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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INOVTEK Polbeng - Seri Informatika
Gabriel Ekoputra Hartono Cahyadi
Universitas Sriwijaya
Computer Science & IT Engineering

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EVALUASI USER ACCEPTANCE PLATFOR EVALUASI USER ACCEPTANCE PLATFORM TOKOPEDIA MELALUI FRAMEWORK UTAUT3 DAN ANALISIS KEPUTUSAN TOPSIS DENGAN IMPLEMENTASI RSTUDIO Muhammad Ravi Wijayanto Sanjaya; M. Rudi Sanjaya; bayu wijaya putra; Gabriel Ekoputra Hartono Cahyadi; Endang Lestari
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6938

Abstract

This study aims to evaluate user acceptance of the Tokopedia e-commerce platform in Indonesia by applying the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) framework combined with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis implemented in RStudio. Data were collected through an online questionnaire distributed through social networks, generating responses from 200 Indonesian users. Each UTAUT3 construct (Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, Habit, and Personal Innovativeness) was measured using a five-point Likert scale. The TOPSIS method was then applied to determine the ranking and relative importance of each construct in shaping user acceptance. The results indicate that Effort Expectancy (EE) and Personal Innovativeness (PI) are the most influential factors, reflecting users' appreciation of Tokopedia's ease of use and their openness to adopting the digital platform. Conversely, Habit (HB) showed the lowest score, indicating that routine use is still limited among some users. These findings provide valuable insights for Tokopedia and other digital commerce platforms to improve user engagement and service optimization in Indonesia's rapidly growing online market. The findings of this study suggest that platform development should focus more on promotional programs to improve user habits in using Tokopedia as a primary e-commerce platform.
Implementation of the TOPSIS Method and Usability Method for Marketplace Application Based on Data Visualization M. Rudi Sanjaya; Bayu Wijaya Putra; Gabriel Ekoputra Hartono Cahyadi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/2w11eh43

Abstract

The rapid development of technology in online marketplaces has significantly influenced consumer shopping behavior, with applications such as Shopee, Tokopedia, Zalora, and Bukalapak leveraging advances in information and communication technology to provide faster and more efficient shopping experiences. However, frequent system disruptions often affect user satisfaction, emphasizing the need for improved information systems. This study, conducted in South Sumatra with 334 respondents, utilized questionnaire data that were processed and visualized using R, where decision-support metrics were analyzed through the TOPSIS method with equal weights and a normalized respondent data matrix calculate_topsis  function(data, weights = c(0.2, 0.2, 0.2, 0.2, 0.2)), normalized_matrix as.matrix(data responden), and the methodology integrated both the usability approach and the TOPSIS method within an R Shiny environment. The findings show that data visualization effectively applied the usability and TOPSIS methods, with usability evaluation results indicating average scores of Memorability (4.263), Satisfaction (4.186), Learnability (4.146), Efficiency (4.101), and Low Error Rate (3.749), where Memorability achieved the highest score, while the TOPSIS results highlighted Learnability as the most significant factor.
Optimization of Sentiment Analysis on Tokopedia User Reviews Using Gridsearchcv and Smote with Machine Learning Algorithms Athallah Yasyfi Imran; M. Rudi Sanjaya; Bayu Wijaya Putra; Gabriel Ekoputra Hartono Cahyadi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/5ax8km80

Abstract

Understanding user sentiment from e-commerce reviews is essential for platform improvement and business strategy. This study compares three machine learning algorithms—Logistic Regression, Random Forest, and XGBoost—for sentiment classification of Indonesian-language Tokopedia reviews. A dataset of 6,822 user reviews was preprocessed through tokenization, stopword removal, and TF-IDF vectorization. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training set. Models were evaluated using accuracy, precision, recall, and F1-score. Results demonstrate that Random Forest achieved the highest accuracy at 86.86%, followed by Logistic Regression at 84.86%, and XGBoost at 82.60%. The application of SMOTE significantly improved classification performance across all models, particularly for minority sentiment classes. These findings indicate that tree-based ensemble methods, especially Random Forest, are effective for sentiment analysis in imbalanced e-commerce datasets. This research provides practical insights for e-commerce platforms to implement automated sentiment monitoring systems, enabling faster response to customer feedback and targeted service improvements. However, the study is limited to Tokopedia reviews and may not generalize to other platforms or languages. Future work should explore deep learning approaches and cross-platform validation to enhance model robustness.
Co-Authors Athallah Yasyfi Imran Bayu Wijaya Putra Endang Lestari M. Rudi Sanjaya Muhammad Ravi Wijayanto Sanjaya