuddin, Nafi' Ikhsan; Rozi, Anief Fauzan
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/k6k6m469

Abstract

The increasing number of RedBus users in Indonesia has led to a growing volume of user reviews on digital platforms, especially the Google Play Store. These reviews reflect user perceptions and are valuable for sentiment analysis. This study aims to classify sentiments in RedBus user reviews using Logistic Regression and Support Vector Machine (SVM) algorithms. A total of 2,000 reviews were collected through automated web scraping and labelled using a lexicon-based approach. The data underwent preprocessing steps including normalisation, tokenisation, filtering, stemming, and labelling. Features were transformed using the TF-IDF method and split into 90% training and 10% testing sets. Evaluation results showed that SVM with a linear kernel outperformed Logistic Regression, achieving 91.10% accuracy and more balanced F1-scores across sentiment classes. Logistic Regression reached 86.39% accuracy but performed lower on positive sentiment. A paired t-test confirmed the statistical significance of the performance difference (p = 0.0005). These findings suggest that SVM is more effective in handling high-dimensional text data and can be recommended for real-world sentiment classification tasks, such as filtering negative reviews and improving customer service.
Co-Authors Athallah, Muhamad Reza Audita Nuvriasari Azizah, Nailatul Burrhanuddin, Nafi' Ikhsan Febriyanti, Natasya Fitri, Ichlasia Ainul Heru Prayitno Manik, Tessa Monika Manurung, Vanni Munandar, Annisa Nurfitri Rida Saputra, Irfan Juliardi Titik Desi Harsoyo, Titik Desi Turnip, Erdina Wibowo, Rizki Widodo, Arief Setyo