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All Journal Computer Architecture and Signal Processing Cyber Security and Network Management
Citra Noviyasari
Universitas Komputer Indonesia

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Data-Driven Learning Analytics Conceptual Framework for Automated Competency Mapping in Outcome-Based Education: A Design Science Research Approach Hasbu Naim Syaddad; Zainal Arifin Hasibuan; Bobi Kurniawan S; Sri Supatmi; Agus Nursikuwagus; Citra Noviyasari
Computer Architecture and Signal Processing Vol. 1 No. 2 (2026): June: Computer Architecture and Signal Processing
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/casp.v1i2.396

Abstract

The implementation of Outcome-Based Education (OBE) in higher education demands precise measurement of Graduate Learning Outcomes (CPL) and Course Learning Outcomes (CPMK). However, current conventional Learning Management Systems (LMS) remain static and centered on final performance metrics (grades), thus failing to map student academic profiles into sub-competencies in a real-time and granular manner. This study proposes a conceptual artifact in the form of an Intelligent Tutoring System (ITS) architecture based on Learning Analytics (LA) and Knowledge Graphs to automate competency mapping. Through the Design Science Research Methodology (DSRM) approach, this framework designs a data fusion pipeline that integrates high-resolution academic log data with curriculum ontologies. The proposed architecture consists of three main layers: data acquisition, predictive modeling using Machine Learning, and a recommendation engine based on Explainable AI (XAI). This conceptual framework provides a blueprint for higher education institutions to transform from reactive curriculum evaluation into precise and auditable adaptive learning governance.
Adaptive-Cognitive Smart Farming Architectures for Food Security Resilience: A Systematic Literature Review of IoT and AI-Based Approaches Ridho Taufiq Subagio; Zainal Arifin Hasibuan; Bobby Kurniawan; Sri Supatmi; Hidayat Hidayat; Citra Noviyasari
Computer Architecture and Signal Processing Vol. 1 No. 2 (2026): June: Computer Architecture and Signal Processing
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/casp.v1i2.445

Abstract

Food security resilience has become an increasingly critical global concern due to the combined effects of climate change, population growth, and resource scarcity. Conventional agricultural practices are no longer sufficient to meet rising food demands, thereby necessitating the adoption of intelligent and adaptive technological solutions. Smart farming, enabled by the integration of the Internet of Things (IoT) and Artificial Intelligence (AI), has emerged as a promising approach to enhance agricultural productivity, efficiency, and sustainability. However, existing smart farming systems remain fragmented and lack adaptive and cognitive capabilities required to dynamically respond to environmental variability. This study proposes an adaptive-cognitive smart farming architecture that integrates IoT, AI, edge-fog-cloud computing, federated learning, and digital twin technologies into a unified framework. A Systematic Literature Review (SLR) is conducted to synthesize insights from 60 high-quality publications indexed in IEEE, Elsevier, and Scopus databases. The proposed architecture adopts a multi-layered design consisting of sensing, edge-fog, cloud, cognitive, and application layers, enabling real-time data processing, distributed intelligence, and adaptive decision-making. To validate the proposed model, experimental simulations are performed using key performance indicators, including accuracy, mean squared error (MSE), latency, and resource efficiency. The results indicate that the proposed approach achieves superior performance, with an accuracy of 89%, a substantial reduction in latency, and improved resource utilization. These findings demonstrate that incorporating adaptive and cognitive intelligence significantly enhances system responsiveness and decision-making capabilities. This study contributes to both theory and practice by introducing a comprehensive framework for next-generation smart farming systems, ultimately supporting food security resilience in an increasingly uncertain environment.
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 Agus Nursikuwagus Bobby Kurniawan Bobi Kurniawan S Hasbu Naim Syaddad Hidayat Hidayat Ridho Taufiq Subagio Sri Supatmi Ucu Nugraha Zainal Arifin Hasibuan