Sentiment Analysis of Gojek Driver Application Reviews Using Support Vector Machine and Naïve Bayes with Optuna-Based Hyperparameter Tuning

2026-05-14T06:56:27+00:00

Purpose – This study aims to analyze user review sentiment toward the Gojek Driver application and compare the performance of two classification algorithms, Support Vector Machine (SVM) and Naïve Bayes, using Optuna as a framework for hyperparameter tuning.
Methods – The study collected and labeled user review data into positive and negative sentiment categories. Text preprocessing involved cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Features were represented using TF-IDF. The dataset was then divided into training and testing sets, and SVM and Naïve Bayes models were trained using automated hyperparameter optimization with Optuna. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix.
Findings – The application of SMOTE to the Optuna-tuned SVM model produced better performance than the other models tested in this study. The best model achieved an accuracy of 0.868, a highest cross-validation accuracy of 92.72%, and a weighted average F1-score of 0.87. These results indicate that SVM was more effective in handling high-dimensional TF-IDF features and complex decision boundaries.
Research implications – The findings support the use of automated sentiment analysis to assist operational decision-making and improve the quality of Gojek Driver services. The proposed approach can accelerate the identification of service-related issues and provide a basis for proactive responses to user feedback.
Originality – This study offers an original contribution by directly comparing SVM and Naïve Bayes on a Gojek Driver review dataset while applying Optuna-based hyperparameter tuning. It highlights the effect of automated tuning on both algorithms within a TF-IDF representation framework for ride-hailing service data, a topic that remains underexplored in the specific context of Gojek Driver within the local literature.

The Utilization of Technology-Based Media in Physical Education Learning Strategies at Madrasah Ibtidaiyah

2026-05-14T06:56:42+00:00

Purpose – This study examines the use of technology-based media in Physical Education learning at Madrasah Ibtidaiyah (MI) and Elementary School (SD) levels. It addresses the limitations of conventional instruction in explaining complex movement concepts and the need for more interactive, visual, and student-centered learning strategies in the digital era.
Methods – A qualitative literature review was conducted using a systematic review procedure adapted from the PRISMA 2020 framework. Searches were carried out in Google Scholar and ERIC for publications from 2015 to 2025. From 145 initial records, 18 studies met the inclusion criteria and were synthesized qualitatively.
Findings – The review shows that instructional videos, animations, simulations, interactive multimedia, mobile applications, and exergames can support students’ movement understanding, motivation, engagement, and participation in Physical Education learning. These media help present movement concepts visually and flexibly. However, their implementation is constrained by limited technological infrastructure, unequal access to digital resources, and insufficient teacher competence in integrating technology into movement-based instruction.
Research implications – The findings suggest that technology integration should be supported by adequate infrastructure, teacher training, and pedagogical strategies that maintain active physical participation. Since this study is literature-based, its findings should be interpreted as synthesized evidence rather than direct empirical proof.
Originality – This study provides a focused synthesis of recent literature on technology-based media in MI/SD Physical Education and identifies its pedagogical potential, implementation challenges, and future research directions.

Journal of Deep Learning, Computer Vision and Digital Image Processing

Sentiment Analysis of Gojek Driver Application Reviews Using Support Vector Machine and Naïve Bayes with Optuna-Based Hyperparameter Tuning

The Utilization of Technology-Based Media in Physical Education Learning Strategies at Madrasah Ibtidaiyah