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All Journal International Journal on Advanced Technology, Engineering, and Information System (IJATEIS) The Indonesian Journal of Computer Science
Wahyu Widyananda
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Machine Learning and Transformer-based Model for Sentiment Analysis of Indonesian E-Commerce Reviews Wahyu Widyananda; Maskur; Ahmad Fauzi
The Indonesian Journal of Computer Science Vol. 14 No. 4 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i4.4980

Abstract

The growth of e-commerce in Indonesia has produced a large volume of user-generated reviews, which contain valuable knowledge for business decisions. However, analyzing this unstructured text data manually is inefficient. The purpose of this study is to improve the performance of sentiment classification on Indonesian e-commerce reviews using machine learning and transformer-based models. The test method is carried out using a public e-commerce review dataset. Three models are evaluated: Multinomial Naïve Bayes, Support Vector Machine (SVM), and IndoBERT. For machine learning models, text pre-processing is performed, and features are extracted using TF-IDF. For the transformer-based model, a fine-tuning approach is used. The results show that the IndoBERT model produces better classification accuracy than the other tested models. For the given dataset, this method obtains 94,1% in accuracy, outperforming both SVM (89,5%) and Multinomial Naïve Bayes (84,2%). The IndoBERT model, despite its higher computational cost, is the most effective for this classification task.
Online Store Product Recommendation System Using Collaborative Filtering and Content-Based Filtering Algorithms to Increase Sales Afandi, Yosi; Maskur, Maskur; Widyananda, Wahyu; Fiernaningsih, Nilawati; Budiarti, Lina; Az Zuhri, Fahmi Muhammad
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 4 No. 3 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v4i3.2007

Abstract

This study aims to evaluate and compare the performance of two recommendation system approaches, namely Collaborative Filtering (CF) and Content-Based Filtering (CBF), in providing relevant product recommendations to users in an e-commerce context. The dataset used consists of 120 data including 90 relevant and recommended products (True Positive), 20 recommended but irrelevant products (False Positive), and 10 relevant but not recommended products (False Negative). Based on the calculation results, both methods show a precision value of 0.818 and a recall of 0.900. This means that approximately 81.8% of products recommended by the system are truly relevant, while 90% of the total relevant products are successfully recommended to users. The F1-score value obtained of 0.857 illustrates a good balance between the accuracy and completeness of the recommendations generated by the system. Furthermore, to measure the level of rating prediction error, the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics are used. The evaluation results show that the CF method has an MSE value of 0.0784 and an RMSE of 0.28, while the CBF method shows an MSE of 0.0961 and an RMSE of 0.31. The lower RMSE value of CF indicates that this method has better accuracy in predicting user preferences than CBF. Overall, both methods show good performance with a low error rate. However, CF proved slightly superior in providing recommendations that match user preferences, so it can be used as a basis for developing smarter and more personalized recommendation systems on e-commerce platforms.
Development of Semantic-Based Voicebots and Natural Language Processing for E-Commerce Product Searches Maskur, Maskur; Afandi, Yosi; Widyananda, Wahyu; Fauzi, Ahmad; Armayrishtya, Zhulvardyan
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 4 No. 3 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v4i3.2008

Abstract

Searching for products online is often an inefficient and confusing process, especially when users do not know the exact name of the product or use terms that differ from the search system. Keyword-based searches tend to produce irrelevant results because the system only matches text literally without understanding the meaning. As users increasingly talk to digital devices, voice-based search technology has become a more natural and intuitive alternative. This research aims to develop a semantic-based voicebot supported by Natural Language Processing (NLP) to improve the effectiveness of product searches on e-commerce platforms. The designed system not only recognizes user speech but also understands the context, intent, and semantic meaning of the given commands. The research stages include collecting user voice data, training the Automatic Speech Recognition (ASR) model for voice-to-text conversion, and applying the semantic NLP model for interpreting the context of product searches. The testing was conducted using Indonesian voice commands in a simulated e-commerce scenario. The results showed that the system achieved an average Word Error Rate (WER) of 1.29%, indicating a high level of accuracy in recognizing speech and understanding user intent. The integration between ASR and semantic NLP proved capable of creating a more natural, responsive search experience that resembles the way humans think and communicate when interacting with online search systems.
Co-Authors Afandi, Yosi Ahmad Fauzi Armayrishtya, Zhulvardyan Az zuhri, Fahmi Muhammad Fauzi Ahmad Muda Fiernaningsih, Nilawati Lina Budiarti, Lina Maskur Maskur Maskur