Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri
Muhamad Alvuad Mualo
Unknown Affiliation
Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Comparative Sentiment Analysis of Provider X Application Reviews Using Support Vector Machine, Random Forest, and Naïve Bayes Algorithms Based on TF-IDF and SMOTE Hamama Kamtelat; Nirwan Moningka; Agung K Henaulu; Haris Kolengsusu; Rahul Lestaluhu; Muhamad Alvuad Mualo
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol. 23 No. 2 (2026): June 2026
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v23i2.40021

Abstract

In the era of digital transformation, the Provider x application has become the main digital touchpoint that reflects corporate credibility through customer experience. User reviews on the Google Play Store are an authentic business intelligence asset, but their massive volume requires machine learning solutions for objective and measurable analysis. This research aims to conduct comparative sentiment analysis by applying three labeling categories (positive, negative and neutral) to capture user opinions more comprehensively. Using a dataset of 10,983 clean reviews, this research applies rigorous text pre-processing and feature extraction using TF-IDF. To overcome class imbalance, the SMOTE (Synthetic Minority Over-sampling Technique) technique is integrated into the model. This research compares three main algorithms: Support Vector Machine (SVM), Random Forest, and Naïve Bayes. Experimental results show that Random Forest excels as the best model with the highest accuracy rate of 82%, significantly surpassing SVM (73%) and Naïve Bayes (71%). These findings prove the effectiveness of ensemble structures in processing high-dimensional text features and provide empirical insights for developers to improve services based on customer voice.
Co-Authors Agung K Henaulu Hamama Kamtelat Haris Kolengsusu Moningka, Nirwan Rahul Lestaluhu