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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 152 Documents
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A Conceptual Framework for Autonomous AI Governance in Smart Digital Ecosystems Bambang Saras Yulistiawan
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 3 (2023): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The rapid advancement of autonomous Artificial Intelligence (AI) technologies has significantly transformed smart digital ecosystems across sectors such as smart cities, healthcare, fintech, autonomous transportation, Internet of Things (IoT), and Industry 4.0. While autonomous AI offers substantial benefits in automation, efficiency, and intelligent decision-making, its increasing adoption also creates complex governance challenges related to algorithmic bias, lack of transparency, cybersecurity threats, privacy violations, accountability ambiguity, and long-term societal risks. This study aims to develop a conceptual framework for autonomous AI governance in smart digital ecosystems by integrating ethical, technical, regulatory, adaptive, and human-centered governance dimensions into a unified governance architecture. This study employs a qualitative conceptual research approach using theory-building methodology and literature synthesis. Data were obtained from academic journals, conference proceedings, AI governance reports, international regulations, policy documents, and institutional publications related to autonomous AI, responsible AI, cybersecurity, and digital governance. The analysis was conducted using systematic literature review, thematic analysis, comparative framework analysis, conceptual mapping, and governance modeling techniques. The findings indicate that autonomous AI governance requires a multidimensional and interconnected governance structure capable of addressing ethical, legal, technical, organizational, and sustainability challenges simultaneously. The proposed framework consists of six governance dimensions: ethical governance, regulatory governance, technical governance, data governance, adaptive governance, and human-AI collaboration governance. These dimensions collectively support fairness, transparency, accountability, cybersecurity, privacy protection, ecosystem resilience, and human oversight within autonomous AI environments. This study concludes that integrated and adaptive governance mechanisms are essential for ensuring responsible, transparent, secure, and sustainable AI implementation in smart digital ecosystems while supporting trustworthy and resilient digital transformation.
Toward an Integrated Intelligent Data Governance Architecture for Decision-Centric Digital Systems Bambang Saras Yulistiawan
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 3 (2023): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The rapid advancement of artificial intelligence (AI), big data analytics, cloud computing, Internet of Things (IoT), and autonomous digital technologies has transformed modern digital ecosystems into highly interconnected and decision-centric environments. However, the widespread adoption of intelligent systems has also introduced significant governance challenges, including fragmented data governance, cybersecurity risks, interoperability limitations, privacy concerns, algorithmic bias, lack of transparency, and weak accountability mechanisms. Existing governance frameworks often operate independently across data governance, AI governance, cybersecurity, and decision-support systems, making them inadequate for managing dynamic and intelligent digital infrastructures. This study aims to propose an integrated intelligent data governance architecture that supports adaptive, secure, transparent, and decision-centric digital systems. This study employs a qualitative-conceptual methodology using Design Science Research (DSR), Systematic Literature Review (SLR), and framework development approaches. Secondary data were collected from scientific journals, conference proceedings, governance frameworks, industry reports, and international standards such as ISO, OECD AI Principles, NIST AI RMF, GDPR, COBIT, and DAMA-DMBOK. Data analysis was conducted using thematic analysis, comparative analysis, architectural analysis, and governance layer modeling. The findings reveal that intelligent digital ecosystems require integrated governance mechanisms combining data governance, AI governance, cybersecurity, explainable AI, interoperability, decision intelligence, and adaptive feedback systems. The proposed architecture consists of seven interconnected layers that collectively improve governance transparency, accountability, operational resilience, digital trust, and decision quality. The study concludes that integrated intelligent governance architectures are essential for supporting sustainable, secure, and trustworthy digital transformation in modern AI-driven environments.
A Unified Theoretical Model of AI-Driven Governance for Adaptive Digital Transformation Bambang Saras Yulistiawan
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 2 (2024): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The rapid advancement of artificial intelligence (AI), Internet of Things (IoT), cloud computing, big data analytics, and autonomous systems has accelerated digital transformation across various sectors, creating increasingly interconnected and intelligent digital ecosystems. However, the widespread adoption of AI technologies also generates complex governance challenges related to transparency, accountability, cybersecurity, data privacy, interoperability, and ethical compliance. Existing AI governance frameworks remain fragmented, sector-specific, and insufficiently integrated to address the adaptive and dynamic nature of modern digital environments. Therefore, this study aims to develop a unified theoretical model of AI-driven governance for adaptive digital transformation. This research employs a qualitative conceptual approach using a systematic and integrative literature review methodology. Relevant literature, governance frameworks, policy documents, and digital transformation studies were analyzed through thematic analysis and conceptual synthesis to identify the core dimensions of effective AI governance. The study integrates governance principles, ethical considerations, organizational structures, and technological mechanisms into a comprehensive multi-layered framework. The findings of the study propose a unified AI governance model consisting of interconnected dimensions, including transparency and explainability, accountability mechanisms, data governance and privacy, cybersecurity resilience, interoperability across systems, and adaptive feedback mechanisms. Compared to existing governance models, the proposed framework provides a more integrated and adaptive approach by bridging fragmented governance perspectives into a single coherent structure. In conclusion, the proposed unified AI governance model contributes theoretically to governance and AI ethics literature while providing practical guidance for governments, organizations, and technology developers in implementing responsible, transparent, and sustainable AI governance systems to support adaptive digital transformation.
Real-time human detection on FPV drones using YOLOv11 and ESP-NOW Aria Kusumah Sastradinata; Bagus Hendra Saputra; Rifky Adishatya; Gumayang Fitri Annisa; Lusy Amelia; Belinda Zhafira; Mukhamad Ayx T Zus Rizal Tofa
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 2 (2026): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol18.2026.1609.pp133-150

Abstract

Conventional aerial surveillance systems still rely heavily on human operators, which may lead to visual fatigue, limited monitoring coverage, and delayed responses during security patrol operations. This study proposes a real-time human detection system for FPV drone surveillance using the YOLOv11 object detection model integrated with ESP-NOW wireless communication. The proposed system incorporates temporal validation and human-in-the-loop confirmation to improve detection reliability and maintain operator control during response activation. Experimental evaluations were conducted under morning, afternoon, and evening conditions. The proposed system achieved average confidence values of 81.25%, 78.38%, and 79.88%, with detection success rates of 71.13%, 75.94%, and 78.03%, respectively. Furthermore, the ESP-NOW communication subsystem successfully transmitted activation signals with delays ranging from 7 ms to 53 ms and maintained stable communication over distances up to 300 m. The main contribution of this research lies in the integration of YOLOv11, temporal validation, human-in-the-loop confirmation, and ESP-NOW communication into a single UAV surveillance framework, enabling reliable real-time human detection while preserving human supervision in operational decision-making.
Implementation of BLAKE3 hashing for accelerating digital evidence integrity verification in forensic investigations Mirza Gofur Saleh; H.A. Danang Rimbawa
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 2 (2026): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol18.2026.1629.pp151-160

Abstract

The evolution of cybersecurity threats demands rapid and legally accountable investigation responses. A crucial principle in digital forensics is maintaining data integrity to ensure the validity of the chain of custody in court using cryptographic hash functions. However, the increasing volume of storage media presents significant technical challenges. Conventional algorithms like SHA-256 process data sequentially, causing hash verification on massive forensic images to take hours. This study aims to evaluate the BLAKE3 algorithm as an accelerator in the digital evidence integrity verification process. The evaluation was conducted using a comparative experimental method between MD5, SHA-256, and BLAKE3 by varying processor core allocations and simulated file sizes up to 50 GB. The test results demonstrated that parallel processing in BLAKE3 significantly reduces execution time. In the 50 GB file test utilizing 8 threads, BLAKE3 achieved a throughput of 5000 MB/s and completed verification in just 10.0 seconds, vastly outperforming SHA-256 which required 142.8 seconds. The application of BLAKE3 proved to provide security equivalent to SHA-256 while accelerating the verification process, thereby supporting more efficient courtroom proceedings without violating legal integrity standards.
Design and implementation of a weapon storage access control system based on hand gesture recognition and face recognition on Raspberry Pi 5 Daffa Rahman; Sunarta Sunarta; Bagus Hendra Saputra
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 2 (2026): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol18.2026.1633.pp161-170

Abstract

This study presents a multimodal biometric access control system for weapon storage facilities, integrating hand gesture recognition and face recognition through a sequential fusion architecture on Raspberry Pi 5. The sequential design activates face verification only after correct gesture authentication, optimizing computational efficiency on edge hardware while establishing a dual-layer security barrier. The gesture module combines MediaPipe Hands landmark extraction with LSTM-based temporal classification, achieving near-perfect accuracy across four gesture classes. The face module employs dlib's ResNet-34 for 128-dimensional embedding comparison, with an empirically recalibrated Euclidean distance threshold of 0.34 to eliminate false acceptance risks identified during intrusion testing. Evaluation under controlled conditions yielded 0% False Reject Rate and 0% False Accept Rate across 60 trials, with reliable GPIO-controlled solenoid actuation. Results demonstrate that sequential fusion of behavioral and physiological biometrics on a single edge device provides a viable security solution for high-risk access control applications.
Decision Support System for Determining Cyber Risk Mitigation Priorities in Higher Education Using the Fuzzy TOPSIS Method Fristi Riandari; Hengki Tamando Sihotang
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 2 (2026): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The increasing frequency and sophistication of cyber threats have made higher education institutions attractive targets for cyberattacks, posing significant risks to information assets, academic operations, and institutional reputation. Universities rely heavily on digital technologies, including academic information systems, e-learning platforms, cloud services, and research databases, making effective cybersecurity risk management essential. However, limited cybersecurity resources often prevent institutions from addressing all potential threats simultaneously, highlighting the need for a systematic approach to prioritizing cyber risk mitigation efforts. This study aims to develop a Decision Support System (DSS) for determining cyber risk mitigation priorities in higher education institutions using the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) method. Six evaluation criteria were considered, namely probability of occurrence, financial impact, operational impact, reputation damage, data sensitivity, and recovery complexity. Expert assessments were expressed using linguistic variables and converted into Triangular Fuzzy Numbers (TFNs) to accommodate uncertainty in the decision-making process. The Fuzzy TOPSIS method was then applied to evaluate and rank cyber risks according to their mitigation priorities. The results demonstrated that the proposed DSS successfully generated a prioritized ranking of cyber risks, with ransomware and data breach risks receiving the highest mitigation priorities due to their substantial impacts on university operations, financial resources, and information security. The findings suggest that the developed DSS effectively supports cybersecurity decision-making by handling uncertainty in expert assessments and providing systematic recommendations for cyber risk mitigation. Consequently, the proposed framework can assist higher education institutions in allocating cybersecurity resources more efficiently and enhancing their overall cybersecurity resilience.
Machine Learning Integration in DEA Models: Current Developments and Future Challenges Hengki Tamando Sihotang; Fristi Riandari; Rasenda Rasenda; Wildan Alrasyid
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 2 (2026): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The increasing availability of large and complex datasets has created new opportunities for enhancing Data Envelopment Analysis (DEA) through the integration of Machine Learning (ML) techniques. This study reviews current developments in the integration of ML and DEA models and identifies key challenges, trends, and future research opportunities. A systematic literature review was conducted by examining recent studies that combine DEA with various machine learning algorithms across multiple application domains, including healthcare, banking and finance, manufacturing, supply chain management, energy, agriculture, and higher education. The findings indicate that Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests, Gradient Boosting methods, and Deep Learning models are among the most frequently employed techniques in DEA-ML frameworks. Despite these advantages, several challenges remain, including data quality issues, model interpretability, computational complexity, limited generalizability, and the lack of standardized integration frameworks. The review concludes that the integration of ML and DEA offers substantial potential for advancing efficiency analysis and organizational performance evaluation. Future research should focus on developing explainable artificial intelligence (XAI) solutions, real-time efficiency analytics, federated learning approaches, and standardized hybrid DEA-ML frameworks to improve transparency, scalability, and practical applicability across diverse operational environments.
Analysis of Household Energy Consumption Patterns Using K-Means Clustering and Explainable Data Mining Riley Emerson; Genevieve Genevieve
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 2 (2026): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The increasing demand for household energy has created significant challenges for energy sustainability, resource management, and the development of effective energy efficiency strategies, thereby necessitating advanced analytical approaches to better understand residential consumption behavior. This study aims to analyze household energy consumption patterns using K-Means clustering and explainable data mining techniques. Household energy consumption data were collected from residential users and subjected to preprocessing procedures, including data cleaning, missing value handling, feature selection, and normalization to ensure data quality and analytical reliability. The K-Means clustering algorithm was then applied to identify homogeneous groups of households based on their energy consumption characteristics, while explainable data mining techniques were employed to interpret cluster profiles and determine the factors influencing cluster membership. The results revealed the existence of three distinct household energy consumption groups, namely low-, moderate-, and high-consumption households, each exhibiting significantly different consumption behaviors, appliance ownership levels, and energy expenditure patterns. Further analysis showed that household size, appliance ownership, and peak electricity usage were the most influential factors differentiating the clusters. These findings demonstrate that the integration of K-Means clustering and explainable data mining provides an effective and interpretable framework for understanding household energy consumption behavior. The proposed approach offers valuable insights for utility companies, policymakers, and consumers by supporting targeted energy efficiency programs, demand-side management initiatives, and evidence-based energy policy development aimed at promoting sustainable household energy consumption.
Development of an Explainable Expert System for Smart Factory Readiness Assessment in Manufacturing Industries Sandor Krizstian
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 2 (2026): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Smart Factory adoption has become a critical strategy for manufacturing industries seeking to improve productivity, operational flexibility, and global competitiveness in the era of Industry 4.0. However, many organizations still lack a systematic, reliable, and transparent approach to evaluating their readiness for Smart Factory implementation. This study aims to develop an Expert System for determining industry readiness for Smart Factories by integrating expert knowledge and Explainable Artificial Intelligence (XAI). Expert knowledge was acquired through interviews with Industry 4.0 specialists, manufacturing practitioners, and automation experts, as well as an extensive literature review to identify readiness criteria related to technology, organization, human resources, processes, and financial capability. To enhance transparency and user trust, Explainable AI techniques were incorporated to provide interpretable explanations and feature contribution analyses for readiness recommendations. The system was validated through expert evaluation and case studies involving manufacturing organizations. The results indicate that the proposed system successfully classified organizations into five readiness levels and generated clear, understandable explanations for each recommendation. Validation findings demonstrated a high level of agreement between system outputs and expert assessments, confirming the reliability and practical applicability of the proposed approach. Furthermore, feature contribution analysis revealed that automation level, workforce digital skills, and IoT infrastructure were the most influential determinants of Smart Factory readiness.

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