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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Sebatik JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Aptisi Transactions on Technopreneurship (ATT) METIK JURNAL TEPIAN Buletin Poltanesa Informatika Wicida BEduManageRs Journal : Borneo Educational Management and Research Journal
Heny Pratiwi
Teknik Informatika, STMIK Widya Cipta Dharma
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Sentiment Analysis Using the Naïve Bayes Method to Improve E-Commerce Customer Satisfaction at the PedagangAksesoris Store Bai' Fathur Rayhan; Heny Pratiwi; Muhammad Fahmi
Poltanesa Vol 26 No 2 (2025): December 2025
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

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Abstract

The rapid development of the e-commerce sector in Indonesia has made customer feedback a very important source of information in assessing the quality of goods and services. However, with so many reviews available, the manual assessment process often becomes complicated. The purpose of this study is to analyze customer sentiment towards PedagangAksesoris store on the Shopee platform using the Naïve Bayes Classifier method to identify positive and negative opinions that can help improve customer satisfaction. The data for this study was collected through web scraping of Shopee user reviews, followed by a preprocessing stage that included cleaning, filtering, removing affixes, and separating words. The data was then divided into training data and testing data to train and test the model. The Naïve Bayes method was applied by calculating word probabilities using Laplace smoothing, while model performance was evaluated using a Confusion Matrix through the RapidMiner application. The results of this study show that the Naïve Bayes model can classify customer reviews with a high degree of accuracy, with precision reaching 100% for the negative category and 80% for the positive category, as well as recall of 87.5% and 100%. These findings confirm that the Naïve Bayes method is an effective and efficient way to perform text-based sentiment analysis on reviews in e-commerce. The results of this sentiment analysis can be used as a basis for strategic decision-making by businesses to improve product quality, services, and customer satisfaction.  
Co-Authors Ahmad Abul Khair Ahmad Fajri Ahmad Rofiq Hakim Amelia Yusnita Ananta Putra, Resifa Andi Yusika Rangan Arsita Ashari Ramadani Azahari Bai' Fathur Rayhan Chandra Panca Wibawa Dana Aulia Rahman Daru Caraka Dendy Kurniawan Dessy Purnamasari Ekawati, Hanifah Fadjri Astra Ryan Sinurat Ita Arfyanti M Yuditia Arfiyanto T Nassa M. Irwan Ukkas Irwan Ukkas Ukkas Muhammad Fachri Sanjaya Muhammad Fahmi Muhammad Ibnu Saad Saad Muhammad Rega Praduana Nursobah, Nursobah Pahrudin, Pajar Pitrasacha Adytia Putra, Resifa Ananta Putri Wulandari SA'AD, MUHAMMAD IBNU Salmon Salmon Sarifmata Purnomo Shinta Palupi Susi Salviati Syamsuddin Mallala