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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal CoreIT
Wayan Oger Vihikan
Udayana University
Computer Science & IT Control & Systems Engineering

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Foreign Tourist Arrivals Forecasting Using Recurrent Neural Network Backpropagation through Time Wayan Oger Vihikan; I Ketut Gede Darma Putra; I Putu Arya Dharmaadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i3.5993

Abstract

Bali as an icon of tourism in Indonesia has been visited by many foreign tourists. Thus, Bali is one of the provinces that contribute huge foreign exchange for Indonesia. However, this potential could be threatened by the effectuation of the ASEAN Economic Community as it causes stricter competition among ASEAN countries including in tourism field. To resolve this issue, Balinese government need to forecast the arrival of foreign tourist to Bali in order to help them strategizing tourism plan. However, they do not have an appropriate method to do this. To overcome this problem, this study contributed a forecasting method using Recurrent Neural Network Backpropagation Through Time. We also compare this method with Single Moving Average method. The results showed that proposed method outperformed Single Moving Average in 10 countries tested with 80%, 70%, and 70% better MSE results for 1, 3 and 6 months ahead forecast respectively.
Classifying Indonesian Hoax News Titles with SVM, XGBoost, and BiLSTM I Nyoman Prayana Trisna; I Made Wiraharja Jaya Putra; Wayan Oger Vihikan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.106608

Abstract

This study investigates the automated detection of hoaxes related to President Jokowi in Indonesian news by analyzing only news titles, aiming for efficient detection and reduced traffic to harmful websites. We compared the performance of traditional (SVM, XGBoost) and deep learning (BiLSTM) algorithms, with and without Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance in a dataset scraped from trusted news sources (CNN Indonesia, Detik News) and a fact-checking platform (turnbackhoax.id). The results indicate that BiLSTM generally outperformed SVM and XGBoost, demonstrating the potential of deep learning for this task. However, applying SMOTE negatively impacted BiLSTM's performance, suggesting overfitting. Notably, precision consistently exceeded recall across all models, indicating high reliability in identifying hoaxes but a potential for missing a significant number of actual hoaxes. This highlights a trade-off between avoiding false positives and ensuring comprehensive detection. The findings also suggest that language-specific characteristics influence algorithm effectiveness. This research contributes to developing efficient and accurate tools for combating misinformation in the Indonesian online environment, emphasizing the importance of title-based analysis and careful consideration on data balancing.
Combining BERT and Graph-Based Ranking for Extractive Summarization of Indonesian News Articles I Nyoman Prayana Trisna; Wayan Oger Vihikan; Anis Zahra Nur Azizah
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol. 11 No. 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Automatic text summarization is an effective solution to manage the vast amount of information in the digital age. This study aims to develop an extractive text summarization system for Indonesian news articles using sentence embeddings generated by IndoBERT and mBERT, combined with TextRank and LexRank algorithms for sentence ranking. The dataset used is Indonesian Text Summarization (IndoSum), which contains thousands of manually summarized articles. The research includes data collection, cleaning, preprocessing, embedding extraction, sentence similarity calculation, and ranking using graph-based methods. Model performance was evaluated using ROUGE and BERTScore. The results show that the combination of IndoBERT and LexRank achieved the highest performance with ROUGE-1 score 0.7018 and BERTscore 0.8696. The model was then implemented into a web prototype using Streamlit to allow users to summarize texts interactively. This study contributes to the advancement of automatic summarization technology for the Indonesian language.
Co-Authors Anis Zahra Nur Azizah I ketut Gede Darma Putra I Made Wiraharja Jaya Putra I Nyoman Prayana Trisna I Putu Arya Dharmaadi