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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 140 Documents
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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.
Enhancing Product Recommendation Systems Using Hybrid Filtering: A Comparative Analysis of Collaborative and Content-Based Approaches Andrine Lauge; Ragnhild Ragnhild
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The rapid growth of e-commerce platforms has led to an overwhelming number of product choices, creating challenges for users in identifying items that match their preferences. Recommendation systems have become essential tools to address this issue; however, traditional approaches such as Collaborative Filtering and Content-Based Filtering suffer from limitations including data sparsity, cold-start problems, and limited recommendation diversity. This study proposes a Hybrid Filtering-based product recommendation system that integrates both Collaborative Filtering and Content-Based Filtering techniques to overcome these challenges. The proposed model utilizes user-item interaction data and product metadata to generate personalized recommendations through a hybrid approach, combining algorithms such as K-Nearest Neighbors (KNN), Matrix Factorization, Term Frequency–Inverse Document Frequency (TF-IDF), and cosine similarity. The system is evaluated using multiple performance metrics, including accuracy (precision, recall, and F1-score), ranking quality (Mean Average Precision and Normalized Discounted Cumulative Gain), and prediction error (Root Mean Square Error and Mean Absolute Error). The results demonstrate that the Hybrid Filtering model outperforms individual methods in all evaluation aspects. It achieves higher accuracy, better ranking performance, lower prediction error, and greater diversity in recommendations. These findings indicate that the hybrid approach effectively addresses the limitations of traditional recommendation systems and provides more reliable and personalized recommendations. In conclusion, this research confirms that Hybrid Filtering is a robust and efficient method for improving the performance of product recommendation systems. The proposed model has significant practical implications for e-commerce platforms, as it enhances user experience, increases engagement, and supports better decision-making processes.
Machine Learning-Based Malware Detection Using Behavioral Pattern Analysis for Enhanced Cybersecurity Khalid Karim
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The rapid growth and increasing sophistication of malware pose significant challenges to traditional cybersecurity systems, particularly those relying on signature-based detection methods. These conventional approaches are often ineffective against new and evolving threats, such as polymorphic and zero-day malware. To address these limitations, this study proposes a machine learning-based malware detection framework that leverages behavioral pattern analysis to improve detection accuracy and adaptability. A comprehensive methodology is implemented, involving dataset collection from publicly available sources, feature extraction using frequency-based, sequence-based, and graph-based techniques, and data preprocessing to ensure quality and balance. Multiple machine learning models, including Random Forest, XGBoost, and Long Short-Term Memory (LSTM), are employed to capture both statistical and temporal patterns in the data. The models are evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The experimental results demonstrate that the proposed model achieves high classification performance and effectively distinguishes between malware and benign software. Behavioral features, particularly sequence-based representations, are found to significantly enhance detection capability. Furthermore, the model shows strong generalization when tested on unseen data, indicating its robustness against new malware variants. Compared to traditional signature-based methods, the proposed approach provides improved detection of zero-day attacks and reduces false positives. This study contributes to the advancement of cybersecurity by presenting a scalable and adaptive malware detection framework that integrates machine learning with behavioral analysis.
A Unified Artificial Intelligence and Stochastic Optimization Framework for Decision-Making in Highly Complex Systems Hengki Tamando Sihotang; Galih Prakoso Rizky A
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Decision-making in highly complex systems is increasingly challenged by uncertainty, dynamic environments, and the availability of large-scale, high-dimensional data. Traditional optimization methods often lack adaptability, while standalone Artificial Intelligence models struggle to explicitly handle uncertainty in a principled manner. To address these limitations, this research proposes a unified framework that integrates Artificial Intelligence with Stochastic Optimization for enhanced decision-making in complex and uncertain environments. The proposed framework combines data-driven learning and probabilistic optimization within a closed-loop architecture consisting of data input, AI-based prediction, stochastic decision-making, and continuous feedback. Advanced AI models, including deep learning and reinforcement learning, are employed to extract patterns and generate predictive insights from real-time and historical data. These outputs are then incorporated into stochastic optimization models, which evaluate decisions under uncertainty using probabilistic constraints and scenario-based analysis. The framework is further strengthened by an adaptive feedback mechanism that continuously updates both learning and optimization components. Experimental evaluation demonstrates that the proposed approach outperforms traditional optimization and pure AI models in terms of decision accuracy, robustness under uncertainty, and adaptability to dynamic environments. The framework also shows improved stability and computational efficiency when applied to large-scale systems. Practical applications in domains such as finance, logistics, and smart city management highlight its real-world relevance. Overall, this research contributes to decision science by bridging the gap between learning and uncertainty modeling, providing a scalable and integrated solution for intelligent decision-making in highly complex systems.
A Unified Artificial Intelligence Driven Data Governance Framework for Decision Intelligence in Smart Digital Ecosystems Bambang Saras Yulistiawan
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This research proposes a Unified Artificial Intelligence–Driven Data Governance Framework to enhance decision intelligence in smart digital ecosystems. The rapid growth of technologies such as the Internet of Things (IoT), smart cities, and digital platforms has led to an exponential increase in data volume and complexity, creating challenges related to data silos, poor data quality, lack of governance standards, and ineffective decision-making. While artificial intelligence (AI) has been widely adopted to address analytical needs, existing approaches often fail to integrate data governance with AI-driven decision processes, resulting in unreliable and less transparent outcomes. To address this gap, this study develops a multi-layered framework that integrates data governance, AI, and decision intelligence into a unified architecture. The proposed framework consists of a data layer, governance layer, AI layer, decision layer, and application layer, supported by key components such as data integration modules, data quality engines, policy enforcement mechanisms, AI model management, and decision support systems. A prototype-based methodology is employed to evaluate the framework using machine learning models and optimization techniques within simulated smart ecosystem environments. The results demonstrate that the proposed framework significantly improves decision accuracy, data quality, and system reliability while maintaining acceptable processing time and scalability. Compared to traditional systems and non-governed AI models, the framework provides enhanced transparency, accountability, and compliance. However, challenges related to computational cost, system complexity, scalability, and ethical considerations such as bias and fairness remain. This research contributes to the field by presenting a comprehensive and scalable solution that bridges the gap between AI and data governance.
Wearable Device-Based Health Monitoring System with AI-Driven Predictive Analytics for Real-Time and Preventive Healthcare Hyran Amul; Gayan Lashith
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This study proposes a wearable device-based health monitoring system integrated with artificial intelligence (AI) predictive analytics to enable continuous, real-time, and proactive healthcare management. The system utilizes wearable sensors to collect physiological and activity data, including heart rate, blood oxygen saturation (SpO?), body temperature, and movement patterns. These data are transmitted through IoT-based communication to a cloud platform, where they undergo preprocessing, feature extraction, and analysis using machine learning and deep learning models. The proposed approach incorporates algorithms such as Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks to perform disease prediction, anomaly detection, and risk scoring. Experimental results demonstrate that the models achieve high performance across multiple evaluation metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, with LSTM showing superior performance in handling time-series data. The system effectively supports real-time monitoring, enabling early detection of potential health risks and providing timely alerts to users and healthcare providers. Compared to existing systems, the proposed framework offers enhanced predictive capabilities, improved responsiveness, and better integration of wearable technology with AI-driven analytics. The findings highlight the significant potential of combining wearable devices and AI in advancing healthcare innovation, particularly in remote patient monitoring, telemedicine, and preventive medicine. Despite challenges related to data privacy, device limitations, and computational requirements, this research demonstrates a scalable and intelligent solution for modern healthcare systems, emphasizing the critical role of predictive analytics in the future of preventive healthcare.

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