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Jurnal Teknik Informatika C.I.T. Medicom
Published by Institute of Computer Science
ISSN : 23378646     EISSN : 2721561X     DOI : -
Core Subject : Science,
Computer Science & IT
The Jurnal Teknik Informatika C.I.T a scientific journal of Decision support sistem , expert system and artificial inteligens which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 17 No 6 (2026): Computer Science" : 5 Documents clear
Comparative Study of CatBoost, XGBoost, Random Forest, and Decision Tree for Phishing Web Page Classification Haryani, Haryani; Agustyaningrum, Cucu Ika
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 6 (2026): Computer Science
Publisher : Institute of Computer Science (IOCS)

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Abstract

Phishing is a fraudulent method in which attackers using fake websites steal user information such as login credentials and sensitive financial data. Therefore, this study compares four machine learning algorithms, namely CatBoost, XGBoost, Random Forest, and Decision Tree, in classifying phishing websites efficiently and accurately. In this study, the dataset used is the Web Page Phishing Dataset, which begins with exploration and preprocessing, which includes data cleaning, handling missing values, normalization, feature selection, and testing. Post-split. The data used has been divided into training data and test data, namely 80:20. The model was implemented using Python in Google Colaboratory. Model performance evaluation was measured in five main metrics, such as accuracy, precision, recall, F1-score, and AUC. The experimental results indicate that CatBoost achieved the best position with a performance of 89.57% in accuracy, 85.74% in F1-score, 88.73% in precision, 88.78% in recall, and 89.00% in AUC. XGBoost ranked second with a very competitive performance, followed by Random Forest, which was relatively stable with an accuracy value of 89.41% and an F1-score of 85.35%. On the other hand, the decision tree achieved the lowest performance with an accuracy of 88.69% and an F1-score of 84.10%. These performance results indicate limitations in handling complex data, as well as a tendency to overfit. Overall, ensemble boosting-based algorithms, especially CatBoost and XGBoost, outperform single trees in detecting phishing websites. These results will be benefical to?progress in the next generation for the construction of intelligent based phishing detection system under machine learning. In addition, the outcomes of this study will gain momentum for future works where hyperparameter optimization, larger datasets and real-time applications for phishing detection systems?can be focused. Furthermore, this work will contrast the application of ensemble?algorithm in the cybersecurity field.
Towards Autonomous Digital Governance: Integrating AI, Data Governance, and Smart Infrastructure for Future Government Bambang Saras Yulistiawan; Galih Prakoso Rizky A
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 6 (2026): Computer Science
Publisher : Institute of Computer Science (IOCS)

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Abstract

The rapid advancement of digital technologies has transformed public governance, evolving from e-government to more integrated digital government systems. However, the transition toward fully autonomous digital governance remains limited. This study aims to analyze how the integration of Artificial Intelligence (AI), data governance, and smart infrastructure can enable the development of autonomous digital governance systems. Using a mixed-method approach, this research combines a systematic literature review and case study analysis with quantitative survey data to examine the relationships between key variables, including AI capability, data governance quality, and infrastructure readiness. The findings indicate that the integration of these components significantly contributes to the formation of an autonomous decision-making system, which in turn enhances governance outcomes in terms of efficiency, transparency, and responsiveness. AI capability emerges as the most influential factor, particularly in enabling automation and predictive analytics, while data governance ensures the reliability and accountability of data-driven processes. Smart infrastructure supports real-time data collection and system connectivity, although disparities in infrastructure readiness remain a challenge. The study also identifies key benefits of autonomous digital governance, including faster decision-making, reduced human bias, and the development of predictive public services. However, several risks are highlighted, such as ethical concerns, privacy issues, and over-reliance on technology. This research proposes an integrated conceptual model of autonomous digital governance, emphasizing the need for synergy between technological and institutional components. The study contributes to the advancement of digital governance theory while providing practical insights for policymakers in designing future-ready governance systems.
Artificial Intelligence Based Multilevel Optimization Models for Complex Decision Systems Hengki Tamando Sihotang; Wildan Alrasyid
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 6 (2026): Computer Science
Publisher : Institute of Computer Science (IOCS)

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Abstract

Complex decision systems, such as supply chains, smart cities, and healthcare networks, are characterized by hierarchical structures, dynamic environments, and high levels of uncertainty, making them difficult to optimize using traditional methods. Conventional optimization approaches, which are typically static and single-level, are limited in their ability to handle interdependent decisions and rapidly changing conditions. This study proposes an Artificial Intelligence-based multilevel optimization model to address these challenges by integrating hierarchical optimization with advanced AI techniques. The proposed framework combines multilevel optimization encompassing strategic, tactical, and operational decision layers with Artificial Intelligence methods, including neural networks for prediction, reinforcement learning for adaptive decision-making, and genetic algorithms for global optimization. A simulation-based methodology is employed to model complex environments and evaluate system performance under various scenarios. The results demonstrate that the proposed model significantly outperforms traditional optimization approaches. It achieves higher accuracy, faster convergence, and greater adaptability in dynamic and uncertain environments. Sensitivity analysis confirms the robustness of the model under varying conditions, while scalability tests indicate its effectiveness in handling large-scale systems. These findings highlight the advantages of integrating AI with multilevel optimization for complex decision-making. It offers both theoretical and practical implications for improving decision-making in complex systems. Future research is recommended to enhance computational efficiency, improve model interpretability, and validate the framework through real-world applications across various domains.
Transparency Analysis of Deep Learning Models in Medical Data Using SHAP and LIME Arka Evander; Lyra Amara Quinn
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 6 (2026): Computer Science
Publisher : Institute of Computer Science (IOCS)

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Abstract

The increasing adoption of deep learning models in healthcare has significantly improved the accuracy of medical diagnosis and prediction; however, their lack of transparency remains a critical challenge. These models often operate as “black boxes,” making it difficult for healthcare professionals to understand the reasoning behind their predictions, which raises concerns regarding trust, safety, and ethical decision-making. This study aims to analyze the transparency of deep learning models applied to medical data by utilizing two widely used explainable artificial intelligence (XAI) techniques, namely SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations). A deep learning model was developed using medical datasets, including clinical (tabular) and/or medical imaging data, and evaluated using performance metrics such as accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). To enhance interpretability, SHAP and LIME were applied to explain the model’s predictions at both global and local levels. The results indicate that the model achieves high predictive performance, with key features such as glucose level, age, blood pressure, and cholesterol significantly influencing predictions. The comparative analysis shows that SHAP provides more consistent, stable, and comprehensive explanations, making it more suitable for global interpretation and clinical decision support. In contrast, LIME offers simpler and more intuitive local explanations, which are useful for understanding individual predictions but may lack stability across samples. This study contributes to the advancement of explainable AI in healthcare by demonstrating how interpretability techniques can bridge the gap between high model performance and practical clinical applicability. Future research is recommended to explore more robust and scalable XAI approaches for real-world medical applications.
Performance Analysis of Generative AI in Bias Detection and Mitigation on Text Datasets Charlotte Charlotte; Grayson Grayson; Matteo Xavier
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 6 (2026): Computer Science
Publisher : Institute of Computer Science (IOCS)

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Abstract

This study investigates the performance of generative artificial intelligence in detecting and mitigating bias within text datasets, addressing a critical challenge in the development of fair and ethical AI systems. This research aims to provide a comprehensive evaluation framework that integrates both bias detection and mitigation, which are often studied separately in existing literature. The methodology employs multiple text datasets, including social media, news articles, and hate speech corpora, to capture diverse forms of bias. Generative models based on transformer architectures, particularly GPT-based and fine-tuned models, are evaluated alongside baseline models. Bias detection is conducted using prompt-based, classifier-based, and lexicon-based approaches, while mitigation strategies include prompt engineering, debiasing algorithms, reinforcement learning with human feedback (RLHF), and data augmentation. Model performance is assessed using a combination of classification metrics (accuracy, precision, recall, F1-score), fairness metrics (demographic parity and equal opportunity), and text quality measures (perplexity, coherence, and semantic similarity). The results indicate that all mitigation techniques contribute to reducing bias, with RLHF and hybrid approaches achieving the highest effectiveness, reducing bias scores by over 50% while significantly improving fairness metrics. This study contributes to AI fairness research by proposing an integrated evaluation framework and demonstrating that it is possible to achieve substantial bias reduction without compromising overall model performance. The findings provide practical insights for the development of more transparent, reliable, and ethically aligned generative AI systems, supporting their responsible deployment in sensitive domains such as healthcare, finance, and hiring.

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