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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Engineering, Mathematics and Computer Science Journal (EMACS)
Priccilia, Shania
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Indonesian generative chatbot model for student services using GPT Priccilia, Shania; Girsang, Abba Suganda
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i1.pp50-56

Abstract

The accessibility of academic information greatly impacts the satisfaction and loyalty of university students. However, limited university resources often hinder students from conveniently accessing information services. To address this challenge, this research proposes the digitization of the question-answering process between students and student service staff through the implementation of generative chatbot. A generative chatbot can provide students with human-like responses to academic inquiries at their convenience. This research developed generative chatbot using pre-trained GPT-2 architecture in three different sizes, specifically designed for addressing practicum-related questions in a private university in Indonesia. The experiment utilized 1288 question-answer pairs in Indonesian and demonstrated the best performance with a BLEU score of 0.753, signifying good performance accuracy in generating text despite dataset limitations.
Sentiment Analysis of Slang Language Trends in Generation Alpha on Social Media Using BERT Priccilia, Shania; Erin, Erin
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13368

Abstract

Generation Alpha is a group growing up in an era of rapid digital technology advancement. Unlike previous generations who experienced a transition into technology, Generation Alpha demonstrates unique communication characteristics, particularly in their frequent use of slang, which is often difficult for older generations to understand. This gap in language understanding can lead to miscommunication, especially when the meaning of slang is misinterpreted. This phenomenon presents a challenge in establishing intergenerational communication, especially in digital and social media contexts where informal language is dominant. This study aims to explore the effectiveness of AI models in analyzing the sentiment of slang language used by Generation Alpha. Three BERT-based models were utilized in this research: BERT, RoBERTa, and DistilBERT. These models were selected based on their performance and efficiency in natural language processing (NLP) tasks, particularly in text classification and sentiment analysis. The dataset consists of 24,958 slang-based posts collected from users on the social media platform X. The analysis shows that DistilBERT achieved the highest accuracy score of 0.83, followed by BERT (0.82) and RoBERTa (0.81). These findings suggest that BERT-based models, especially DistilBERT, perform reliably in identifying the sentiment behind slang expressions used by Generation Alpha and hold potential for implementation in AI-based moderation or social media monitoring systems.
Co-Authors Abba Suganda Girsang, Abba Suganda Erin, Erin