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Agus Harjoko
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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 506 Documents
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Honeypot-Integrated CSIRT Model Based on ISO 27001/27035 Meinarni, Ni Putu Suci; Udayana, I Putu Agus Eka Darma
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 3 (2026): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.117482

Abstract

Higher education institutions are evolving into digital entities that manage critical assets, including sensitive personal data and academic records. However, many universities in Indonesia still lack a structured and legally compliant approach to information security, leaving them vulnerable to cyber threats such as defacement attacks and data breaches. This paper proposes a conceptual model for a Computer Security Incident Response Team (CSIRT) specifically designed for academic environments. The model integrates international standards ISO/IEC 27001 for Information Security Management Systems and ISO/IEC 27035 for incident handling, with proactive threat detection using honeypot technology. The framework consists of four layers: governance, operational procedures, technology infrastructure, and legal compliance. Designed using a systems-thinking approach and validated through expert review, the model addresses institutional readiness and outlines a roadmap for phased implementation. It aims to support early threat detection, structured response workflows, and digital forensic readiness in alignment with Indonesian cybersecurity law. This honeypot-integrated CSIRT model contributes a strategic reference for universities seeking to enhance their security capabilities in a sustainable and lawful manner.
Predictive Modeling of Urban Air Quality Using Machine Learning Dahlan, Akhmad; Fatta, Hanif Al; Farida, Lilis Dwi; Utama, Hastari; Pristyanto, Yoga
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 3 (2026): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.117546

Abstract

Air pollution is a critical environmental concern adversely affecting public health in urban areas worldwide. Accurate prediction of air quality enables timely intervention by environmental agencies and policymakers. This study presents a comprehensive comparative analysis of ensemble machine learning methods for predicting urban air quality using the publicly available UCI Air Quality Dataset, containing 9,358 hourly instances of chemical sensor measurements collected in a heavily polluted Italian city from March 2024 to February 2025. Five algorithms were evaluated: Random Forest (RF), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and CatBoost, against Ridge Regression and SVR baselines. A systematic pipeline encompassing preprocessing, temporal feature engineering, Bayesian hyperparameter optimization via Optuna, and time-series cross-validation was implemented. Results demonstrate XGBoost achieved the best performance with RMSE = 2.14, MAE = 1.58, and R² = 0.9312. SHAP-based feature importance analysis revealed CO(GT) lag features, C6H6(GT), and NOx(GT) as the most influential predictors.
Comparative Performance Analysis of Xception and ResNet50 Architectures for Facial Expression Recognition Using the FER-2013 Dataset Aryani, Diah; Akbar, Habibullah; Delio, Ferdinand Defin
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 3 (2026): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.117591

Abstract

This study aims to evaluate and compare the performance and computational efficiency of the Xception and ResNet50 architectures in facial expression classification tasks. Facial Expression Recognition (FER) plays an important role in the development of intelligent systems capable of interpreting human emotions. This technology has various applications, including adaptive learning systems, emotion-aware customer service, and mental health support systems. This research compares two widely used Convolutional Neural Network (CNN) architectures, Xception and ResNet50, for facial expression classification using the FER-2013 dataset. The dataset contains 35,887 grayscale facial images with a resolution of 48×48 pixels categorized into seven basic emotions. All images were resized to 224×224 pixels and converted into RGB format to match the input requirements of pretrained ImageNet models.Both architectures were trained using a transfer learning strategy with selective fine-tuning on specific layers. Data augmentation techniques were applied to increase dataset variability and reduce overfitting. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results show that Xception architecture outperforms ResNet50, achieving a validation accuracy of 70.69% and a weighted F1-score of 0.71. These findings demonstrate that appropriate architecture selection and structured training strategies can significantly improve FER performance in practical intelligent systems.
Comparison of Linear and Ridge Regression for Estimating Indonesia’s IHSG, 2010–2024 Indah Saraswati, I Dewa Ayu; Yunita Dewi, Kadek; Rehatta, Jullio; Sunarya, I Made Gede; Oka Gunawan, I Made Agus
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 3 (2026): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.117861

Abstract

This study aims to estimate the movement of the Indonesia Composite Stock Price Index (IHSG) using linear regression and Ridge Regression based on monthly data from 2010 to 2024, where IHSG serves as a key indicator of Indonesia’s capital market and requires a simple yet reliable estimation model to support economic and investment decisions. The methodology applies linear regression as a baseline model and Ridge Regression to address potential multicollinearity among independent variables, with model performance evaluated using 5-fold cross-validation and metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that linear regression achieves MAE = 0.068829, MSE = 0.007987, and RMSE = 0.087823, while Ridge Regression performs slightly better with MAE = 0.068547, MSE = 0.007970, and RMSE = 0.087732. Although the differences are relatively small, Ridge Regression consistently produces lower and more stable error values, indicating that it is a more robust alternative for IHSG estimation, particularly for medium- to long-term analysis.
View-Aware Chest X-Ray Report Generation with Relational-Contrastive Alignment Lailiyah, A’iza Karimatul; Yustanti`, Wiyli; Ravuri, Benhur
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 3 (2026): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.122422

Abstract

One of the most popular diagnostic imaging modalities is the chest X-ray (CXR), however creating radiology reports still takes a lot of time and requires radiologist competence. Current automated techniques limit cross-view comprehension and report quality by frequently using single-view images, discarding fine-grained spatial information through global average pooling, and using inflexible decoding methodologies. This study proposes a view-aware chest X-ray report generation framework that integrates multiple radiographic views, strengthens visual–text semantic alignment, and calibrates decoding automatically. The framework comprises a domain-specific visual representation using a CheXpert-pretrained DenseNet-121 encoder that preserves 7×7 spatial features, a view-aware embedding, a relational-contrastive semantic alignment module, and an automatic decoding calibration mechanism. Evaluated on the IU X-ray dataset using paired frontal–lateral images and a DistilGPT2 decoder, the proposed framework achieved BLEU-1 of 0.4768, BLEU-2 of 0.3004, BLEU-3 of 0.2056, BLEU-4 of 0.1525, METEOR of 0.4001, ROUGE-L of 0.3211, and CIDEr of 0.3812. These results demonstrate that the proposed framework improves the coherence and clinical relevance of generated chest X-ray reports while providing an effective vision-language framework for multi-view radiology report generation.
Mixed-Integer Programming versus Constraint Programming for the Travelling Salesman Problem with Time Windows and Clustered Backhauls: A School-Meal Delivery Case Study Santiyuda, Kadek Gemilang
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 3 (2026): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.122506

Abstract

Under Indonesia’s Makanan Bergizi Gratis (MBG), a vehicle delivers freshly cooked meals to schools within their lunchtime windows, takes a driver break, and then collects the reusable containers before returning to the kitchen. We study this route as a Travelling Salesman Problem with Time Windows and Clustered Backhauls (TSPTW-CB). Each school’s container collection is paired with its delivery, deliveries come before any collection, and a food-freshness limit fixes a single dispatch time for the trip. We formulate the problem in two ways. First, a mixed-integer linear program (MILP) solved with Gurobi. The second is a constraint program (CP) solved with OR-Tools CP-SAT. We compare them on route instances built from a real case study in Bali, Indonesia, and we check every result with an independent schedule simulator. On feasible instances the two solvers are equally fast and return the same optimal route. The difference appears on infeasible instances. The CP model proves in a fraction of a second that one vehicle cannot serve a set of schools, while the MILP does not finish within ten minutes. This feasibility question decides how many vehicles a kitchen needs, so constraint programming is the better tool for this route.

Page 51 of 51 | Total Record : 506

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