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All Journal Journal of Information Technology and Computer Science (JOINTECS) Jurnal Ilmiah FIFO SMARTICS Journal J I M P - Jurnal Informatika Merdeka Pasuruan Conference on Innovation and Application of Science and Technology (CIASTECH) SPIRIT Jurnal Aplikasi Dan Inovasi Ipteks SOLIDITAS Prosidia Widya Saintek Jurnal ilmiah teknologi informasi Asia JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Fitri Marisa
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Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Social Sciences Transportation

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DistilBERT-Based E-Commerce Sentiment Analysis Zahri Aksa Dautd; Aviv Yuniar Rahman; Fitri Marisa
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 2 (2026): Vol. 3 No. 2 (2026): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

The rapid advancement of digital technology has driven significant growth in Indonesia’s e-commerce sector, with Shopee emerging as one of the largest platforms generating millions of product reviews daily. These reviews contain valuable consumer opinions that can be analyzed to assess customer satisfaction, yet their massive volume makes manual analysis inefficient and subjective. This study aims to develop an automated sentiment analysis model using DistilBERT to classify Shopee product reviews into positive and negative sentiments. The dataset comprises approximately 1 million Englishlanguage reviews covering various product categories, including electronics, fashion, beauty, and household items. The research methodology involves text preprocessing, tokenization using DistilBertTokenizerFast, and fine-tuning of the DistilBERT model under multiple data-split ratios (90:10, 80:20, 70:30, 60:40). Experimental results demonstrate that DistilBERT achieved the highest accuracy of 94.8%, outperforming baseline models such as Naïve Bayes (88.4%) and SVM (89.6%). These findings confirm that DistilBERT effectively maintains a balance between accuracy, precision, and recall while offering high computational efficiency. This research contributes both methodologically and practically by establishing DistilBERT as a scientifically robust and resource-efficient solution for large-scale sentiment analysis in Indonesia’s e-commerce environment.
Co-Authors Abi Zahma Adi Semri Neno Aditya - Aditya Anggun Adrianus Muit Bau Ahmad Farhan Almaukar Anastasia Anastasia L Maukar Anik Yuesti Aviv Yuniar Rahman Dedi Eryan Subakti Dwi Purnomo Egy Noviansa Erdian Ari Widodo Fachri Ayudi Fitrony fangqi Arifin Faqih Rofi'i Hamdan Yuwafi Ilhamsyah I Inayati Sa'adah Indra Darma Wijaya Indra Dharma Wijaya Irfan Agus Istiadi Jefri Angga Maulana Junianto Ismail Misbahul Munir Mochamad Tri Anjasmoros Muhammad Agus Sahbana Muhammad Januar Wicaksono Nita Anastasya Putri Indah Purnamasari Rangga Pahlevi Putra RIa Diajeng Anita Rifqy Abdillah Rivaldo Tito L Dasilva Robby Muktiyanto Ryan Afilusuf Sabilillah Alfath Sandi probo sarjono Silviana Silviana Sofyan huda Syahroni Wahyu Iriananda, Syahroni Wahyu Wardianto - Yudid Nuriantono Yuliana - Zahri Aksa Dautd