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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics Jurnal Informatika dan Teknik Elektro Terapan JOIN (Jurnal Online Informatika) BAREKENG: Jurnal Ilmu Matematika dan Terapan Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Brahmana : Jurnal Penerapan Kecerdasan Buatan Journal La Multiapp Action Research Literate (ARL) International Journal of Electronics and Communications Systems Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science
Zahra, Amalia
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Hybrid Recommendation System Based on Implicit Feedback with Collaborative Filtering and Gradient Boosting Kurniawan, Hendra; Zahra, Amalia
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 2 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112368

Abstract

Recommendation systems are essential components in video streaming services as they assist users in selecting relevant content in line with the increasing availability of large-scale content. However, most recommendation systems still rely on explicit feedback data such as ratings, which are often unavailable on many platforms. This study aims to develop a hybrid recommendation system based on implicit feedback by constructing an interaction score derived from user behavior as a substitute for ratings. The proposed model integrates collaborative filtering methods (matrix factorization and k-nearest neighbor) with the CatBoost gradient boosting decision tree algorithm. The evaluation was conducted using empirical data from a video streaming service, with performance measured using root mean squared error (RMSE) and mean absolute error (MAE). The results indicate that the hybrid model achieves lower RMSE and MAE values compared to individual models. These findings confirm that the hybrid approach is effective in improving recommendation accuracy while also contributing to enhanced user experience quality in video streaming platforms without explicit rating data.
Hybrid CNN–LSTM–Transformer Architectures for Stock Price Prediction: Comparing Sequential and Parallel Integration Strategies Taufik, Mhd; Zahra, Amalia
International Journal of Electronics and Communications Systems Vol. 6 No. 1 (2026): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v6i1.29617

Abstract

Accurate stock price forecasting remains challenging because financial time-series exhibit complex local patterns, long-term temporal dependencies, and rapidly changing market dynamics. Although hybrid deep learning models have demonstrated promising predictive capabilities, limited evidence is available on how different architectural integration strategies influence forecasting performance. This study compares sequential and parallel hybrid CNN–LSTM–Transformer architectures enhanced with attention mechanisms and Bayesian optimization for predicting BBNI stock prices. Historical market data were processed through convolutional, recurrent, and attention-based learning components, while Bayesian optimization was employed to identify optimal hyperparameter configurations. Both hybrid architectures consistently outperformed the baseline CNN–LSTM model, demonstrating substantial improvements in prediction accuracy. The parallel architecture achieved superior performance in minimizing large prediction errors and explaining variance, whereas the sequential architecture produced lower absolute prediction errors with greater computational efficiency. These findings indicate that predictive performance depends not only on model complexity but also on the way deep learning components are structurally integrated. The study provides practical guidance for designing hybrid forecasting architectures that balance predictive accuracy and computational efficiency for financial time-series prediction in emerging markets.
Co-Authors Al Mukarram, Khasyi Cahyadi, Jonathan Caprisiano, Ebenhaiser Jonathan Chandra, Wiryanata David David Fadzlul Rahman, Ferry Faldi, Faldi Farhan Fadhil, Muhammad Fathur Rahman Halim, Rico Harits, Sayekti Hendra Kurniawan Hendra Saputra Henry, Michael Himawan, Randy Joko Pranoto, Wawan Kristian Handoko Wijaya Sukardjoh Martulandi, Adipta Maskur, Maimunah Michael, Stefanus Muhammad Manaqib, Muhammad Mukhlas, M. Anang Naibaho, Agusti Frananda Alfonsus Novika, Andien Dwi Ramadhansyah, Muhammad Hafizh Riftiarrasyid, Mohammad Faisal Roesmiatun Purnamadewi, Yasinta Sams, Andrew Steven Setyoko, Andreas Dimas Suma'inna, Suma'inna Taufik, Mhd Vincent Vincent, Vincent William, Ezra