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All Journal Jurnal Riset Informatika Jurnal Informatika Terpadu
Samsudin, Syafri
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Computer Science & IT Decision Sciences, Operations Research & Management Education

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Analisis Sentimen Ulasan Aplikasi Gojek Menggunakan Support Vector Machine Dan Random Forest Aditya, Azka Bima; Samsudin, Syafri; Rizki, Winahyu Pandu; Mahendra, Mahir; Setiawan, Arif
Jurnal Informatika Terpadu Vol 11 No 2 (2025): September, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v11i2.1884

Abstract

The rapid development of digital transportation, such as Gojek, requires a deep understanding of user satisfaction. This study analyzes the sentiment of Gojek application reviews to evaluate public opinion and compare the performance of the Support Vector Machine (SVM) and Random Forest models. A quantitative experimental method was applied to 30,055 user reviews for versions "4" and "5" from the Google Play Store. The data underwent comprehensive text preprocessing, automatic sentiment labeling using VADER enriched with an Indonesian lexicon, and TF-IDF feature extraction. The training data imbalance was addressed using SMOTE before the data was split for training and testing. The results show that user sentiment was dominated by positive (38.9%) and neutral (38.2%) categories. In the performance evaluation, the SVM model demonstrated superior performance with 96% accuracy and an F1-score of 0.96, outperforming the Random Forest model, which achieved 93% accuracy and an F1-score of 0.93. In conclusion, SVM is a more effective model for sentiment classification of Gojek reviews. Future research is recommended to refine the lexicon and implement aspect-based analysis to obtain more detailed insights.
COMPARATIVE MACHINE LEARNING ALGORITHMS FOR YOUTUBE SENTIMENT ANALYSIS ON DPR DEMONSTRATION 2025 USING LEXICON Samsudin, Syafri; Abdul Chamid, Ahmad; Jazuli, Ahmad
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.470

Abstract

The high volume of public comments on YouTube regarding the DPR Demonstrasion August 2025, which reached 43,910 raw data, presents a significant challenge in conducting efficient sentiment analysis. Time and cost limitations in manual labeling for large-scale datasets are a major obstacle in the development of predictive models. This study aims to address this problem by proposing a hybrid approach that integrates Lexicon-Based auto-labeling with a comparative evaluation of five Machine Learning algorithms. The research methodology included a text preprocessing stage that generated 40,097 unique comments, feature extraction using TF-IDF, and data sharing with an 80:20 ratio. The performance of the Support Vector Machine algorithm was comprehensively compared to Random Forest, Decision Tree, K-Nearest Neighbors, and Naive Bayes. The results of the experiment showed that the SVM model recorded the most superior performance with an accuracy of 96.5% and a weighted F1-Score of 0.966. This score significantly outperformed other benchmarking algorithms, where Random Forest came in second place with 89.2% accuracy, followed by Decision Tree at 85.6%, KNN at 84.6%, and Naive Bayes at the lowest with 84.0%. These findings validate that the integration of Lexicon-Based labeling with SVM classification is a highly accurate, robust, and efficient solution for handling sentiment analysis on large-scale social media data in Indonesia.
Co-Authors Aditya, Azka Bima Ahmad Abdul Chamid Ahmad Jazuli Arif Setiawan Mahendra, Mahir Rizki, Winahyu Pandu