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All Journal International Journal of Artificial Intelligence for Digital Marketing
Andry Rachmadany
Muhammadiyah University of Sidoarjo, Indonesia
Economics, Econometrics & Finance Environmental Science

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SENTIMENT ANALYSIS OF INDRIVE APP USAGE REVIEWS ON GOOGLE PLAYSTORE USING SUPPORT VECTOR MACHINE (SVM) AND NAÏVE BAYES ALGORITHM Afifani Aulida Romadhoni; Andry Rachmadany; Bayu Hari Prasojo
International Journal of Artificial Intelligence for Digital Marketing Vol. 2 No. 10 (2025): International Journal of Artificial Intelligence for Digital Marketing
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ijaifd.v2i10.421

Abstract

Objective: This study aims to analyze user sentiment toward the InDrive application on Google Play Store by employing Support Vector Machine (SVM) and Naïve Bayes algorithms, motivated by the increasing number of user reviews that are difficult to evaluate manually, thus requiring a text mining approach to efficiently classify opinions into positive and negative categories. Method: A dataset of 30,000 reviews was collected through web scraping, and the analysis involved several stages, including preprocessing (cleaning, case folding, normalization, tokenizing, stopword removal, and stemming), term weighting using TF-IDF, and classification using SVM and Naïve Bayes. Results: The results revealed that SVM outperformed Naïve Bayes with an accuracy of 78%, precision of 0.80, and recall of 0.74, whereas Naïve Bayes achieved 76% accuracy, 0.79 precision, and 0.70 recall, indicating that SVM is more effective in handling complex user review data compared to Naïve Bayes. Novelty: The novelty of this research lies in applying a comparative study of the two algorithms to InDrive application reviews, which has not been extensively explored, and is expected to provide insights for developers to better understand user perceptions and improve the quality of application services.
THE EFFECT OF LIVE STREAMING, RATINGS, AND PRODUCT REVIEWS ON PURCHASING DECISIONS FOR COMPASS SHOE PRODUCTS IN THE SHOPEE APPLICATION M. Ivan Imanulloh Khaqi; Mochamad Rizal Yulianto; Andry Rachmadany
International Journal of Artificial Intelligence for Digital Marketing Vol. 2 No. 10 (2025): International Journal of Artificial Intelligence for Digital Marketing
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ijaifd.v2i10.422

Abstract

Objective: This study aims to analyze the effect of the live streaming feature, product ratings, and product reviews on purchasing decisions for Compass shoes in the Shopee application. Method: Using multiple linear regression techniques, the study identified that all independent variables (live streaming, ratings, and product reviews) have a positive and significant relationship to purchasing decisions, and the research instrument proved to be reliable with Cronbach's Alpha and Composite Reliability values above 0.7 for all constructs. Results: The analysis results show that the product review variable (X3) has the most significant influence, while live streaming (X1) has the smallest influence, and the research model shows moderate predictive power of purchasing decisions (R Square=0.528). Novelty: This finding confirms the importance of reviews, ratings, and real-time interactions in encouraging consumers to make purchasing decisions in digital marketplaces.
Co-Authors Afifani Aulida Romadhoni Bayu Hari Prasojo M. Ivan Imanulloh Khaqi Mochamad Rizal Yulianto