Jurnal Testing dan Implementasi Sistem Informasi

DESIGN AND IMPLEMENTATION OF A DECENTRALIZED E-VOTING SYSTEM ON LOCAL SERVER INFRASTRUCTURE

Tengku Mohd Diansyah, Nuraminah Ramli, Muzammil Jusoh — Mon, 01 Dec 2025 00:00:00 +0700

This study addresses the limitations of existing decentralized e-voting systems, particularly their reliance on public distributed infrastructures, limited real-world deployment feasibility, and lack of comprehensive evaluation. Previous studies have demonstrated the potential of distributed ledger-based voting mechanisms; however, most focus on conceptual designs or small-scale prototypes without detailed performance and usability validation. To address this gap, this research proposes and implements a decentralized e-voting system deployed on a local server infrastructure using distributed ledger technology and automated validation mechanisms for vote integrity. The system is designed to reduce dependency on external networks while maintaining transparency, immutability, and operational efficiency. The system was evaluated through functional testing, performance analysis, and user acceptance testing involving 30 participants in a controlled environment with 20 simulated voters. The results show that the system achieved a functional accuracy of 96% across 25 test scenarios. The average transaction response time ranged between 0.6 and 1.6 seconds, indicating efficient processing under moderate load conditions. However, the evaluation is limited to small-scale simulations and does not include stress testing, large-scale scalability analysis, or advanced security validation. Therefore, the findings demonstrate system feasibility rather than fully validated effectiveness. These results suggest that decentralized e-voting systems deployed on local infrastructures can provide a practical and efficient solution for controlled election environments, while further research is required to evaluate scalability, security robustness, and real-world deployment readiness.

A QUALITATIVE SYNTHESIS OF ARTIFICIAL INTELLIGENCE ADOPTION IN MSMES: INSIGHTS INTO OPPORTUNITIES AND CHALLENGES

Astri Ayu Purwati, Siti Intan Nurdiana Wong Abdullah, Mazzlida Mat Deli — Mon, 01 Dec 2025 00:00:00 +0700

Artificial intelligence (AI) has emerged as a transformative technology that is reshaping business processes, innovation, and competitiveness, particularly for Micro, Small, and Medium Enterprises (MSMEs). Despite its potential, AI adoption among MSMEs remains uneven and is often constrained by various technological, organizational, and environmental factors. This study aims to provide a comprehensive understanding of AI adoption in MSMEs by conducting a qualitative synthesis of existing literature, focusing on the identification of key drivers, barriers, opportunities, and organizational impacts. This research adopts a systematic literature review (SLR) approach, guided by the PRISMA framework, to analyze 25 peer-reviewed articles indexed in Scopus. A thematic analysis was employed to systematically code and categorize the findings, enabling the identification of recurring patterns and critical themes related to AI adoption in MSMEs. The results reveal four major themes: (1) drivers of adoption, including technological readiness, organizational capabilities, competitive pressure, and perceived benefits; (2) barriers to adoption, such as financial constraints, lack of expertise, technological complexity, and organizational resistance; (3) opportunities enabled by AI, including enhanced operational efficiency, improved customer insights, innovation, and market expansion; and (4) organizational impacts, particularly in terms of performance improvement, digital transformation, innovation capability, and strategic alignment. The study contributes to the literature by providing an integrative and qualitative perspective on AI adoption in MSMEs, extending the Technology-Organization-Environment (TOE) framework with AI-specific insights. Practically, the findings offer guidance for MSME managers and policymakers in designing strategies to overcome adoption barriers and leverage AI for sustainable growth. Future research is encouraged to explore empirical and longitudinal approaches to further examine the dynamic nature of AI adoption in MSMEs.

HAND POSE CLASSIFICATION USING MEDIAPIPE HANDS AND CNN-LSTM FOR AUGMENTED REALITY BASED INTRAVENOUS INFUSION LEARNING

Tue, 02 Dec 2025 00:00:00 +0700

Intravenous infusion training requires precise hand positioning and coordinated movements; however, conventional training approaches remain subjective and lack consistent real-time feedback. Moreover, existing augmented reality (AR)-based systems are largely limited to visualization and do not provide intelligent, automated skill evaluation. To address this gap, this study proposes an integrated hand pose classification framework that combines MediaPipe-based landmark extraction, CNN-LSTM spatio-temporal modeling, and AR-based feedback for real-time procedural learning. The novelty of this work lies in the seamless integration of lightweight feature representation, hybrid deep learning, and interactive AR feedback within a unified learning system. Experimental results demonstrate that the proposed approach achieves high classification performance, with an accuracy of 94.82% and an AUC of approximately 0.97, indicating strong discriminative capability. The system also operates in real time with low latency, enabling immediate feedback and adaptive learning. This study contributes theoretically to spatio-temporal gesture modeling and practically to the development of intelligent AR-based training systems. The proposed framework offers a scalable and objective solution for improving procedural accuracy, consistency, and accessibility in medical education.

THE INFLUENCE OF MARKETING MIX ON CONSUMER PURCHASE PATTERNS USING THE APRIORI DATA MINING ALGORITHM

Fri, 05 Dec 2025 00:00:00 +0700

The increasing volume of sales transaction data in retail and marketplace environments presents an opportunity to extract valuable insights for decision-making; however, such data are often underutilized. This study aims to analyze consumer purchasing patterns using the Apriori algorithm and to examine the influence of the marketing mix (product, price, place, and promotion) on purchasing decisions that shape these patterns. This research employs a quantitative approach by integrating data mining and statistical analysis. Transaction data are processed using the Apriori algorithm through RapidMiner to generate association rules and identify frequent itemsets. In addition, questionnaire data are analyzed using multiple linear regression to evaluate the effect of marketing mix variables on purchasing decisions. The results show that product, price, place, and promotion simultaneously have a significant effect on purchasing decisions. Partially, product (t = 2.622; p = 0.011), price (t = 4.738; p = 0.000), and place/distribution (t = 2.239; p = 0.029) have a significant positive effect, while promotion does not have a significant effect (t = 1.486; p = 0.143). The Apriori analysis reveals dominant purchasing patterns that can be translated into practical marketing strategies, such as product bundling and layout optimization. This study contributes by integrating association rule mining with marketing mix analysis to provide both predictive patterns and explanatory insights. However, the findings should be interpreted with caution due to data limitations, including a relatively small sample size (n = 148) and a short observation period of three months during peak season, which may limit generalizability. Despite these constraints, the results offer practical implications for optimizing marketing strategies and contribute theoretically to interdisciplinary research in data mining and consumer behavior.

EMPLOYEE PAYROLL SYSTEM DESIGN USING EXTREME PROGRAMMING ALGORITHM

Wed, 10 Dec 2025 00:00:00 +0700

The employee payroll system was built to make it easier for companies, especially those that still handle payroll manually, to manage employee payroll data. Company X still records employee information, absence information, and salary information manually in a book as part of its employee payroll process. The design of the employee payroll system in this study uses the Extreme Programming method. The Extreme Programming approach is used in the design of the employee payroll system in this study. This study also tests the system. The system is tested using White-box and Black-box testing methodologies, with the aim of testing the functional and non-functional requirements of the system. The findings of this study lead to the creation of an employee payroll information system that Company X can use to facilitate the payroll process for its employees.