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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 476 Documents
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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.
IndoBERT Optimization for Sentiment Analysis on DeepSeek App Reviews Sunan, Muh.; Resiloy, Unique Desyrre A.; Endriani, Desy; Suhaeni, Cici; Sartono, Bagus; Dito, Gerry Alfa
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.107507

Abstract

In the digital era, sentiment analysis is important to evaluate public opinion, especially in the context of Play Store apps with Indonesian-language reviews. This research aims to improve the performance of the IndoBERT model in sentiment analysis of DeepSeek app reviews by using data augmentation and hyperparameter tuning techniques. Data augmentation is done through the back-translation technique, while the hyperparameters tested include the number of epochs, learning rate, and batch size. Experimental results show that the combination of data augmentation with epoch 10, learning rate 2e-5, and batch size 16 produces the highest accuracy of 93.95% and F1-score of 0.94, with better stability than the model without augmentation. The model without augmentation showed fluctuations in performance, indicating overfitting in some configurations. These findings confirm the importance of applying augmentation techniques and hyperparameter tuning in improving the accuracy and stability of sentiment analysis models, and contribute to the development of NLP models for Indonesian and other resource-constrained languages.
Improved Wavelet-GLCM for Robust Batik Motif Classification Pradana, Gregorius Adi; Harjoko, Agus
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.108011

Abstract

Batik is a traditional Indonesian fabric made by applying wax on the fabric, then processed with a certain technique. The diversity of batik motifs often makes it difficult for people to recognize them. Therefore, a batik motif classification system is needed, one of the methods of which is based on digital image processing. However, in this method, variations in rotation and scale in the image often cause feature values to change, thus decreasing accuracy. To overcome this, this study proposes a robust Improved Wavelet-GLCM (IWGLCM) feature extraction method for classifying batik motif images that account for rotation and scale variations. This method combines the Gray Level Co-occurrence Matrix (GLCM) and statistical values from the results of the Discrete Wavelet Transform (DWT). These combined features are then classified using Support Vector Machine (SVM). In the test scenario with variations in rotation and scale at the same time, this method managed to achieve optimal performance with an accuracy of 95.00%, a precision of 95.08%, a recall of 95.00%, and an f1-score of 95.00%.
Classification of Infected Salmon Using CNN Deep Features and Optuna-Optimized SVM Pradita, Agus Hendra; Ayu, Putu Desiana Wulaning; Hostiadi, Dandy Pramana
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.108820

Abstract

Fish diseases are a major challenge in the aquaculture industry, impacting productivity and the economy, particularly in salmon farming. This study aims to develop an image classification system for infected salmon using Convolution Neural Network (CNN) deep features approach and Support Vector Machine (SVM) classifier optimized with Optuna. The dataset consists of 1,208 images that were balanced through augmentation before being divided into 70% training data and 30% test data. Features were extracted from the middle layer of three pretrained CNN architectures: EfficientNetB1 (block6d_add), ResNet50 (conv4_block6_out), and VGG16 (block4_pool), then selected using the Least Absolute Shrinkage and Selection Operator (LASSO) method to address high-dimensionality issues. An SVM classification model was trained using stratified 5-fold cross-validation, both with default parameters and hyperparameter optimization results from Optuna. The results show that the model with features from EfficientNetB1 tuned by Optuna achieved the highest accuracy of 99.34%, a significant improvement over the default model 98.23%. Meanwhile, ResNet50 and VGG16 achieved optimal accuracies of 98.23% and 98.89%, respectively, after tuning. This study contributes to the development of an adaptive and accurate early detection system for infected fish.
Rainfall Intensity Prediction Using LSTM and Random Forest Hybrid Model Pambudi, Ari; Aryani, Diah; Sunarto, Ronald Nur
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.109189

Abstract

Predicting rainfall accurately is essential for managing water resources and preventing hydrometeorological disasters. The unpredictability of daily rainfall patterns necessitates accurate and effective prediction methods. This study proposes a residual hybrid approach to forecast rainfall using historical rainfall data alone. To record temporal information, a Long Short-Term Memory (LSTM) model is used. patterns in the time series data and generate initial predictions. The residual, defined as the difference between actual values and LSTM predictions, is then used as the intend to use a Random Forest (RF) model for training, which learns the non-linear patterns not effectively captured by the LSTM. Although the dataset includes various meteorological variables such as temperature and humidity, this study uses only rainfall as the main input. The data is split into training and testing sets with an 80:20 ratio. Model performance is evaluated using MAE, MSE, and RMSE, with RMSE as the primary evaluation metric. Experimental results show that the LSTM-RF hybrid model consistently delivers greater accuracy of predictions in comparison to single-model approaches, demonstrating strong potential in improving the reliability of rainfall forecasting based solely on historical data.

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