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INDONESIA
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Published by Universitas Gadjah Mada
ISSN : 19781520     EISSN : 24607258     DOI : https://doi.org/10.22146/ijccs
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Indonesian Journal of Computing and Cybernetics Systems (IJCCS), a two times annually provides a forum for the full range of scholarly study . IJCCS focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and eveolutionary computation, so that more intelligent system can be built to industrial applications. The topics include but not limited to : fuzzy logic, neural network, genetic algorithm and evolutionary computation, hybrid systems, adaptation and learning systems, distributed intelligence systems, network systems, human interface, biologically inspired evolutionary system, artificial life and industrial applications. The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 10 Documents
 1 
Search results for , issue "Vol 19, No 4 (2025): October" : 10 Documents clear
Predicting Resale Prices using Random Forests with Fine-Tuning Hyperparameters Widjaja, Herman; Perdana, Nanda; Wasito, Ito
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.103967

Abstract

The accurate prediction of housing prices is essential for informed decision-making by purchasers, sellers, and policymakers in dynamic real estate markets. This study investigates the application of machine learning models—Random Forest, XGBoost, Decision Tree, and LightGBM—to predict resale flat prices in Singapore. It provides valuable insights into the use of machine learning in housing markets, particularly for datasets with similar size, complexity, and data types. The objectives are to develop predictive regression models for property prices and to analyze and compare the performance of these models. Key contributions include the development of tools to objectively estimate suitable property prices and the advancement of price prediction research through an extensive comparison of machine learning models. While previous studies have demonstrated the predictive capabilities of these models, this research focuses on the impact of hyperparameter tuning on the performance of the Random Forest model. By systematically optimizing parameters such as max_depth, n_estimators, and n_jobs, computation time was reduced by over 93% (from 865 seconds to 50 seconds) with minimal loss in accuracy. With proper hyperparameter tuning, Random Forest achieved the best performance in terms of MAE score (26.555), outperforming XGBoost (27.552), Decision Tree (28.832), and LightGBM (29.752).
Classifying Indonesian Hoax News Titles with SVM, XGBoost, and BiLSTM Trisna, I Nyoman Prayana; Putra, I Made Wiraharja Jaya; Vihikan, Wayan Oger
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.
Optimization of Palm Fruit Ripeness Detection With Yolov11 on CPU Anniswa, Iqbal Ramadhan; JAUHARIS SAPUTRA, Wahyu Syaifullah; Idhom, Mohammad; Rizaldy Pratama, Alfan; Susrama Mas Diyasa, I Gede
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.111253

Abstract

The palm oil industry is one of the strategic sectors that contributes significantly to the Indonesian economy. However, this industry still faces various challenges, particularly in terms of operational efficiency and the implementation of digitalization, especially at the level of independent farmers who often still use manual methods to determine the ripeness of the fruit. This manual process is prone to subjectivity, which can impact harvest quality and supply chain efficiency. To address this issue, this study proposes a palm oil fruit ripeness detection system based on the YOLOv11 algorithm, chosen for its advantages in inference speed and detection accuracy, especially when run on devices with limited resources. The developed model was then implemented using the ONNX Runtime Framework. This enables accelerated inference processes and supports portability on hardware with limited resources. Test results show that the model achieves an mAP@50 accuracy of 90.2% with an average latency of around 255 ms to 300 ms. With these achievements, this system is not only reliable in detecting fruit ripeness, but also efficient in processing time and relevant to support digital transformation in the palm oil plantation sector.
Optimization of Multimodal Deep Learning for Depression Detection Hermawan, Aditiya; Daniawan, Benny; Edy, Edy; Nathaniel, Joese
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.111407

Abstract

Depression is a complex and often underdiagnosed mental health condition that manifests through subtle verbal, acoustic, and behavioral cues. Traditional unimodal detection systems struggle to capture the full spectrum of depressive symptoms, often leading to inaccurate or incomplete assessments. This study proposes a multimodal deep learning framework that integrates textual, audio, and visual modalities to improve the robustness and reliability of automatic depression detection, achieving an overall classification accuracy of 74%. The approach prioritizes privacy and interpretability by using facial keypoints and gaze direction rather than raw video frames, and applies attention mechanisms to align and fuse features across modalities. Each modality is processed through dedicated neural architectures tailored to its data type, and their outputs are combined within a fusion model that learns to capture cross-modal emotional patterns. Experimental results demonstrate that the proposed multimodal system significantly outperforms its unimodal counterparts in terms of classification performance. The visual modality was found to contribute most strongly to detection accuracy, as confirmed by ablation analysis. These findings highlight the value of multimodal integration in capturing complex psychological signals and support the development of intelligent, non-invasive screening tools for use in digital mental health applications.
Recognition of Toraja Carving Motifs Using Texture Features with GLCM Imelda, Imelda; Utama, Gunawan Pria; Cahyana, Asep
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.111590

Abstract

Indonesia comprises a diverse array of ethnic groups and cultures. Each ethnic group has unique carving motifs rich in philosophical meaning. Toraja motifs are among the most distinctive in the world. These motifs are often found in traditional houses, textiles, and architectural ornaments. However, people's understanding of the symbolic value of these carvings remains limited, thereby risking cultural erosion. This study aims to recognize Toraja carving motifs using digital image processing, specifically through the extraction of Gray Level Co-occurrence Matrix (GLCM) texture features, which include contrast, correlation, energy, and homogeneity at 0° orientation. The Toraja carving dataset was processed through preprocessing, feature extraction, and thresholding-based classification stages. This study contributes to the combination of GLCM and thresholding that can improve accuracy while providing a computationally efficient solution for traditional motif pattern recognition. Experimental results show that thresholds of 0.002 and 0.004 produce recognition accuracies of 100% and 82%, respectively.
Development and Validation of a Virtual Reality Circumcision Training Simulator: Simulator Sickness, User Experience, and Clinical Performance in Bali, Indonesia Sindu, I Gede Partha; Kertiasih, Ni Ketut; Dinata, I Gede Surya; Sugiartawan, Putu
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.111664

Abstract

Virtual Reality (VR) is increasingly integrated into medical education, yet its application in Indonesia remains limited. This study developed and validated a VR-based circumcision simulator to evaluate simulator sickness, user experience, and clinical performance. A mixed-methods, repeated-measures design was conducted with 74 participants (25 Novices, 24 Intermediates, 25 Experts). Participants engaged in three simulation modes (Autonomous, Guided, Haptic). Instruments included SSQ, FMS, VRNQ, UEQ-S, Checklist, and OSATS. Analyses employed repeated-measures ANOVA, nonparametric tests, and Spearman correlations. Simulator sickness was highest in Autonomous Mode. User experience scores improved with expertise, showing positive correlations with performance and negative correlations with sickness. Experts consistently outperformed other groups, and skill improvements were retained for up to one month. The VR circumcision simulator demonstrated strong construct validity and educational impact. Instructional modes effectively reduced sickness, while haptic integration enhanced spatial orientation. Future studies should incorporate physiological measures and assess real-world skill transfer.
A Comparative Deep Learning Approach for Classifying Oil Palm Fruit Ripeness Levels Using YOLOv8s and Faster R-CNN Rasmila, Rasmila; Oktavian, Sakbanullah Dwi; Dasmen, Rahmat Novrianda; Amalia, Rahayu
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.111942

Abstract

Assessing oil palm fruit ripeness is essential for optimizing harvest timing and maximizing market value. In many developing regions, harvesting is still performed every 10–15 days through manual visual inspection, a process prone to human error that often causes premature harvesting and reduces selling value by up to 50%. This study explores deep learning-based object detection for automatic classification of oil palm fruit bunches. A dataset of 4,578 annotated high-resolution images was prepared and categorized into six ripeness classes: Empty, Immature, Underripe, Abnormal, Ripe, and Overripe. Two advanced detection models, YOLOv8s and Faster R-CNN with a ResNet-50 backbone, were evaluated under identical conditions using precision, recall, and mean Average Precision (mAP) metrics. YOLOv8s achieved precision and recall above 99%, with a mAP 0.5:0.95 of 0.9254, demonstrating strong reliability and efficiency for real-time use. Faster R-CNN achieved a higher mAP 0.5 of 0.9964, indicating superior localization accuracy but slower computation. Overall, YOLOv8s provides a better trade-off between accuracy and speed, making it more practical for automated harvesting. This research supports precision agriculture by emphasizing AI-driven solutions that improve productivity, minimize losses, and promote sustainable palm oil management.
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
Deep Learning Factor Investing in the Indonesian Stock Market Atha Rohmatullah, Fawwaz; Alzami, Farrikh; Rakhmat Sani, Ramadhan; Novita Dewi, Ika; Winarno, Sri; Sulistyono, Teguh
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.112549

Abstract

Traditional linear factor models often fail to capture the complex, non-linear dynamics of emerging stock markets. This research designs and validates a novel Recurrence Plot (RP) matrices with β-VAE deep learning methodology to discover non-linear investment factors within the Indonesian context. We demonstrate that this framework is a systematically superior "factor factory" compared to a linear RP with PCA baseline, discovering twice as many high-quality factors (Sharpe > 0.3) and generating 7-fold more alpha on average. A key finding is the model's ability to disentangle high-frequency predictive signals (identified by SHAP) from more valuable, low-frequency profitable trends (validated by backtesting). The champion factor from this process yields a robust annualized alpha of 6.65% with a minimal max drawdown of -7.73% from 2018 to 2025. This study concludes that the RP -> β -VAE approach is a robust and resilient framework for discovering safer, non-linear sources of return unexplained by conventional models.

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