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All Journal Jurnal Pustakawan Indonesia Jurnal INSYPRO (Information System and Processing) JURNAL INSTEK (Informatika Sains dan Teknologi) Journal Software, Hardware and Information Technology Jurnal Pustakawan Indonesia Innovative: Journal Of Social Science Research Journal of Embedded Systems, Security and Intelligent Systems System Information and Computer Technology (SYNCTECH)
Ibrahim, Firmansyah
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Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Extracting Value from Minority Voices: Epistemic Validation of Naive Bayes and SMOTE Models for E-Commerce Review Sentiment Analysis Ibrahim, Firmansyah; Prasetya, Didik Dwi; Patmanthara, Syaad
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.10275

Abstract

In the e-commerce ecosystem, negative customer reviews, despite often being a numerical minority, represent the most valuable (axiological) business asset for service improvement. However, this value is frequently obscured by the high volume of positive reviews, creating a significant imbalance in the data. This study aims to design and validate a text mining model that is axiologically focused on extracting critical insights from this "minority voice." We applied the Naive Bayes Classifier (NBC) algorithm, augmented with TF-IDF feature weighting, on a dataset of 6,000 reviews from the 'Famous Florist' store. The epistemic challenge of severe data imbalance (5,432 positive vs. 97 negative) was addressed through the methodological intervention of the Synthetic Minority Over-sampling Technique (SMOTE). The model's validity was assessed using 10-Fold Cross-Validation. The epistemic validation results demonstrated the model's validity, achieving an average accuracy of 90%. Crucially, the model achieved a 99% rate for the negative class. This affirms the model's axiological validity: its ability to reliably identify customer complaints (e.g., 'damaged,' 'packaging') and transform raw data into actionable recommendations for improvement.
Co-Authors Agus Buono Akib, Faisal ANTAMIL, ANTAMIL Asminarni, Asminarni Asrul Azhari Muin, Asrul Azhari Didik Dwi Prasetya Dillah, Nur Fadilah Rahmat Erfina Irman Hermadi Ismi, Nur Katili, Andi Yusuf Nahrun Hartono, Nahrun Nur Inayah Prabujaya, Ilham Rachman, Ellys Rahman Rahman Rahman Reza Maulana Rizaldy, Adhy Sri Wahyuni Syaad Patmanthara Syah, Herwan