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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Sebatik Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming JURNAL IT: Media Infromasi STMIK Handayani Makassar Building of Informatics, Technology and Science INFOKUM Journal of Artificial Intelligence and Digital Business TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Azahari, Azahari
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering Public Health

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Customer Sentiment Analysis of E-Commerce Products Using the Naïve Bayes Method and Word Embedding Harpad, Bartolomius; Azahari, Azahari; Salmon, Salmon
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8879

Abstract

This study discusses customer sentiment analysis toward e-commerce products using the Naïve Bayes method combined with Word Embedding techniques to enhance the semantic understanding of Indonesian-language customer reviews. The research background is based on the rapid growth of e-commerce, which has created a strong need to understand consumer opinions through online reviews. The main challenge in sentiment analysis lies in the complexity of natural language, such as the use of informal words, abbreviations, and diverse emotional expressions. This study utilizes 40,607 Tokopedia customer reviews across five product categories with three sentiment labels (positive, neutral, and negative). The research stages include data collection, text preprocessing (case folding, tokenization, stopword removal, stemming, and slang normalization), feature representation using Word2Vec and FastText, and classification using Multinomial Naïve Bayes. Experimental results show that the combination of Word2Vec and Naïve Bayes achieved an accuracy of 87.92%, while FastText and Naïve Bayes improved accuracy to 91.52%. The FastText-based model proved superior in handling morphological variations and non-standard spellings, making it more effective for Indonesian customer review texts. The WordCloud visualization reveals the dominance of positive words such as “sesuai” (appropriate), “barang” (item), and “cepat” (fast), indicating customer satisfaction regarding product conformity and service speed. The Confusion Matrix results indicate a bias toward the positive class due to data imbalance, where the model still struggles to recognize neutral and negative classes. Overall, this study demonstrates that integrating Word Embedding with Naïve Bayes enhances classification performance and provides richer semantic representations compared to traditional Bag of Words approaches. This approach has the potential to be applied in developing data-driven recommendation systems and marketing strategies within Indonesia’s e-commerce ecosystem.
Implementasi Bot WhatsApp untuk Layanan Informasi Frontline: Studi Kasus: STMIK WICIDA Putra, Muhammad Sadam Saktia; Azahari, Azahari; Heny Pratiwi
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp320-326

Abstract

This study implemented a WhatsApp bot as a frontline information service at STMIK Widya Cipta Dharma (WICIDA). The main problems were the high burden of repetitive questions, limited service hours, and inconsistent responses. WhatsApp was chosen because of its high adoption rate and support for real-time communication. The study included needs analysis, bot architecture design, Node.js-based development, knowledge base integration, and performance evaluation. The results showed that the bot was able to answer 87.4% of questions correctly, reduce staff workload by 56%, and speed up response time to <3 seconds. These findings demonstrate that the WhatsApp bot is effective as a scalable solution to improve the quality of educational information services.
Co-Authors Amelia Yusnita Ar Rahman, Nurbalingga Suhendra Ekawati, Hanifah Halim, Ryan Artanto Harianto, Kusno Harpad, Bartolomius Heny Pratiwi Pahrudin, Pajar Pratiwi, Heny Putra, Muhammad Sadam Saktia Rosita, Dewi Salmon Salmon Salmon Salmon, Salmon Sari, Aulia Rahmita Syamsuddin Mallala Ukkas, Irwan Yonatan, Kevin Yulindawati, Yulindawati Yunita Yunita