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All Journal Jurnal Digit : Digital of Information Technology MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA 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
Sri Supatmi
Universitas Komputer Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Industrial & Manufacturing Engineering

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Transforming the Global Aquaculture Supply Chain through the Integration of Artificial Intelligence and Big Data for Overcome Asymmetry Information Hernalom Sitorus; Zaenal Arifin Hasibuan; Bobi Kurniawan; Sri Supatmi
Big Data Analytics and Data Science Vol. 1 No. 2 (2026): June: Big Data Analytics and Data Science
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/bdas.v1i2.443

Abstract

The global aquaculture sector faces structural challenges in the form of information asymmetry that causes a misalignment between production and market demand. The still-dominant production-driven paradigm leads to supply chain inefficiencies, low transparency, and limited traceability. This research aims to develop an information system integration model based on Artificial Intelligence (AI) and Big Data to transform the supply chain into a market-driven one. The research uses the Design Science Research (DSR) method, which includes needs analysis, data integration architecture design, development of Machine Learning and Deep Learning-based predictive models, and evaluation through prototype implementation. Expected outcomes include a data integration architecture, a supply-demand prediction model, and an AI-based traceability framework. This research contributes to improving the efficiency, transparency, and global competitiveness of the aquaculture sector.
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.
Co-Authors Agis Abhi Rafdhi Agus Nursikuwagus Andi Agus Salim Bella Hardiyana Bobby Kurniawan Bobi Kurniawan S Bobi Kurniawan, Bobi Citra Noviyasari Eddy Soeryanto Soegoto Ednawati Rainarli Hani Irmayanti Hasbu Naim Syaddad Hernalom Sitorus Herry Saputra Hidayat Hidayat Irawan Afrianto Lia Warlina Luki Ishwara Petrus Sokibi Ridho Taufiq Subagio Rifqi Fahrudin Rio Yunanto Rizki Adha Rizky Jumansyah Siti Ar-Rachmi Ningrum Sri Nurhayati Ucu Nugraha Zaenal Arifin Hasibuan Zainal Arifin Hasibuan zulkifli, ridwan