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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Academic Journal of Computer Science Research (AJCSR) Computer Architecture and Signal Processing Cyber Security and Network Management
Bobi Kurniawan S
Universitas Komputer Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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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.
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.
INDEKS RISIKO BENCANA PARIWISATA DAN KLASTER KABUPATEN KOTA JAWA BARAT BERBASIS DATA WISATA 2020–2024: TOURISM DISASTER RISK INDEX AND CLUSTERING OF WEST JAVA REGENCIES AND CITIES BASED ON TOURISM DATA (2020–2024) Ucu Nugraha; Sri Titi Handayani; Hernalom Sitorus; Bobi Kurniawan S; Adam Mukharil Bachtiar; Ednawati Rainarli; Hanhan Maulana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7294

Abstract

West Java Province is one of Indonesia’s leading tourism destinations and, at the same time, a region with a high incidence of disasters. However, available disaster risk information such as the Indonesian Disaster Risk Index and the West Java Provincial Disaster Risk Assessment remains broad in scope and has not explicitly integrated the tourism dimension. This study aims to develop a Tourism Disaster Risk Index at the regency/municipality level in West Java Province by utilizing data on the number of disaster events, the number of disaster victims during the 2020–2024 period, and the number of tourism destination objects, while also clustering regions to construct a disaster-based typology of tourism risk. The methods include: (1) aggregating five-year disaster event and victim data by regency/municipality; (2) calculating the total number of tourism destination objects (natural, cultural, and man-made); (3) applying min–max normalization to produce partial indices for events, victims, and tourism destination objects; (4) constructing a composite Tourism Disaster Risk Index using weights of 0.4:0.4:0.2, in which hazard (events) and impact (victims) are deliberately assigned greater weights than tourism exposure as a conceptual decision aligned with disaster risk frameworks that prioritize life safety and physical damage; and (5) applying the K-Means algorithm (k = 3) to perform clustering based on the partial indices. The results show that the Tourism Disaster Risk Index (0–100 scale) ranges from 0.76 to 56.70, with a mean of 12.27 and a median of 6.90. A total of 25 regencies/municipalities fall into the low tourism risk category, while Bogor Regency and Cianjur Regency are in the moderate category. The clustering yields three clusters: cluster 1 comprises 21 regencies/municipalities with relatively low tourism risk; cluster 2 includes five regencies (Bogor, Bandung, Garut, Majalengka, and Pangandaran) characterized by moderate risk and a high concentration of destinations; and cluster 3 consists solely of Cianjur Regency as an outlier with exceptionally high disaster impacts. These findings provide a quantitative foundation as well as a prototype decision-support tool for local governments and stakeholders to prioritize resources and design interventions for disaster-resilient tourism development in West Java Province.  
Integrasi Gemini AI Berbasis Retrieval-Augmented Generation (RAG) pada Moodle untuk Penilaian Esai Otomatis dengan Pendekatan Human-in-the-Loop di Pendidikan Tinggi Rizki Adha; Lusianto Lusianto; Dodi Syaripudin; Bobi Kurniawan S; Adam Mukharil Bachtiar; Hanhan Maulana; Ednawati Rainarli
Academic Journal of Computer Science Research Vol 8, No 1 (2026): Academic Journal of Computer Science Research (AJCSR)
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/ajcsr.v8i1.16253

Abstract

Penilaian esai merupakan komponen penting dalam evaluasi pembelajaran di pendidikan tinggi, namun proses penilaian manual oleh dosen membutuhkan waktu yang besar dan berpotensi menimbulkan inkonsistensi, terutama pada kelas dengan jumlah mahasiswa yang besar. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi sistem penilaian esai otomatis berbasis Gemini AI yang terintegrasi dengan Learning Management System (LMS) Moodle menggunakan pendekatan Retrieval-Augmented Generation (RAG) dan mekanisme Human-in-the-Loop (HIL). Penelitian menggunakan metode Research and Development (R&D) dengan model ADDEI, yang meliputi tahap analisis, perancangan, pengembangan, evaluasi, dan implementasi sistem. Evaluasi sistem dilakukan melalui pengujian fungsional (black-box testing), serta kuesioner Human-in-the-Loop yang melibatkan 24 dosen dari 14 perguruan tinggi swasta di wilayah Banten, DKI Jakarta, dan Jawa Barat. Hasil pengujian menunjukkan bahwa seluruh fungsi sistem berjalan sesuai dengan spesifikasi. Evaluasi HIL menunjukkan tingkat penerimaan yang tinggi hingga sangat tinggi, terutama pada indikator peran AI sebagai decision-support system dan tanggung jawab akademik dosen. Selain itu, hasil validasi dosen menunjukkan bahwa sebagian besar rekomendasi skor dan umpan balik yang dihasilkan oleh sistem dapat diterima, dengan dosen tetap memiliki kendali penuh dalam menentukan nilai akhir. Temuan ini menunjukkan bahwa integrasi Gemini AI berbasis RAG dengan mekanisme Human-in-the-Loop efektif sebagai sistem pendukung penilaian esai yang efisien, akuntabel, dan sesuai dengan kebutuhan penilaian akademik di pendidikan tinggi.
Co-Authors Adam Mukharil Bachtiar Agus Nursikuwagus Citra Noviyasari Dodi Syaripudin Ednawati Rainarli Hasbu Naim Syaddad Hernalom Sitorus Lusianto Lusianto Maulana, Hanhan Rizki Adha Sri Supatmi Sri Titi Handayani Ucu Nugraha Ucu Nugraha Zainal Arifin Hasibuan