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All Journal Jurnal ULTIMA InfoSys Jurnal ULTIMATICS Proceeding National Conference Business, Management, and Accounting (NCBMA) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Feliks Victor Parningotan Samosir
Universitas Pelita Harapan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology Social Sciences

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Journal : Jurnal ULTIMATICS

 1 
Sentiment Analysis in E-Commerce: Beauty Product Reviews Tumanggor, Gavrila Louise; Samosir, Feliks Victor Parningotan
Ultimatics : Jurnal Teknik Informatika Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3708

Abstract

The increasing popularity of online shopping platforms is fueling the need for automated sentiment analysis for product reviews. This research aims to build an automatic sentiment analysis model in Indonesian for e-commerce product reviews. This model is expected to help consumers make purchasing decisions more quickly. We utilize the IndoBERT model, which has shown to be quite effective for general sentiment analysis, achieving an evaluation accuracy of 66.2% despite a high evaluation loss of 0.8006. The approach used combines Natural Language Processing (NLP) and Machine Learning (ML) techniques. It is hoped that this research will be useful for consumers, shop owners, and researchers in efficiently understanding the sentiment of e-commerce product reviews.
Sentiment Analysis in E-Commerce: Beauty Product Reviews Tumanggor, Gavrila Louise; Samosir, Feliks Victor Parningotan
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3708

Abstract

The increasing popularity of online shopping platforms is fueling the need for automated sentiment analysis for product reviews. This research aims to build an automatic sentiment analysis model in Indonesian for e-commerce product reviews. This model is expected to help consumers make purchasing decisions more quickly. We utilize the IndoBERT model, which has shown to be quite effective for general sentiment analysis, achieving an evaluation accuracy of 66.2% despite a high evaluation loss of 0.8006. The approach used combines Natural Language Processing (NLP) and Machine Learning (ML) techniques. It is hoped that this research will be useful for consumers, shop owners, and researchers in efficiently understanding the sentiment of e-commerce product reviews.
Sentiment Analysis of University X Students: Comparing Naive Bayes and BERT Approaches David, Jonathan; Saputra, Kie Van Ivanky; Panjaitan, Andry Manodotua; Samosir, Feliks Victor Parningotan
ULTIMATICS Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i2.4034

Abstract

Student satisfaction with university facilities and services requires in-depth analysis to ensure improvements in unsatisfactory facilities or services while maintaining those that meet expectations. This study aims to analyze sentiment in student satisfaction surveys using Natural Language Processing (NLP) methods. Survey data collected from 2022 to 2024 were analyzed using two main approaches: Naive Bayes (NB) with n-grams (n=1,2,3) employing feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW), and Bidirectional Encoder Representations from Transformers (BERT). The analysis results indicate that BERT outperforms NB in terms of sentiment prediction accuracy, although the difference is not highly significant. This study also identified keywords for both positive and negative sentiments. These keywords were then analyzed across 11 categories of facilities and services to provide focused insights into aspects that need to be maintained or improved. This study concludes that sentiment analysis provides significant contributions to universities in evaluating and enhancing the quality of facilities and services according to student preferences.
Co-Authors Albertus Indarko Wiyogo Andreas Widjaja Anggara, Erik Dwi David, Jonathan Erik Dwi Anggara Hapnes Toba Hapnes Toba Haryani, Calandra Alencia Hery Hery Hery Hudi, Robertus Jessica, Vira Kie Van Ivanky Saputra Kurniawan, Matthew Loudry Palmarums Mustamu Mewati Ayub Mewati Ayub Mitra, Aditya R. Mustamu, Loudry Palmarums Panjaitan, Andry M. Riechie, Riechie Tarigan, Riswan Effendi Tumanggor, Gavrila Louise Widjaja, Andree E. Wiyogo, Albertus Indarko