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Technologia Journal
Published by PT Anagata Sembagi Education
ISSN : -     EISSN : 30469163     DOI : https://doi.org/10.62872/ezf7zc71
Core Subject : Science,
Computer Science & IT
This journal publishes original articles on current issues and international trends in the field of information engineering and information systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 38 Documents
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Preliminary Analysis of e-Government Implementation and Readiness in Sikka Regency: A Foundational Study for the AVELINE Evaluation Model Agustinus Lambertus Suban
Technologia Journal Vol. 2 No. 4 (2025): Technologia Journal-November
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/j412jw89

Abstract

This study presents a preliminary analysis of e-government implementation and readiness in Sikka Regency, East Nusa Tenggara, as the empirical foundation for developing the AVELINE Evaluation Model a context-sensitive framework designed to assess e-government maturity in developing local governments. Using a mixed-methods approach, the research integrates quantitative survey data from 120 respondents across 15 government agencies with qualitative insights from in-depth interviews and document analysis. The findings indicate a moderate overall readiness index (3.04 out of 5), reflecting partial progress in digital transformation. Among the five assessed dimensions, policy and governance readiness scored highest (3.47), while citizen engagement and human resource readiness remained lowest (2.76 and 2.85, respectively). The study identifies key inhibitors such as limited ICT infrastructure, insufficient digital literacy, and fragmented inter-agency coordination, which hinder effective SPBE (Electronic-Based Government System) implementation. Conversely, strong policy commitment and emerging leadership support provide a foundation for improvement. Empirical results confirm significant correlations between infrastructure, human resources, and organizational readiness, emphasizing that technological success depends on institutional and socio-environmental factors. Theoretically, this research contributes to the development of the AVELINE Evaluation Model, integrating six dimensions Administrative, Viability, Environmental, Legal, Information, and Network readiness into a holistic tool for assessing e-government maturity. Practically, recommendations for infrastructure enhancement, human resource capacity building, and participatory governance. Overall, the findings highlight that digital transformation in Sikka Regency remains in a transitional phase technologically functional but organizationally fragile underscoring the need for a tailored, context-aware evaluation framework to guide sustainable e-government development in underdeveloped regions
Information Security in The Cloud Era: Strategies and Implementation Nyoman Gunantara
Technologia Journal Vol. 2 No. 4 (2025): Technologia Journal-November
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/40mq7e60

Abstract

Cloud computing has become a fundamental driver of global digital transformation, providing organizations with scalability, operational efficiency, and flexibility. However, the migration to cloud environments has also intensified cybersecurity challenges, including data breaches, misconfigurations, identity compromise, and evolving cyber-threats. This systematic literature review analyzes strategic and implementation approaches to cloud information security across multiple sectors. The study applies a structured methodology aligned with academic SLR standards to identify key security practices, technological controls, and governance frameworks. Findings reveal that effective cloud security requires a holistic model integrating Zero Trust Architecture, encryption, identity and access management, artificial intelligence-driven threat monitoring, and compliance with regulatory frameworks. Organizational readiness, human capability, and governance maturity significantly influence implementation outcomes. The study concludes that adaptive, multi-layer security models combined with continuous workforce development and regulatory harmonization are critical for building sustainable cloud resilience
Application of Large Language Models (LLMs) for Optimising Indonesian Language-Based Public Service Chatbots Ali Ibrahim; Sitti Rachmaeati Yahya; Iwan Adhicandra
Technologia Journal Vol. 2 No. 4 (2025): Technologia Journal-November
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/74q5b220

Abstract

This study examines the potential of Large Language Models to optimise Indonesian language-based public service chatbots by integrating linguistic, technological, and administrative perspectives. Using a mixed-method approach that combines a systematic literature review with secondary benchmarking of state-of-the-art LLMs, the research evaluates model performance in Indonesian semantic comprehension, contextual reasoning, and domain adaptability. The findings show that LLMs can significantly improve chatbot accuracy, inclusivity, and responsiveness, outperforming rule-based systems that struggle with informal expressions, multi-intent queries, and policy-specific terminology. Benchmarking highlights that GPT-4 and PaLM-2 achieve high contextual coherence and low hallucination rates, while Indonesian-centric models such as IndoGPT demonstrate strong local language adaptability. However, risks related to data privacy, bias, hallucination, and governance limitations present substantial challenges for implementation. The study proposes a strategic framework that emphasizes AI governance, interoperable data infrastructure, institutional capacity building, hybrid retrieval–generation design, and citizen engagement to ensure responsible adoption. Overall, the integration of LLM-powered chatbots has the potential to transform Indonesia’s digital public service landscape, provided that deployment is accompanied by robust oversight, ethical safeguards, and sustainable technological planning
Problem-Based Learning Models in Computer Science Education Supported by Digital Platforms: A Systematic Literature Review Purwanto, Agus; Santoso, Joko; Karang Utama, I Wayan; Nugroho, Anggun; Irfan Fauziawan, Affan
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/a8nnzd66

Abstract

Computer science education requires strong problem-solving skills, algorithmic thinking, and collaboration. Problem-Based Learning (PBL) is widely regarded as a relevant pedagogical approach to address these demands, particularly when supported by digital platforms that enable collaborative, exploratory, and contextual learning. This study aims to synthesize PBL models in computer science education supported by digital platforms, identify the roles of digital platforms in facilitating PBL, and evaluate its impacts and implementation challenges. A Systematic Literature Review with a qualitative synthesis approach was conducted on peer-reviewed journal articles and conference proceedings published between 2014 and 2024. A total of 36 studies were included and analyzed through thematic analysis and narrative synthesis. The findings indicate that digital PBL is implemented through several dominant patterns, including full PBL, hybrid PBL, and project-based PBL, with a strong emphasis on authentic problems reflecting real-world computing practices. Digital platforms function as pedagogical mediators that support collaboration, solution exploration, and reflective learning. Although digital PBL demonstrates positive effects on problem-solving skills, motivation, and collaboration, its effectiveness is highly dependent on pedagogical alignment, instructor readiness, and students’ digital literacy.
A Systematic Literature Review on Intelligent Tutoring Systems for Outcome-Based Education in Higher Education Syaddad, Hasbu Naim; Salim , Andi Agus; Ishwara, Luki; Hasibuan , Zainal Arifin; Kurniawan , Bobi; Supatmi, Sri
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 Ishwara, Luki; Syaddad, Hasbu Naim; Salim, Andi Agus; Kurniawan, Bobi; Bachtiar, Adam Mukharil; Rainarli, Ednawati
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.   
Edge–Cloud Synergy in Real-Time System Optimization Fachrurozi, Moch.
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: 10.62872/38d3g980

Abstract

The convergence of edge and cloud computing paradigms has emerged as a critical architectural approach for real-time system optimization. This research synthesizes recent developments in edge–cloud synergy, examining how the combination of edge computing's ultra-low latency capabilities with cloud computing's massive computational resources addresses the growing demands of real-time applications in industrial systems, smart cities, and Internet of Things (IoT) environments. Through comprehensive analysis of contemporary research from 2021 to 2025, this study identifies four primary research trajectories: architecture and orchestration patterns, AI optimization and predictive maintenance, resource scheduling mechanisms, and vertical domain applications. Quantitative evidence demonstrates that hybrid edge–cloud architectures achieve 10–15× latency reduction compared to cloud-only approaches, bandwidth savings exceeding 90%, energy efficiency improvements of 22–42%, and detection accuracy rates approaching 90% in anomaly detection scenarios. However, significant challenges persist in resource management, security frameworks, and standardization efforts. This comprehensive review provides insights into the current state of edge–cloud synergy and identifies critical research directions for advancing real-time system optimization in next-generation networks.
Design and Development of a Data Mining-Based Recommendation System for E-Learning Palumpun, Yulius
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/k604v647

Abstract

The rapid growth of e-learning platforms has intensified the need for effective personalization mechanisms to address content overload and diverse learner characteristics. Recommendation systems based on data mining have emerged as essential components for guiding learners toward relevant courses and adaptive learning paths. This study aims to design and develop an integrated data mining-based recommendation system for e-learning that enhances personalization and learning effectiveness within a unified platform architecture. This research adopts a research and development approach combined with system engineering methodology. Learner interaction data, course metadata, and performance records were collected from the e-learning platform and processed through data preprocessing techniques, including cleaning, feature extraction, and clustering. The recommendation engine integrates collaborative filtering, content-based filtering, and reinforcement learning for adaptive learning path optimization. System performance was evaluated using accuracy, precision, recall, F1-score, MAE, and NDCG metrics. The results show significant improvements compared to the baseline model, including higher recommendation accuracy and a substantial increase in learner completion rates. The discussion confirms that hybrid modeling and integrated system architecture enhance both algorithmic performance and pedagogical outcomes. In conclusion, the proposed system provides a scalable and effective framework for personalized e-learning through integrated data mining techniques.

Page 4 of 4 | Total Record : 38

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