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All Journal Nuras : Jurnal Pengabdian Kepada Masyarakat International Journal of Artificial Intelligence for Digital Marketing
Romadhoni, Afifani Aulida
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Humanities Economics, Econometrics & Finance Education Environmental Science Social Sciences

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SENTIMENT ANALYSIS OF INDRIVE APP USAGE REVIEWS ON GOOGLE PLAYSTORE USING SUPPORT VECTOR MACHINE (SVM) AND NAÏVE BAYES ALGORITHM Romadhoni, Afifani Aulida; Rachmadany, Andry; Prasojo, Bayu Hari
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.
Optimalisasi Strategi Pemasaran Properti Menggunakan Platform Nuelink pada PT. Dewe Makmur Mapan Romadhoni, Afifani Aulida; Rachmadany, Andry
Nuras : Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 2 (2026): April (In Progress)
Publisher : Lembaga Pendidikan, Penelitian, dan Pengabdian Kamandanu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/nuras.v6i2.1078

Abstract

Businesses in the increasingly competitive property industry must structuredly and efficiently optimize their digital marketing strategies. PT. Dewe Makmur Mapan faces several key issues. These include inconsistent content publication, limited promotional video production, and underutilization of the Nuelink social media management platform to support effective marketing. The purpose of this community service is to optimize the scheduling, production, and evaluation of digital content using a participant observation approach. This approach includes problem identification, strategy implementation, and content performance analysis. Improved promotional video production and editing with CapCut, AI-based copywriting, structured scheduling and publication with Nuelink, and the utilization of analytical features for data-driven evaluation, among other solutions, were explored. The results of the activity showed increased upload consistency, content variety, audience engagement (likes, views, and comments), and more consistent follower growth. The division of roles between paid advertising and organic content management improved the effectiveness of marketing operations. Based on the results of the activity, it was concluded that optimizing social media management platforms simultaneously can increase audience engagement, promotional reach, and the effectiveness of digital marketing for property businesses.
Co-Authors Bayu Hari Prasojo Rachmadany, Andry