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Tech-E
Published by Universitas Buddhi Dharma
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Tech-E dikembangkan dengan tujuan menampung karya ilmiah Dosen dan Mahasiswa, baik hasil tulisan ilmiah maupun penelitian yang berupa hasil studi kepustakaan.
Arjuna Subject : -
Articles 125 Documents
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FireReady Challenge: A 2D Gamified Prototype for Community Fire Safety Preparedness Rabeah Md Zin; Ahmad Amru Mohamad Zaid; Zailah Salamon; Adi Irfan Che Ani; Nur ‘Amirah Mhd Noh
Tech-E Vol. 9 No. 2 (2026): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v9i2.4304

Abstract

Fire protection remains a critical public concern, as fire-related incidents continue to cause substantial property damage, personal injuries, and loss of life. Although public awareness campaigns and routine safety drills are widely implemented, conventional didactic training methods such as posters, presentations, and demonstrations often fail to generate lasting comprehension or meaningful behavioural change. To address this gap, this paper presents the FireReady Challenge, a 2D gamified learning prototype designed to enhance community awareness and preparedness in fire safety. The game adopts the ADDIE instructional model to systematically translate key learning objectives into interactive digital experiences focused on hazard identification, safe evacuation procedures, emergency communication readiness, and proper fire extinguisher use. Gamification elements, including points, badges, real-time feedback, and time-bound missions, are embedded to strengthen motivation and user engagement. This concept design demonstrates an innovative approach to ICT-based fire safety education for community dissemination. Future research will involve pilot-scale deployment and empirical validation to evaluate learning outcomes, usability, and user acceptance.
Machine Learning Approaches to Workplace Mental Health: Predicting Treatment-Seeking Behavior Using the OSMI Dataset Nor Aishah Othman; Mariana Rosdi
Tech-E Vol. 9 No. 2 (2026): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v9i2.4305

Abstract

Employee mental health is increasingly recognized as essential for sustainable organizational performance, particularly in technology sectors where work intensity and psychological strain are prevalent. This study leverages machine learning to identify predictors of treatment-seeking behavior using the Open Sourcing Mental Illness (OSMI) dataset, which includes 1,387 anonymized responses from the 2014 OSMI survey. The survey examines employees’ experiences and perceptions of mental health in the global tech industry. Through data cleaning and encoding, key factors influencing help-seeking behavior were identified, including family history of mental illness and work interference due to psychological distress. Two machine learning models, Decision Tree and K-Nearest Neighbour (KNN), were employed for prediction. The Decision Tree model achieved an accuracy of 73%, while KNN attained 100%, suggesting high predictive power, albeit with potential overfitting risks. These findings align with recent studies promoting the integration of AI-driven analytics in workplace wellness programs to detect hidden behavioral trends and enable early interventions. The results demonstrate that machine learning models can offer valuable insights into employee well-being and preventative strategies. Future research should focus on incorporating larger, more diverse datasets and adopting explainable AI (XAI) techniques to enhance interpretability, fairness, and trust in predictive systems for mental health in the workplace.
Desain Microcomputer Cloud Computing for Informatics Study Program at LIA University Based on Local Area Network (LAN) Ariya Pannadhitthana Candra; Murniasih, Intan
Tech-E Vol. 9 No. 2 (2026): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v9i2.4314

Abstract

Digital transformation in higher education increasingly requires information technology infrastructure that is efficient, flexible, secure, and sustainable. Cloud computing has emerged as a strategic solution by enabling scalable resources, centralized management, and service standardization effectively. However, public cloud adoption in academic institutions frequently encounters constraints, including recurring operational costs, data security and sovereignty risks, regulatory concerns, and strong dependence on reliable internet connectivity. These issues are particularly salient for instructional laboratories that demand continuous access, predictable performance, and institutional control. This study aims to design and implement a Local Area Network (LAN)-based cloud computing system using a microcomputer as the primary server within the Informatics Study Program at LIA University. The research employs a research and development (R&D) methodology comprising needs analysis, system architecture design, implementation, and functional as well as performance testing. An open-source virtualization platform is deployed to deliver Infrastructure as a Service (IaaS) and Software as a Service (SaaS) in a private cloud environment. The results demonstrate that the proposed LAN-based local cloud provides centralized computing and storage services with low latency, high stability, and efficient utilization.
Public Sentiment Analysis of Free Nutritious Meal Program Discourse on Social Media X Using Support Vector Machine N-Gram Features Based Silaban, Daniel; Gracia Simatupang; Sardo Pardingotan Sipayung
Tech-E Vol. 9 No. 2 (2026): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v9i2.4355

Abstract

The Free Nutritious Meal Program is a government policy aimed at improving the nutritional quality of society and has generated diverse public responses on social media. This study aims to analyze public sentiment toward the Free Nutritious Meal Program on social media X using the Support Vector Machine (SVM) algorithm with N-Gram features and Term Frequency–Inverse Document Frequency (TF-IDF) weighting. The data were collected through a crawling process from social media X, resulting in 1,014 tweets. After data cleaning, 931 tweets were obtained and labeled into two sentiment classes, namely positive and negative. The research stages include text preprocessing, N-Gram feature extraction (unigram and bigram), classification using the SVM algorithm, and model evaluation using the 10-Fold Cross-Validation method with the assistance of the RapidMiner tool. The experimental results show that the SVM model achieved an accuracy of 79.59%. Although the precision value for the negative class is relatively high, the recall and F1-score remain relatively low due to the imbalance in data distribution. Overall, the results indicate that public sentiment toward the Free Nutritious Meal Program on social media X is dominated by positive sentiment. The findings of this study are expected to serve as an initial evaluation for the government in understanding public perceptions of the implementation of the program.
Architectural Analysis of the Repository Pattern in Web-Based Credit Score Conversion Assessment System Based on PermenPAN-RB No. 1 of 2023 Lapatta, Nouval Trezandy; Syahrullah
Tech-E Vol. 9 No. 2 (2026): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v9i2.4362

Abstract

PermenPAN-RB Regulation No. 1 of 2023 introduced a major shift in functional position assessment by emphasizing performance predicate conversion in credit score evaluation, which increases architectural demands on supporting information systems. In practice, many assessment systems remain tightly coupled and difficult to evolve when regulatory rules, integration sources, or reporting formats change. This paper presents an architecture-oriented analysis of a web-based credit score conversion assessment information system that applies the Repository Pattern as a core architectural mechanism to decouple business logic from persistence, integration, and document-generation concerns. The analysis adopts a scenario-based evaluation approach inspired by the Architecture Tradeoff Analysis Method (ATAM) and is grounded in the ISO/IEC 25010 software quality model, focusing on maintainability, modifiability, testability, scalability, and reliability. Architectural evaluation is conducted by examining layered boundaries, repository abstractions, and dependency injection mechanisms under representative regulatory-driven change scenarios, including rule adjustments, data integration extensions, and reporting modifications. The results demonstrate consistent change localization across architectural layers, where rule changes are confined to service modules, integration changes are absorbed by repository adapters, and reporting changes remain isolated within document-generation components. These findings show that repository-based architectures significantly reduce coupling, improve change isolation, and support the sustainable evolution of government information systems operating under dynamic regulatory environments.

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