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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Annual Research Seminar Journal of Information Technology and Computer Science INTEGER: Journal of Information Technology AkMen JURNAL ILMIAH Jurnal Riset Informatika JSAI (Journal Scientific and Applied Informatics) Jurnal Sistem Komputer dan Informatika (JSON) Journal Informatic, Education and Management (JIEM) Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Pengabdian Masyarakat Ekonomi dan Bisnis Digital The Indonesian Journal of Computer Science Buffer Informatika
Ali Ibrahim
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Analisis Sentimen Aplikasi MPStore Menggunakan Algoritma Logistic Regression dan LDA Tia Arlin Dita; Ali Ibrahim; Rizka Rahmadhani; Mira Afrina
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9557

Abstract

The rapid growth of the digital economy encourages user satisfaction as the key to successful application innovation. Within technopreneurship, understanding user sentiment is essential for sustainable product development. This study aims to analyze sentiment and identify the deter-minants of user satisfaction regarding the MPStore application based on reviews from the Google Play Store. Review data were collected via scraping and analyzed using Logistic Regression (LR) for sentiment classification (positive, negative, neutral) also Latent Dirichlet Al-location (LDA) for satisfaction topic extraction. The result shows that the LR model achieved an accuracy of 88.5%. The LDA analysis also successfully revealed eight main topics, includ-ing ease of use, transaction speed, and technical obstacles (errors, login, balance issues). Over-all, a majority of users hold a positive perception of MPStore's efficiency and ease of transac-tions. This study concludes that the combination of sentiment analysis and topic modeling is effective for explaining the level of user satisfaction and providing a strategic foundation for digital application developers.
The Sentiment Analysis Of Indonesian Startup Application Reviews Using TF-IDF+SVM and FastText: A Comparative Study Aini Nabilah; Nurlayli Indah Sari; Mira Afrina; Ali Ibrahim
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103807

Abstract

The rapid rise of startups in Indonesia makes user reviews on the Google Play Store a valuable data source for understanding user perceptions and satisfaction. These unstructured reviews contain insights supporting product development and business strategies. This study analyzes sentiments in Indonesian startup app reviews and compares two classification methods: TF-IDF + Linear SVM and fastText, implemented using Google Colab. Reviews were collected in September 2025 using google-play-scraper; 4,000 reviews were retrieved and refined into 3,152 unique reviews after cleaning and preprocessing. Sentiment labeling used ratings (1–2 negative, 4–5 positive); because the neutral class was limited, this study focuses on balanced binary classification with 1576 positive and 1576 negative reviews. The process involves data scraping, text preprocessing, model training, and evaluation using accuracy, precision, recall, and F1-score metrics, with Linear SVM chosen as an efficient baseline for high-dimensional sparse TF-IDF features. Results show that fastText achieves 91.88% accuracy and an F1-macro of 0.9184, slightly outperforming TF-IDF + SVM (F1-macro 0.9103), suggesting that the embedding-based approach better captures semantic nuances of Indonesian text. Future work may extend this study to ABSA to assess sentiments toward price, UI/UX, and customer service for deeper technopreneurship insights in Indonesia.
Co-Authors Abdiansah, Abdiansah Adella Salsabila Adhityah Anugrah Ahmad Fali Oklilas Aini Nabilah Al Farissi Albert Amadeus Valentino Ali Bardardi Alya Nurseptiani Andi Marlinah Apriansyah Putra - Asyrof Fitrah Aulia Najibah Putri Budi Astuti Dwi Citra Hartini Dwi Rosa Indah Endang Lestari Ruskam Endang Lestari Ruskan Endang Lestari Ruskan Ermatita - Eti Arini Fathoni Fathoni - Hendi Putra Wijaya Jaidan Jauhari Ken Dihta Tania Khairunnisa Larasathi Jiwany Marini . MGS. Afriayan Firdaus Michelle Liu Mira Afrina Muhamad Lutfi Azizan Mutia Sahira Nadya Angelia Nurlayli Indah Sari Nyimas Diah Permata Sari Nyimas Sriwihajriyah Ovi Dyantina Putri Amalia Rafika Maulindah Redha Bayu Anggara RIA ANGGRAINI Rizka Dhini Kurnia Rizka Rahmadhani Rizki Artinio Permana Putra Ruth Christin Aprilia Situmorang Salsabila, Shofi Salsabila, Unik Hanifah Septiano Alvian Ismau Solly Aryza Tia Arlin Dita Tumpol S Simarmata Vira Putri Rahmadini Windhi Tia Saputra Yadi Utama Yadi Utama, Yudha Pratomo Yusmaniarti Zurnan Alfian