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All Journal Elkom: Jurnal Elektronika dan Komputer INFOKAM Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sains dan Informatika Journal of Information Systems and Informatics bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Digit : Digital of Information Technology JUTECH : Journal Education and Technology Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Education Technology Information Social Sciences and Health Jurnal Sintaks Logika (JSilog) Jurnal Indonesia Sosial Sains JAMU : Jurnal Abdi Masyarakat UMUS Innovative: Journal Of Social Science Research Jurnal Teknik Indonesia Jurnal Multidisiplin Teknologi dan Arsitektur Journal of Citizen Research and Development
Otong Saeful Bachri
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muhadi Setiabudi, Brebes, Indonesia
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Classification of Sentiment of Emina Product Reviews Using the Naive Bayes Algorithm Wiwik Astriani; Otong Saeful Bachri; Bambang Irawan
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3554

Abstract

The rapid development of e-commerce in Indonesia has led to an increase in the number of consumer reviews containing opinions and experiences of using products. In the cosmetic product category, text reviews have an important role in influencing purchasing decisions. However, the large volume of data and the imbalance of sentiment distribution are the main challenges in conducting manual and accurate sentiment analysis. Therefore, an automated approach based on machine learning is needed that is efficient and capable of handling large-scale and unbalanced data. This study aims to analyze the sentiment of reviews of Emina brand cosmetic products on the Tokopedia platform and evaluate the effectiveness of the Multinomial Naïve Bayes algorithm combined with TF-IDF and SMOTE data balancing techniques in classifying positive, neutral, and negative sentiments. The research data was obtained through web scraping of Emina product reviews, resulting in 446,325 review data. The research stages include text preprocessing, rule-based sentiment labeling, feature extraction using TF-IDF, data balancing using SMOTE, and classification modeling with the Naïve Bayes Multinomial algorithm. Model performance evaluation was carried out using accuracy, precision, recall, F1-score, and confusion matrix metrics. The test results showed that the model achieved an accuracy of 94.72% with a stable F1-score value in all sentiment classes, including minority classes, after the implementation of SMOTE. This study proves that the combination of Multinomial Naïve Bayes, TF-IDF, and SMOTE is effective for large-scale analysis of cosmetic product review sentiment and is able to significantly overcome the problem of data imbalance.
Co-Authors Abdi R, Aulia Agung Permana, Tegar Agyztia Premana Ahmad Najmi Fuadi Almesta, Adhika Yameraja Andi Yulianto Anin, Anindia Anindia Ardiani Apriliani, Rosema Dina Ariestanto Ramdhan, Nur Badruzzaman, Ni'am Bagus W, Teguh Bambang Irawan Bambang Irawan Bambang Irawan /E21207042 Bhakti, M.Herdian Budiasih, Kristanti Catur Edi Widodo Dumadi Dumadi Elis Afriyani Fery Sofian Efendi Fitro, Achmad Gagas Prasetya Ghaffar, Dzaky Abdul Gian Fitralisma Harliana Harliana Harliana Harliana Hasan, Sarah Irmansyah, Teguh Bagus Wicaksono Iza Azkiah Khalid Iskandar Khalid Iskandar Mubarok, Muhammad Zaqi Muhami, Rizal Muhammad Bagus Irwana Aji Muhammad Lutfi Jamaludina Mukson Mukson Nadia Nur Alifiana Nur Ariesanto Ramdhan Nur Ariesanto Ramdhan Nurfikri, Muhammad Ihsan Oky Dwi Nurhayati Pandhu Wijaya, Akhmad Pratama Putra, Rizki Premana, Agyztia Raden Mohamad Herdian Bhakti Ramdhan, Nur Ariesanto Rifqoh Febriyanti Putri Riono, Slamet Bambang Rizky Esa Riyanto Roby Setiadi Roby Setiadi Rohman, Aulia Abdi Rudianto Rudianto Shahib, M Umar Sri Yulianti Susilo, Bayu Syaogi, Moh. Tegar Agung Permana Teuku Rizal Adi Pangestu Tri S, Wananda Wahyuningsih, Puji Wiwik Astriani Yenny Ernitawati Yuhano Yuhano