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All Journal Media Jurnal Informatika Journal of Renewable Engineering Technologia Journal Integrated System and Management Technology
Luki Ishwara
Universitas Komputer Indonesia
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Integrated and Spatiotemporal Predictive Data-Driven Narcotics Intelligence Ecosystem: Governance, Interoperability, and Analytics Pipeline for Evidence-Based Policy : Case Study: BNNP West Java, Indonesia Luki Ishwara; Agus Nursikuwagus; Ednawati Rainarli; Zainal Arifin Hasibuan; Sri Supatmi
Integrated System and Management Technology Vol. 1 No. 2 (2026): July: 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.v1i2.436

Abstract

Across BNN, police, health, corrections, and local government, Indonesia's crossagen cynarcotics control produces a lot of data yet fragmented, limiting timely identification of abuse patterns,hotspots, and resource requirements. It aims to bridge the gap between international breakthroughson the use of machine-learning–based monitoring and optimization under uncertainty and underdeveloped provincial integration of governance, interoperability, and predictive analytics in the public sector. It is about designing and assessing an integrated spatiotemporal predictive, datadriven narcotics intelligence ecosystem for BNNP West Java. The approach combines iterative information systems engineering with an embedded case study and a mixed-methods evaluation covering seven phases: requirements structuring; data governance and quality; federated/hybrid interoperability and Privacy-Preserving Record Linkage; spatiotemporal predictive pipelines with both baseline and advanced models and anomaly detection; hotspot and risk mapping; early warning and situational dashboards linked to operational protocols; and implementation assessment with institutional learning.Evaluation utilizes quantitative measurements for data quality and model performance (including lead time and false-alarm considerations) and qualitative findings evaluating governance readiness and usability. Expected outputs can comprise a four-pillar framework bridging governance and policy impact, replicable artefacts to be deployed at the provincial level, and implications for evidence-based narcotics policy under national digital-government agendas, with considerations for data-access andprivacy limitations.
The Navigating the Data Labyrinth: A Bibliometrix of Data Governance Challenges in Implementing Digital Twins for Disaster Management in Developing Countries Andi Agus Salim; Hasbu Naim Syaddad; Luki Ishwara; Irawan; Estiko Rijanto; Irfan Dwiguna
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6295

Abstract

Indonesia faces significant disaster risks due to its location in the Ring of Fire, necessitating advanced mitigation technologies like Digital Twins (DT). However, the effectiveness of DT relies heavily on real-time data integration, which is often hindered by governance issues rather than technological capability. This study aims to identify specific data governance challenges in adopting DT for the public sector specifically in disaster management and proposes a conceptual framework suitable for developing countries, using Indonesia as the primary representative case. A Bibliometric analysis was conducted using the PRISMA protocol. Data was collected from Scopus (n=107) and Google Scholar/PoP, covering the period 2018–2026, focusing on the intersection of Digital Twin, Disaster Management, and Data Governance. Additionally, a qualitative case study approach was employed, utilizing Indonesia as the primary representation of developing countries to validate the proposed framework. The study identifies three key challenge dimensions: (1) Organizational (data silos and ownership ambiguity), (2) Technical (semantic interoperability and legacy systems), and (3) Legal-Ethical (data privacy and sovereignty). The paper proposes the "Integrated Disaster Data Governance for Digital Twin (IDDG-DT)" framework, which aligns with the Satu Data Indonesia policy, emphasizing that robust data governance is a prerequisite for successful Digital Twin implementation.
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.  
Computer Vision Analysis for Traffic Monitoring and Road Safety in Smart City Concept Luki Ishwara; Hasbu Naim Syaddad; Andi Agus Salim; Bobi Kurniawan; Adam Mukharil Bachtiar; Ednawati Rainarli
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/vk562576

Abstract

Rapid urban growth and rising traffic complexity require Smart City solutions that move beyond passive CCTV toward intelligent, real-time traffic management. This study examines how computer vision–based analytics contribute to road safety when integrated into an Intelligent Transportation System (ITS). A quantitative quasi-experimental design was applied across multiple intersections using a 12-month before–after window. Data were collected from video analytics (vehicle and pedestrian detection, tracking, violations, road conditions), adaptive signal logs, crash and injury records, near-miss indicators, and contextual variables such as weather and traffic volume. Analysis combined perception validation (mAP, tracking accuracy), time-series operational assessment, and Difference-in-Differences modeling to estimate safety impacts. Results show high perception reliability (mAP > 0.85) and significant operational improvements, including a 33% reduction in waiting time and 35% shorter queues. More importantly, red-light violations decreased by 39%, near-miss events by 45%, crash frequency by 42%, and severity index by 37%. The findings indicate a causal pathway from vision-based perception to adaptive control and enforcement, leading to measurable safety gains. The study concludes that computer vision serves as a safety governance instrument within Smart City ITS when detection outputs are tightly coupled with intervention mechanisms.   
Integrating AI-Driven Advanced Knowledge Management Systems to Mitigate Civil Servants' Risk Andi Agus Salim; Hasbu Naim Syaddad; Luki Ishwara
Technologia Journal Vol. 3 No. 3 (2026): Technologia Journal-August
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/rftf7g69

Abstract

The rapid digitalization of public administration has positioned artificial intelligence (AI) as a strategic lever for strengthening institutional knowledge and reducing operational risk among civil servants. Yet public organizations continue to struggle with fragmented knowledge repositories, tacit knowledge loss due to workforce turnover, inconsistent decision-making, and exposure to compliance, legal, and reputational risks arising from manual and siloed information practices. This study examines how an AI-Driven Advanced Knowledge Management System (AI-AKMS) can be integrated into civil service institutions to mitigate such risks. Using a systematic literature review of twenty-five peer-reviewed sources published between 2021 and 2026, the study synthesizes evidence on AI-enabled knowledge capture, retrieval-augmented generation, predictive risk analytics, and generative AI governance in public administration. The novelty of this study lies in proposing an integrated conceptual framework that links AI-based knowledge management functions directly to specific civil-service risk categories, namely compliance risk, decision risk, knowledge-continuity risk, and reputational risk, an integration rarely addressed jointly in prior literature. Findings indicate that AI-AKMS adoption improves knowledge retrieval accuracy, accelerates policy compliance checking, and strengthens organizational resilience, provided that governance, data quality, and human oversight mechanisms are institutionalized. The study concludes with practical implications for public-sector digital transformation strategy and identifies avenues for future empirical validation.
A Three-Layer Cyber AI Governance Framework for Accountable Reinforcement Learning in National Data Sovereignty Andi Agus Salim; Hasbu Naim Syaddad; Luki Ishwara; Agus Nursikuwagus; Handoko Handoko; Rio Yunanto
Journal of Renewable Engineering Vol. 3 No. 4 (2026): JORE - August
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/8h6yam07

Abstract

The rapid deployment of reinforcement learning (RL) agents in critical national infrastructure has outpaced the governance instruments designed to hold them accountable, creating a widening gap between algorithmic autonomy and sovereign oversight. This article proposes a Three-Layer Cyber-AI Governance Framework that integrates the technical, organizational, and regulatory dimensions of accountability for RL systems operating within national data sovereignty regimes. Using a systematic literature review guided by PRISMA 2020 procedures, twenty-five peer-reviewed and preprint sources published between 2021 and 2026 were analyzed through thematic synthesis to identify recurring governance constructs across cybersecurity, AI ethics, and data-sovereignty scholarship. The synthesis reveals three interdependent layers: an Algorithmic Layer governing reward design, explainability, and adversarial robustness; an Organizational Layer governing human oversight, audit trails, and incident reporting; and a Sovereign-Regulatory Layer governing data localization, cross-border data flow, and international cooperation. The proposed framework departs from existing layered models by embedding a continuous feedback loop that links real-time algorithmic telemetry to national regulatory review, closing the accountability gap that single-layer or purely technical frameworks leave open. The article concludes that accountable reinforcement learning under conditions of national data sovereignty requires coordinated, multi-layer instruments rather than isolated technical fixes, and it outlines an agenda for empirical validation of the framework across diverse regulatory contexts.   
Co-Authors Adam Mukharil Bachtiar Agus Nursikuwagus Andi Agus Salim Andi Agus Salim Bobi Kurniawan, Bobi Ednawati Rainarli Estiko Rijanto Handoko Handoko Hasbu Naim Syaddad Hasbu Naim Syaddad Irawan Irfan Dwiguna Rio Yunanto Sri Supatmi Syaddad, Hasbu Naim Zainal Arifin Hasibuan