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All Journal JURIKOM (Jurnal Riset Komputer) Zonasi: Jurnal Sistem Informasi
Epriyanti, Nadia
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media

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PERBANDINGAN METODE NAÏVE BAYES, DECISION TREE, DAN KNN DALAM ANALISIS SENTIMEN APLIKASI GOJEK DI PLAYSTORE Maretta, Aulia Pinkan; Anadia, Qothrunnada Wafi; Sasmita, Ruth Mei; Epriyanti, Nadia; Rizkyllah, Anabel Fiorenza; Mariska, Inneke Via; Tania, Ken Ditha; Meiriza, Allsela
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 2 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Mei 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zjf8x279

Abstract

Sentiment analysis on user evaluation of Gojek application services on Play Store is important to understand user opinions on the services provided. This study compares three machine learning methods, namely Naïve Bayes, Decision Tree, and K-Nearest Neighbors (KNN) when categorizing user sentiment on Google Play Store as positive, negative, or neutral. The data processed comes from the Gojek user review dataset obtained from Kaggle. The analysis process involves data preprocessing (cleaning, stopword removal, tokenization, and split data), data transformation, and implementation of classification algorithms. The evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results of the study prove that Naïve Bayes has the best performance with an accuracy of 89%, followed by KNN (86%) and Decision Tree (84%). This study provides good insight for application developers in choosing the best method to understand user opinions and improve service quality.
Perbandingan Kinerja SVM, Random Forest dan XGBoost pada Aplikasi Access by KAI Menggunakkan ADASYN Epriyanti, Nadia; Meiriza, Allsela; Yunika Hardiyanti, Dinna
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.9139

Abstract

The rapid growth of digital applications has heightened the need to understand user perceptions more thoroughly, particularlythrough sentiment analysis of user-generated reviews. In practice, sentiment classification often faces challenges related to class imbalance, especially when neutral reviews are significantly fewer than positive or negative ones. This imbalance can limit a model’s ability to accurately detect all sentiment categories. This study examines the comparative performance of three machine learning algorithmsSupport Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) by applying the Adaptive Synthetic Sampling (ADASYN) technique to address class imbalance. This study differs from previous similar research by conducting a simultaneous comparative analysis of three algorithms using the ADASYN method in the context of Access by KAIapplication reviews, which has not been examined in prior studies. Experimental results indicate that after implementing ADASYN, model accuracies reached 75.17% for SVM, 84.06% for RF, and 83.17% for XGBoost. Although accuracy slightly decreased after oversampling, the F1-scores for the neutral class improved to 0.13 (SVM), 0.05 (RF), and 0.14 (XGBoost). Before applying ADASYN, the models achieved accuracies of 85.88% (SVM), 85.13% (RF), and 85.37% (XGBoost), but they were unable to effectivelyrecognize neutral sentiments, with F1-scores of 0.00 for SVM and RF, and 0.03 for XGBoost. These findings suggest that ADASYN enhances model sensitivity to neutral sentiment, with XGBoost demonstrating the most consistent and robust performance in sentiment classification for the Access by KAIapplication.
Co-Authors Allsela Meiriza, Allsela Anadia, Qothrunnada Wafi Ken Ditha Tania Maretta, Aulia Pinkan Mariska, Inneke Via Rizkyllah, Anabel Fiorenza Sasmita, Ruth Mei Yunika Hardiyanti, Dinna