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All Journal PROSIDING SEMINAR NASIONAL JURNAL SISTEM INFORMASI BISNIS E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Informasi dan Komunikasi Indonesian Journal of Technology, Informatics and Science (IJTIS) Walisongo Journal of Information Technology JAST Prosiding Seminar Nasional Pendidikan, Bahasa, Sastra, Seni, dan Budaya
Mokhamad Iklil Mustofa
Universitas Islam Negeri Walisongo
Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering

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Sentiment Classification of MyPertamina Reviews Using Naïve Bayes and Logistic Regression Dwi Yuni Saraswati; Handayani, Maya Rini; Umam, Khothibul; Mustofa, Mokhamad Iklil
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9723

Abstract

This research conducts a comparative evaluation of the effectiveness of the Naïve Bayes and Logistic Regression algorithms in mapping public perceptions of the MyPertamina application on the Google Play Store. The data consists of 2,000 user reviews obtained through a scraping technique. The research steps include labeling the reviews as positive or negative, followed by pre-processing and TF-IDF weighting. The dataset was systematically divided into two parts, with 80% allocated for model training and the remaining 20% for evaluation. The Naïve Bayes and Logistic Regression models were implemented using the Python programming language and evaluated based on accuracy, precision, recall, and F1-score metrics. The analysis shows that Logistic Regression achieved an accuracy of 86%, while Naïve Bayes achieved 81%. Logistic Regression demonstrated superior performance as it effectively captures linear relationships between features in TF-IDF representations and provides a more balanced outcome in terms of precision and recall. In contrast, Naïve Bayes is more influenced by high-frequency word distributions and does not account for feature correlations, which can limit its performance in certain contexts. Therefore, Logistic Regression is considered more suitable for sentiment classification tasks in this study. These findings emphasize the importance of selecting appropriate algorithms for sentiment analysis and suggest opportunities for future research using alternative methods to enhance predictive accuracy.
Comparative Study of SVM and Decision Tree Algorithms on the Effect of SMOTE Technique on LinkAja Application Faruq, Muhammad Kholfan; Umam, Khothibul; Mustofa, Mokhamad Iklil; Mahfudh, Adzhal Arwani
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.9806

Abstract

The widespread adoption of digital wallets like LinkAja in Indonesia has led to a surge in user-generated reviews, which are valuable for assessing service quality. This study compares the classification performance of Support Vector Machine (SVM) and Decision Tree algorithms on user reviews from the LinkAja application. 7.000 reviews were gathered through web scraping and processed with standard text cleaning, tokenization, stopword removal, and stemming, resulting in 6,261 usable entries. These were divided into training and testing sets in a 70:30 ratio. The performance of each algorithm was evaluated both before and after the application of Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Prior to SMOTE, SVM recorded an accuracy of 77.97%, precision of 0.74, recall of 0.33, and F1 score of 0.45, while Decision Tree reached 72.01% accuracy, 0.50 precision, 0.62 recall, and 0.55 F1 score. After SMOTE, SVM accuracy slightly improved to 78.29%, with notable increases in recall (0.74) and F1 score (0.60); Decision Tree also saw an accuracy rise to 74.56% but experienced a slight decline in F1 score to 0.52. These findings demonstrate that SVM, particularly when used with SMOTE, offers better overall performance and class balance in classifying reviews with imbalanced sentiment distribution, making it more suitable than Decision Tree for this application.
Co-Authors Abdul Rofiq Ahmad Ukasyah S Alfunarrusda Chodzirin, Muhammad Devi Marita Putri Dwi Yuni Saraswati Faruq, Muhammad Kholfan Fauzan, Roman Fitro Nur Hakim Intan Rahma Sandhika Khothibul Umam Lina Sayekti Mahfudh, Adzhal Arwani Masyhudan Ahmad Maya Rini Handayani Muhammad Abdur Rofi Maulidin Muhammad Chodzirin Osa Marsanda Putri Ridho Aditya Nugroho Roman Fauzan Sarah Latifah Ummah Sayekti, Lina Thooriq Nur Ali Zaenal Mustofa