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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JUITA : Jurnal Informatika Jurnal Informatika Universitas Pamulang Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Pendidikan dan Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT
Theresia Ratih Dewi Saputri
Universitas Ciputra
Religion Agriculture, Biological Sciences & Forestry Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Social Sciences Other

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Journal : IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

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Detecting YouTube Clickbait with Transformer Models: A Comparative Study Samuel, Bryan; Saputri, Theresia Ratih Dewi
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.111977

Abstract

Clickbait remains a common strategy on YouTube, where video titles are often crafted to maximize viewer engagement. Although transformer-based machine learning technologies have advanced rapidly, studies that specifically investigate clickbait in YouTube video titles are still rare, even though such titles have unique linguistic characteristics that are shorter, more informal, and more ambiguous than news headlines or other social media texts. This study compares three Transformer models, namely BERT, RoBERTa, and XLNet, for the task of clickbait detection using two benchmark datasets. Each model was fine-tuned and evaluated using standard classification metrics, with additional analyses on training and inference efficiency. The results show that all three models achieved accuracy above 95 percent. RoBERTa achieved the best performance on the Chaudhary dataset (99.84 percent), while BERT cased performed best on the Vierti dataset (96.91 percent). In contrast, XLNet lagged in both accuracy and computational efficiency, with inference times exceeding six seconds per batch. This study demonstrates a 1.31 percent improvement in accuracy compared to previous SVM-based methods and provides a comprehensive evaluation of three Transformer architectures in the YouTube context, offering empirical guidance for more effective clickbait detection.
Machine Learning Approaches for Predicting Seasonal Stock Trends Gunawan, Jason Miracle; Andreas, Christopher; Saputri, Theresia Ratih Dewi
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.112504

Abstract

The financial market is vital for economic growth yet it often experiences volatility, particularly in Indonesia’s transportation sector where stock prices are strongly affected by seasonal fluctuations. Conventional forecasting methods often neglect these recurring patterns, lowering predictive accuracy. This study assesses the capability of Machine Learning algorithms to capture seasonality in stock price prediction, using PT Garuda Indonesia (Persero) Tbk (GIAA.JK)’s monthly data from August 2019 to May 2025, retrieved from Yahoo Finance. Four models–Linear Regression, Extreme Gradient Boosting (XGBoost), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)–were trained and tested, with performance evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Hyperparameter tuning was applied to XGBoost, LSTM, and GRU, while statistical validation employed the Kruskal-Wallis test. Results showed that the tuned GRU outperformed other models, achieving MAE of 5.90, RMSE of 7.33, and MAPE of 9.67%, demonstrating ‘excellent’ accuracy in modelling both short-term and seasonal dynamics. These findings highlight the superiority of GRU in modelling both short-term fluctuations and long-term seasonal dependencies in stock prices. The results contribute practical insights for investors and emphasize the importance of integrating seasonality in predictive models for volatile sectors
Co-Authors Andreas, Christopher Aprillio, Daniel Atmadjaja, Anna Bella Bryan Edwin Kurniawan Evan Tanuwijaya Felicia Graciella Gunawan, Jason Miracle Laura Mahendratta Tjahjono Lestari, Caecilia Citra Maximus Aurelius Wiranata Michelle Chandra Mychael Maoeretz Engel Rinabi Tanamal Salmon Charles Siahaan Samuel, Bryan Sutanto, Richard Evan Tony Antonio Trianggoro Wiradinata Wijaya, Mychael Wiryonoputro, Tinara Nathania Yaurentius, Evelyn Callista Yosua Setyawan Soekamto Yuwono Marta Dinata