Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Journal of Information Systems and Informatics Zonasi: Jurnal Sistem Informasi
Anadia, Qothrunnada Wafi
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Languange, Linguistic, Communication & Media

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
Performance Comparison of Sentiment Classification Algorithms on SIGNAL Reviews Using SMOTE Anadia, Qothrunnada Wafi; Meiriza, Allsela
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1196

Abstract

Public service apps like SIGNAL are widely used to provide public access to information and vehicle tax payments. However, diverse user reviews highlight the need to evaluate public perception through sentiment analysis. Selecting an appropriate classification algorithm is crucial to ensure accurate results, particularly when dealing with imbalanced review data. Therefore, This study examines the comparative performance of four algorithms Naïve Bayes, Random Forest, Decision Tree, and SVM in analyzing the sentiment of 36,000 user feedback obtained from Google Play Store. The dataset underwent preprocessing, feature extraction using TF-IDF, and class balancing using SMOTE. Model evaluation was conducted using accuracy, precision, recall, and F1-score. The findings indicated that Random Forest performed the best overall performance (accuracy 91.04%, F1-score 94.80%), followed by Naïve Bayes (accuracy 89.89%, F1-score 93.38%), SVM (accuracy 89.22%, F1-score 93.02%), and Decision Tree (accuracy 88.40%, F1-score 92.31%). These findings indicate that Random Forest is highly effective for balanced datasets, while SVM and Naïve Bayes offer competitive precision for applications prioritizing accuracy in positive class detection. The output of this study can be applied practically by developers and related institutions in optimizing public service applications and by applying Random Forest algorithm to gain actionable insights for optimizing features and aligning services more closely with user needs.
Co-Authors Allsela Meiriza, Allsela Epriyanti, Nadia Ken Ditha Tania Maretta, Aulia Pinkan Mariska, Inneke Via Rizkyllah, Anabel Fiorenza Sasmita, Ruth Mei