Article Per Year (5 Year)
p-Index From 2021 - 2026
1.456
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal CoreIT JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics Jurnal Ilmiah Teknologi dan Komputer (JITTER)
Wayan Oger Vihikan, Wayan Oger
Udayana University
Computer Science & IT Control & Systems Engineering

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Journal of Information Systems and Informatics

 1 
Sentiment Analysis of X (Twitter) Comments on The Influence of South Korean Culture in Indonesia Savitri, Putu Rheya Ananda; Suarjaya, I Made Agus Dwi; Vihikan, Wayan Oger
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.749

Abstract

Hallyu or Korean wave refers to the phenomenon of South Korean values and culture spreading to other countries, ultimately influencing global culture. South Korean culture, such as K-pop music, dramas, films, fashion, food, and lifestyle, has gained popularity in Indonesia since 2002. Because South Korean culture influences many aspects of life in Indonesia, responses to this Korean wave are widely discussed in social media, especially through X (Twitter) ranging from positive sentiment to negative sentiment. To gain a more in-depth and detailed understanding of public opinion, a classification process was conducted on the social media platform X (Twitter) using a deep learning algorithm based on the CNN method. The results of this classification provide more accurate and informative insight into the attitudes, opinions, and reactions of the Indonesian people towards the influence of South Korean culture in this country. The research was conducted using 717,998 tweet data resulting in an accuracy of 79%.
Sentiment Analysis of Indonesian Citizens on Electric Vehicle Using FastText and BERT Method Wijaya, Darryl Rayhan; Sasmitha, Gusti Made Arya; Vihikan, Wayan Oger
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.784

Abstract

Electric vehicles have become one of the most important innovations in the automotive industry in recent years. This is not only related to technological developments, but also to its significant impact on the environment and lifestyle of global society. Lot of people do not know about the benefit of using electric vehicles for our environment. The transition from conventional vehicles to electric vehicles can really make our environment healthier and also reducing the pollution. At the same time, debates and feelings about electric vehicles continue to grow around the world. This study aims to understand the dynamics of people's feelings and opinions about electric vehicles through sentiment analysis using the FastText and IndoBERT methods. FastText is an efficient text classification and representation learning method developed by Facebook's AI Research (FAIR) lab. IndoBERT is a pre-trained language model specifically designed for the Indonesian language, leveraging the Bidirectional Encoder Representations from Transformers (BERT) architecture. By analyzing a total of 119,310 data from January 2020 to June 2023, the tweets data were categorized into negative, neutral, and positive classes. Model yielded the highest accuracy of 82.5% using IndoBERT method. The results outcomes positive perceptions of electric vehicles among Indonesian citizen with a percentage of 58%. By carrying out this research, it is hoped that it can produce quality information for producers, the community and the government in developing and advancing public interest in purchasing electric vehicles considering the very positive impact they have on the surrounding environment.
Indonesian Health Question Multi-Class Classification Based on Deep Learning Vihikan, Wayan Oger; Trisna, I Nyoman Prayana
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.838

Abstract

The health online forum is commonly used by Indonesian to ask questions related to diseases. A well-known example, Alodokter, has hundreds of thousands of health questions which are assigned to certain topics. Building a model to classify questions into a topic is important for better organization and faster response by relevant health professionals. This research experimented on 20 deep learning methods from RNN, CNN, and IndoBERT with different configurations to see the performance of each model when classifying questions into six different most common diseases that cause death in Indonesia. The results show the majority of the model can outperform the SVM as baseline. Bidirectional RNN such BiLSTM and BiGRU combined with CNN show a good metric score even though a certain version of the IndoBERT model generally outperforms all the other models.
Implementation of a Telegram-Based Child Consultation Chatbot Using IndoBERT Whurapsari, Gusti Ayu Wahyu; Suarjaya, I Made Agus Dwi; Vihikan, Wayan Oger
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1079

Abstract

Children’s health and development are crucial aspects that require proper attention from parents. However, many parents lack easy access to immediate consultation regarding their child's health and well-being. To address this issue, this study develops a child consultation chatbot on Telegram using the IndoBERT model. The chatbot utilizes data from Halodoc and Alodokter, structured into an intent-based format with 227 tags, 5,428 patterns, and 278 responses. The dataset undergoes preprocessing, including lowercasing, text cleaning, normalization, stopword removal, and stemming. Four preprocessing scenarios are tested, including the use of term frequency-based stopwords without applying stemming, the use of NLTK stopwords without stemming, the use of term frequency-based stopwords combined with stemming, and the use of NLTK stopwords combined with stemming. The best model, trained with an 80:20 training-validation split using term frequency-based stopwords without stemming, achieves 98% accuracy, 98.5% F1-score, 98.9% precision, and 98.5% recall. The chatbot successfully classifies user intent and ensures structured interactions through a confidence-based response mechanism. This research demonstrates that an IndoBERT-based chatbot can effectively assist parents in obtaining quick and relevant information regarding their children's health and development.
Co-Authors Azizah, Anis Zahra Nur Dharmawan, I Putu Yogi Prasetya I Made Agus Dwi Suarjaya Putra, I Made Wiraharja Jaya Salsabila, Archels Ramadhany Sasmitha, Gusti Made Arya Savitri, Putu Rheya Ananda Trisna, I Nyoman Prayana Utami, Putu Mia Setya Whurapsari, Gusti Ayu Wahyu Wijaya, Darryl Rayhan