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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Digit : Digital of Information Technology Technologia Journal Integrated System and Management Technology Indonesian Journal of Infomatics Computer Architecture and Signal Processing Software Engineering in Computing Systems Big Data Analytics and Data Science Cyber Security and Network Management Jurnal Nasional Komputasi dan Teknologi Informasi
Zainal Arifin Hasibuan
Universitas Komputer Indonesia
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Neuroscience Transportation

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Systematic Literature Review (SLR) On Consensus Mechanism In Cyber-Physical System (CPS) For Smart Farm Performance Optimization) Sri Titi Handayani; Zainal Arifin Hasibuan; Sri Supadmi
Cyber Security and Network Management Vol. 1 No. 2 (2026): May: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i2.393

Abstract

This study aims to analyze the consensus mechanism on the Cyber-Physical System (CPS) to optimize smart farm performance through the Systematic Literature Review (SLR) approach. CPS integration is a fundamental component in smart farms as it allows real-time coordination of sensors, actuators, and computing devices to produce accurate and adaptive farming decisions. However, a dynamic and heterogeneous farming environment demands an efficient, stable, and energy-efficient consensus mechanism so that all nodes in the network can reach a consistent agreement on data or actions. Through SLR on 30 studies, this study found that the consensus mechanism was able to increase sensor synchronization by up to 30%, reduce latency by 27%, decrease water consumption by 19%, increase sensor response by 31%, and improve data security by up to 22%. Several consensus approaches such as average consensus, multi-sensor consensus, secure consensus, edge-based consensus, and fault-tolerant consensus have been proven to improve the accuracy of environmental monitoring, accelerate automatic irrigation response, optimize precision fertilization, and strengthen information search in unstable signal conditions. In addition, consensus in CPS shows a significant role in handling smart farm big data as well as strengthening system resilience to disruptions. However, this study also identified a research gap related to the need for a consensus model that is lighter, adaptive, and in accordance with the characteristics of tropical agriculture such as in Indonesia. These SLR findings provide a direction for the development of more efficient, secure, and sustainable consensus-based CPS for future smart farm implementation.
Machine Learning Model Development for Adaptive Recruitment Recommendation System Based on Portfolio Analysis and Professional Network Rizki Adha; Zainal Arifin Hasibuan; Bobi Kurniawan; Sri Supatmi
Cyber Security and Network Management Vol. 1 No. 2 (2026): May: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i2.410

Abstract

The rapid advancement of digital transformation and artificial intelligence has significantly reshaped recruitment processes within organizations. Conventional recruitment systems predominantly rely on curriculum vitae screening and keyword-based matching, which often fail to capture contextual competencies and relational professional evidence. This study proposes the development of an adaptive machine learning–based recruitment recommendation system that integrates professional portfolio analytics and professional network structures within a unified graphbased framework. The proposed approach adopts a Research and Development (R&D) methodology under a data-driven system development paradigm. Candidate data from an existing recruitment system are integrated with external professional data sources, including GitHub and LinkedIn. A heterogeneous graph representation is constructed to model relationships among candidates, skills, projects, and organizations. Graph Neural Networks (GNN) are employed to learn contextual relational embeddings, while a Gradient Boosting Machine (GBM) is utilized for candidate job suitability classification. The proposed framework is designed to enhance objectivity, contextual awareness, and adaptability in recruitment decision-making. By leveraging multi-source digital professional evidence and incorporating an adaptive learning mechanism, the system aims to reduce skills mismatch and improve alignment between candidate competencies and evolving industry requirements. Future work will focus on empirical validation using real-world recruitment datasets and the integration of fairness-aware and explainable AI mechanisms to ensure transparency and ethical compliance.
Predictive decision support for underutilization risk in public sector tourism: Evidence mapping and a design science roadmap Ucu Nugraha; Zainal Arifin Hasibuan; Bobi Kurniawan S; Sri Supatmi; Agus Nursikuwagus; Citra Noviyasari
Cyber Security and Network Management Vol. 1 No. 2 (2026): May: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i2.440

Abstract

Publicly funded tourism assets can become stranded when utilization persistently falls below a reasonable level relative to capacity or policy-defined potential. Yet tourism analytics research largely forecasts demand or composite performance and seldom formalizes underutilization as a governance outcome, nor evaluates decision quality within planning and budgeting workflows. This study (i) maps recent evidence and research gaps and (ii) proposes a conceptual artefact in the form of a policy-ready methodology and roadmap for developing a predictive decision support system (DSS) to mitigate underutilization risk. An evidence-mapping review of 117 Scopus-indexed studies (2021–2026) reveals a critical gap: 0% of the analyzed studies explicitly formalize "underutilization" as a policy outcome in their titles. Furthermore, evaluation procedures remain opaque, with 79.5% of studies failing to clearly specify their methodologies. In response, we outline a design-science roadmap for an auditable predictive DSS that operationalizes underutilization through two complementary metrics: the Underutilization Gap (UG) and the Utilization Ratio (UR). The proposed architecture integrates heterogeneous tourism, spatial, and socio economic data while providing traceable audit trails via Explainable AI (XAI) to ensure scores are logically defensible in public budgeting. Crucially, the framework introduces a two-layer evaluation that couples technical predictive performance (E1) with decision-utility metrics (E2), such as rank agreement and allocation efficiency. This methodology equips local governments with a practical, theoretically grounded instrument to justify prioritization, optimize resource allocation, and reduce the likelihood of underutilization-related policy failure.
A Design Science Roadmap for Auditable Ocular Disease Classification: Evidence Mapping of AI Governance Gaps Rizal Rachman; Eddy Soeryanto Soegoto; Irawan Afrianto; Irfan Dwiguna sumitra; Zainal Arifin Hasibuan
Integrated System and Management Technology Vol. 1 No. 2 (2026): April: Integrated System and Management Technology
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/ismat.v1i2.442

Abstract

The integration of Artificial Intelligence (AI) into ocular diagnostics has led to substantial improvements in predictive accuracy. However, a persistent gap remains between technical performance and clinical accountability. The present study addresses the "accuracy trap" and the lack of transparency in current deep learning models for ocular disease classification. The objective of the research is twofold: firstly, to identify methodological deficiencies in extant literature and, secondly, to propose a standardised evaluative framework to ensure model auditability. A systematic evidence mapping (SEM) approach, combined with design science research methodology (DSRM), was utilised to scrutinise 10 high-impact Scopus-indexed studies published between 2023 and 2026. The findings reveal a critical "predictive validity gap," where 80% of the evidence base relies on aggregate accuracy while 90% remains "black box" without functional Explainable AI (XAI) layers. The synthesis of these gaps resulted in the formulation of a conceptual roadmap that mandated multi-metric evaluation, incorporating Cohen's Kappa, and pathophysiological traceability. In conclusion, this research establishes that clinical deployment of AI must transition from model-centric success to a governance-oriented paradigm that prioritises decision utility and auditable audit trails. This roadmap provides a rigorous blueprint for the future implementation of transparent and accountable medical AI systems.
A Systematic Literature Review on Intelligent Tutoring Systems for Outcome-Based Education in Higher Education Hasbu Naim Syaddad; Andi Agus Salim; Luki Ishwara; Zainal Arifin Hasibuan; Bobi Kurniawan; Sri Supatmi
Technologia Journal Vol. 3 No. 1 (2026): Technologia Journal-February
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/pd6g0a26

Abstract

Penerapan Outcome-Based Education (OBE) di pendidikan tinggi menuntut pendekatan pembelajaran yang mampu mendukung pencapaian capaian pembelajaran dan kompetensi mahasiswa secara terukur. Intelligent Tutoring Systems (ITS) merupakan sistem pembelajaran berbasis kecerdasan buatan yang bersifat adaptif dan personal, sehingga berpotensi mendukung implementasi OBE. Namun, temuan empiris terkait penerapan dan efektivitas ITS dalam konteks OBE di pendidikan tinggi masih tersebar dan belum tersintesis secara sistematis. Penelitian ini bertujuan untuk mengkaji peran, karakteristik, dan efektivitas ITS dalam mendukung outcome-based education di pendidikan tinggi. Penelitian ini menggunakan metode systematic literature review dengan mengacu pada pedoman PRISMA 2020. Pencarian literatur dilakukan melalui basis data Scopus terhadap artikel jurnal berbahasa Inggris yang dipublikasikan pada periode 2018–2025. Dari proses seleksi yang ketat, sebanyak 56 artikel jurnal memenuhi kriteria inklusi dan dianalisis menggunakan pendekatan sintesis naratif. Hasil kajian menunjukkan bahwa ITS umumnya dibangun atas komponen inti berupa model peserta didik, model domain, model pedagogik, dan antarmuka tutor. Teknik kecerdasan buatan yang banyak digunakan meliputi machine learning, rule-based systems, Bayesian networks, dan natural language processing. Sebagian besar studi melaporkan bahwa ITS berdampak positif terhadap kinerja akademik, penguasaan kompetensi, dan keterlibatan mahasiswa. Meskipun demikian, penelitian lanjutan masih diperlukan untuk mengevaluasi dampak jangka panjang dan integrasi ITS dalam kerangka OBE di tingkat institusi.  
Profil Literasi Digital Mahasiswa Berdasarkan Empat Pilar Kominfo Menggunakan Algoritma K-Means Muhamad Nawawi; Ridwan Zulkifli; Dony Waluya Firdaus; Zainal Arifin Hasibuan; Bobi Kurniawan; Sri Supatmi

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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10375

Abstract

Abstrask - Transformasi digital menuntut mahasiswa memiliki literasi digital yang tidak hanya mencakup kemampuan teknis, tetapi juga kesadaran etika dan keamanan digital. Penelitian ini bertujuan untuk memetakan tingkat literasi digital mahasiswa serta mengidentifikasi karakteristik klaster menggunakan algoritma K-Means. Penelitian menggunakan pendekatan kuantitatif dengan metode survei terhadap mahasiswa, berdasarkan empat dimensi literasi digital, yaitu digital skills, digital culture, digital ethics, dan digital safety. Hasil analisis K-Means menunjukkan terbentuknya dua klaster utama, yaitu klaster literasi digital matang (Cluster 1) dan klaster literasi digital belum matang (Cluster 2). Cluster 1 memiliki nilai rata-rata literasi digital sebesar 4,57, sedangkan Cluster 2 sebesar 3,88. Dimensi dengan perbedaan paling signifikan antar klaster adalah digital safety (selisih 0,77) dan digital ethics (selisih 0,72). Analisis karakteristik responden menunjukkan bahwa mahasiswa angkatan aktif (2022–2024) lebih banyak berada pada Cluster 1 (hingga 67%), dibandingkan mahasiswa baru (2025) dan mahasiswa over study (2021) yang didominasi Cluster 2. Selain itu, mahasiswa luar Jawa Barat memiliki proporsi literasi digital matang yang lebih tinggi (64%) dibandingkan mahasiswa asal Jawa Barat (51%). Temuan ini menegaskan bahwa literasi digital mahasiswa bersifat dinamis dan kontekstual, sehingga penguatan literasi digital di perguruan tinggi perlu difokuskan pada aspek etika dan keamanan digital secara berkelanjutan.Kata kunci: literasi digital; mahasiswa; K-Means clustering; digital ethics; digital safety; Abstract - Digital transformation demands that students possess digital literacy encompassing not only technical skills but also ethical awareness and digital security. This study aims to map the level of digital literacy of students and identify cluster characteristics using the K-Means algorithm. The study employed a quantitative approach with a student survey method, based on four dimensions of digital literacy: digital skills, digital culture, digital ethics, and digital safety. The results of the K-Means analysis indicate the formation of two main clusters: a mature digital literacy cluster (Cluster 1) and an immature digital literacy cluster (Cluster 2). Cluster 1 has an average digital literacy score of 4.57, while Cluster 2 has an average digital literacy score of 3.88. The dimensions with the most significant differences between clusters are digital safety (difference 0.77) and digital ethics (difference 0.72). An analysis of respondent characteristics shows that active students (2022–2024) are predominantly in Cluster 1 (up to 67%), compared to new students (2025) and overstudy students (2021), who are predominantly in Cluster 2. Furthermore, students from outside West Java have a higher proportion of mature digital literacy (64%) than students from West Java (51%). This finding confirms that student digital literacy is dynamic and contextual, thus strengthening digital literacy in higher education requires a continuous focus on digital ethics and security.Keywords: digital literacy; students; K-Means clustering; digital ethics; digital safety;
Symantic Literatur Review : Artificial Intelligence dalam Telemedicine dan Remote Patient Monitoring Diana Effendi; Sri Nurhayati; Zainal Arifin Hasibuan; Bobi Kurniawan S.; Sri Supatmi
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v15i1.18702

Abstract

The development of telemedicine and Remote Patient Monitoring (RPM) is increasing along with the need for efficient, adaptive, and data-driven remote healthcare services. Artificial Intelligence (AI) plays a crucial role in strengthening these systems through predictive analysis, medical classification, and real-time patient monitoring. However, research on AI integration in telemedicine and RPM remains scattered and exhibits wide methodological variation, necessitating a systematic review to understand the consistency of findings and the direction of research development. This study conducted a Systematic Literature Review (SLR) following the PRISMA 2020 protocol, analyzing 128 publications from 2020–2025 obtained from Scopus, PubMed, IEEE Xplore, and Google Scholar. This study combined SLR synthesis with bibliometric mapping (co-occurrence and thematic mapping) to highlight the evolution of themes and topical interrelationships more explicitly. Bibliometric analysis results show an increase in the number of publications from 12 articles in 2020 to 45 articles in 2024, a nearly fourfold increase, before stabilizing in 2025. Co-occurrence and thematic mapping findings reveal four main themes: telemedicine–AI, computational methods based on machine learning and deep learning, physiological monitoring, and human factors in clinical evaluation. The study also identifies several challenges, including data security, signal quality, model transparency, and healthcare worker readiness. Theoretically, the findings emphasize that AI integration in telemedicine–RPM needs to be understood as a socio-technical issue that demands human-centered evaluation. Policy-wise, strengthening data governance and clinical validation standards is necessary for more accountable and secure implementation. This study concludes that AI plays a central role in the development of telemedicine and RPM, but further studies are needed on service personalization, multimodal data integration, and large-scale clinical validation.
Profil Literasi Digital Mahasiswa Berdasarkan Empat Pilar Kominfo Menggunakan Algoritma K-Means Muhamad Nawawi; Ridwan Zulkifli; Dony Waluya Firdaus; Zainal Arifin Hasibuan; Bobi Kurniawan; Sri Supatmi
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 1 (2026): Februari, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.212

Abstract

Abstrask - Transformasi digital menuntut mahasiswa memiliki literasi digital yang tidak hanya mencakup kemampuan teknis, tetapi juga kesadaran etika dan keamanan digital. Penelitian ini bertujuan untuk memetakan tingkat literasi digital mahasiswa serta mengidentifikasi karakteristik klaster menggunakan algoritma K-Means. Penelitian menggunakan pendekatan kuantitatif dengan metode survei terhadap mahasiswa, berdasarkan empat dimensi literasi digital, yaitu digital skills, digital culture, digital ethics, dan digital safety. Hasil analisis K-Means menunjukkan terbentuknya dua klaster utama, yaitu klaster literasi digital matang (Cluster 1) dan klaster literasi digital belum matang (Cluster 2). Cluster 1 memiliki nilai rata-rata literasi digital sebesar 4,57, sedangkan Cluster 2 sebesar 3,88. Dimensi dengan perbedaan paling signifikan antar klaster adalah digital safety (selisih 0,77) dan digital ethics (selisih 0,72). Analisis karakteristik responden menunjukkan bahwa mahasiswa angkatan aktif (2022–2024) lebih banyak berada pada Cluster 1 (hingga 67%), dibandingkan mahasiswa baru (2025) dan mahasiswa over study (2021) yang didominasi Cluster 2. Selain itu, mahasiswa luar Jawa Barat memiliki proporsi literasi digital matang yang lebih tinggi (64%) dibandingkan mahasiswa asal Jawa Barat (51%). Temuan ini menegaskan bahwa literasi digital mahasiswa bersifat dinamis dan kontekstual, sehingga penguatan literasi digital di perguruan tinggi perlu difokuskan pada aspek etika dan keamanan digital secara berkelanjutan. Kata kunci: literasi digital; mahasiswa; K-Means clustering; digital ethics; digital safety; Abstract - Digital transformation demands that students possess digital literacy encompassing not only technical skills but also ethical awareness and digital security. This study aims to map the level of digital literacy of students and identify cluster characteristics using the K-Means algorithm. The study employed a quantitative approach with a student survey method, based on four dimensions of digital literacy: digital skills, digital culture, digital ethics, and digital safety. The results of the K-Means analysis indicate the formation of two main clusters: a mature digital literacy cluster (Cluster 1) and an immature digital literacy cluster (Cluster 2). Cluster 1 has an average digital literacy score of 4.57, while Cluster 2 has an average digital literacy score of 3.88. The dimensions with the most significant differences between clusters are digital safety (difference 0.77) and digital ethics (difference 0.72). An analysis of respondent characteristics shows that active students (2022–2024) are predominantly in Cluster 1 (up to 67%), compared to new students (2025) and overstudy students (2021), who are predominantly in Cluster 2. Furthermore, students from outside West Java have a higher proportion of mature digital literacy (64%) than students from West Java (51%). This finding confirms that student digital literacy is dynamic and contextual, thus strengthening digital literacy in higher education requires a continuous focus on digital ethics and security. Keywords: digital literacy; students; K-Means clustering; digital ethics; digital safety;
Co-Authors Agus Nursikuwagus Andi Agus Salim Bella Hardiyana Bobby Kurniawan Bobi Kurniawan S Bobi Kurniawan S. Bobi Kurniawan, Bobi Citra Noviyasari Diana Effendi Eddy Soeryanto Soegoto Ednawati Rainarli Firdaus, Dony Waluya Hani Irmayanti Hasbu Naim Syaddad Hasbu Naim Syaddad Hidayat Hidayat Ifan Dwiguna Irawan Afrianto Irfan Dwiguna Sumitra Luki Ishwara Muhamad Nawawi Nanang Abdurahman Petrus Sokibi Ridho Taufiq Subagio Rifqi Fahrudin Rio Yunanto Rizal Rachman Rizki Adha Siti Ar-Rachmi Ningrum Sri Nurhayati Sri Supadmi Sri Supatmi Sri Titi Handayani zulkifli, ridwan