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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 45 Documents
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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.
An Integrated PIR-Camera IoT Security System with Real-Time Telegram Notification Imam Tri Suryadin; Aang Anwarudin; Lazuardi Fatahilah Hamdi; Riski Yudhi Prasongko
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: 10.62872/5caybe23

Abstract

The rapid growth of Internet of Things (IoT) technology has motivated numerous studies to develop motion-based security systems that combine Passive Infrared (PIR) sensors with microcontrollers and instant-messaging platforms such as Telegram. A review of four recent studies on PIR-based IoT motion detection, however, shows that each system still carries partial limitations: some provide visual verification through a camera module but lack an offline data-retention mechanism, while others record activity to a cloud platform but do not capture images, leaving alerts without visual evidence. This study aims to synthesize the strengths and shortcomings of the four reviewed systems through a comparative literature analysis and to propose a refined system architecture that consolidates their complementary features while resolving their respective gaps. The research applies a descriptive-qualitative literature review combined with a design-science approach to formulate an enhanced architecture consisting of a PIR sensor, an ESP32-CAM module, a Telegram Bot API for dual text-and-image alerts, cloud-based data logging on ThingSpeak, and a local offline-backup mechanism using SPIFFS, supplemented with an adaptive debounce filter to reduce false triggers. The outcome of this study is a conceptual architecture, block diagram, and operational flowchart that integrate visual verification, persistent logging, and connectivity resilience within a single design, providing a more complete reference for future implementation of IoT-based motion detection security systems.
Utilizing Generative AI in Computer Security: An Analysis of Opportunities and Risks Didi Rahmat Saputra
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/z4mjxf09

Abstract

The development of Generative Artificial Intelligence (Generative AI), particularly Large Language Models (LLM), has opened new opportunities and simultaneously created risks in computer security. This study aims to analyze the utilization, opportunities, risks, and mitigation strategies of Generative AI in the context of computer security. The study used a qualitative approach with a literature review method of journal articles, proceedings, books, and relevant scientific reports from the period 2021–2026. The literature was searched through Google Scholar, IEEE Xplore, ScienceDirect, SpringerLink, and the ACM Digital Library, then analyzed using content analysis. The results of the study indicate that Generative AI has the potential to support threat detection, cyberthreat intelligence, malware analysis, penetration testing, code security, phishing analysis, and incident response. However, this technology also has dual-use characteristics because it can be used for phishing, social engineering, malicious code modification, and presents risks of prompt injection, data poisoning, information leakage, hallucinations, and over-reliance on AI. Mitigation requires human validation, the principle of least privilege, access control, data protection, and structured AI governance. Thus, Generative AI should be positioned as a decision-support technology that enhances the capabilities of security professionals, rather than replacing human oversight entirely.
Evaluation of Deep Learning for Source Code Vulnerability Detection Based on Vulnerability Detection Score Muhammad Amin; Muhammad Rais Wathani; Annisa Fathia Aziza
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/xcnyy434

Abstract

Source code vulnerabilities pose a serious threat to software development, with reported Common Vulnerabilities and Exposures exceeding 25,000 entries per year, making manual inspection unscalable. This study aims to evaluate and compare four deep learning architectures for automated source code vulnerability detection: Convolutional Neural Network, Bidirectional Long Short-Term Memory, CodeBERT, and GraphCodeBERT, and to measure CodeBERT's ability to classify eight types of Common Weakness Enumeration. The research method uses a quantitative experimental approach with 43,164 C and C++ functions derived from the combined BigVul and CVEfixes datasets. The data were processed through comment cleaning, MD5 hash-based deduplication to prevent data leakage, and class balancing with a one-to-three ratio. The datasets were then divided into training, validation, and test datasets. In addition to conventional metrics such as accuracy, precision, recall, F1-score, and AUC-ROC, this study applies a Vulnerability Detection Score, which measures the proportion of missed vulnerabilities at a false positive rate below zero point five percent. The results showed that CodeBERT achieved the best performance with an F1-score of 0.702, an AUC-ROC of 0.896, and a Vulnerability Detection Score of 0.857, outperforming the baseline model, which only achieved an F1-score of around 0.64. However, all models recorded high Vulnerability Detection Scores in the range of 0.857 to 0.906, meaning that approximately 86 percent of vulnerabilities were still missed under realistic operating conditions. The eight-class Common Weakness Enumeration classification only achieved a macro-F1 of 0.28. This study concluded that conventional metrics tend to overestimate the model's readiness for real-world deployment.
Sterilization and Microbial Inactivation Technologies for Liquid Foods: A Review of Food Safety, Shelf Life, and Food-System Resilience in Indonesia Loso Judijanto
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: 10.62872/7q620w30

Abstract

Liquid foods are highly susceptible to microbial contamination and quality deterioration, while distribution across geographically dispersed markets requires adequate shelf life, safety, and product stability. This article critically examines the development of sterilization and microbial inactivation technologies for liquid foods and evaluates their contribution to food safety and food-system resilience, with particular attention to Indonesia. Using a qualitative literature review, the study synthesizes peer-reviewed research published from 2020 to 2026 on thermal processes, particularly ultra-high-temperature (UHT)/aseptic processing and ohmic heating, and on non-thermal technologies including high-pressure processing (HPP), pulsed electric fields (PEF), ultraviolet-C (UV-C), and cold plasma. The synthesis shows that no single technology is optimal for every food matrix or safety target. UHT/aseptic processing remains the most established option when commercial sterility and ambient shelf stability are required, whereas HPP and PEF can provide superior retention of fresh-like quality but generally require additional hurdles when bacterial spores are relevant. UV-C performance is strongly constrained by optical properties, while cold plasma still requires broader product-specific validation, chemical-safety assessment, and scale-up evidence. For Indonesia, a portfolio approach is the most defensible strategy: optimize mature thermal and volumetric-heating platforms for mass-market products while developing targeted HPP, PEF, UV-C, and hybrid applications according to product properties, economics, regulatory requirements, and industrial capability

Page 5 of 5 | Total Record : 45

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