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International Journal Science and Technology (IJST)
Published by Asosiasi Dosen Muda Indonesia
ISSN : 28287223     EISSN : 28287045     DOI : https://doi.org/10.56127/ijst.v1i2
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
International Journal Science and Technology (IJST) is a scientific journal that presents original articles about research knowledge and information or the latest research and development applications in the field of technology. The scope of the IJST Journal covers the fields of Informatics, Mechanical Engineering, Electrical Engineering, Information Systems and Industrial Engineering. This journal is a means of publication and a place to share research and development work in the field of technology.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 147 Documents
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Generative AI Risk Governance in Organizational Information Systems: A Conceptual Model Integrating Security, Ethics, and Digital Trust Nurdiyanto Yusuf
International Journal Science and Technology Vol. 5 No. 2 (2026): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v5i2.2876

Abstract

The rapid adoption of Generative Artificial Intelligence in organizational information systems has created new opportunities for improving productivity, decision-making, service innovation, and knowledge management. However, its implementation also introduces critical risks related to data privacy, information security, inaccurate outputs, algorithmic bias, ethical misuse, and declining user trust. Objective: This study aims to develop a conceptual model of Generative AI risk governance by integrating AI governance readiness, information security control, ethical AI awareness, user digital trust, and AI adoption effectiveness. The model is proposed to explain how organizations can adopt Generative AI in a secure, ethical, responsible, and trusted manner. Methodology: This study employed a conceptual research design using an integrative literature review approach. Data were collected from secondary academic sources, including peer-reviewed journal articles, reputable conference proceedings, and official technical reports relevant to Generative AI, information systems, cybersecurity, AI ethics, responsible AI governance, and digital trust. The data were analyzed through thematic synthesis to identify conceptual domains, relationships among constructs, and research propositions. Findings: The findings indicate that AI governance readiness serves as a foundational construct that strengthens information security control and ethical AI awareness. These two mechanisms contribute to user digital trust, which subsequently supports the effectiveness of Generative AI adoption in organizational information systems. Implications: This study implies that organizations should not adopt Generative AI solely based on technological benefits. Organizations need to establish governance policies, security controls, ethical guidelines, user education, and trust-building strategies to ensure that Generative AI implementation is safe, accountable, and aligned with organizational objectives. Originality: The originality of this study lies in its integrated conceptual framework, which connects technology adoption, information security, AI ethics, responsible AI governance, and digital trust into a single model for responsible Generative AI implementation in organizational information systems.
Design of a SCADA-Based Control System for Hybrid Power Generation Integrating a Screw Turbine and Solar Panels Jonah Alfred Mekel; Franklin Bawano; Alfred Noufie Mekel; Tineke Saroinsong
International Journal Science and Technology Vol. 5 No. 2 (2026): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v5i2.2894

Abstract

The increasing demand for sustainable electricity requires hybrid renewable energy systems capable of improving power supply reliability and operational supervision under variable energy-source conditions. Purpose: This study aims to design and implement a laboratory-scale hybrid renewable energy control system integrating an Archimedes screw turbine and a photovoltaic system with PLC-based control and SCADA monitoring. Methodology: An experimental design and implementation approach was employed by integrating renewable energy generation, battery storage, electrical measurement devices, an industrial PLC, HMI, and cloud-based SCADA platform. Electrical voltage, current, power, energy, battery condition, and load status were acquired during laboratory testing and evaluated based on the functionality of monitoring, data logging, trend visualization, alarm notification, battery protection, and load control. Findings: The developed system successfully integrated renewable energy generation and industrial automation into a unified supervisory platform. The PLC continuously acquired electrical parameters, executed battery protection and load-switching logic, and communicated with the HMI and SCADA system. Real-time monitoring, historical data recording, graphical trend visualization, alarm notification, and remote load control operated successfully during experimental testing. Implications: The proposed architecture provides a practical platform for renewable energy monitoring, industrial automation education, and further development of automatic energy management and predictive supervision systems. Originality: Unlike previous studies that primarily focus on photovoltaic performance, Archimedes screw turbine optimization, or SCADA architecture separately, this study integrates photovoltaic generation, an Archimedes screw turbine, battery storage, industrial PLC control, HMI visualization, and cloud-based SCADA monitoring within a single laboratory-scale hybrid renewable energy platform.
Testing Various Liquid Organic Fertilizers and Varieties for Yardlong Bean (Vigna unguiculata L.) on Limited Land in Pengempon Village, Sruweng, Kebumen Fauziyatul Fitri; Rennanti Lunnadiyah Aprilia
International Journal Science and Technology Vol. 5 No. 2 (2026): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v5i2.2930

Abstract

Limited agricultural land and the increasing volume of biodegradable household waste require cultivation technologies that are efficient, sustainable, and applicable to small-scale vegetable production. Liquid organic fertilizer (LOF) produced from fruit and vegetable waste has the potential to recycle organic materials while supplying nutrients for crop growth. Objective: This study aimed to evaluate the growth and yield responses of three yardlong bean varieties to different sources of waste-derived LOF under limited-land polybag cultivation. Methods: A quantitative experimental approach was employed using a 4 × 3 factorial Randomized Complete Block Design with three replications. The first factor consisted of four LOF treatments: no LOF, fruit-waste LOF, vegetable-waste LOF, and mixed fruit–vegetable waste LOF. The second factor comprised the Parade, Fantastik, and Komet varieties. Plant height, leaf number, pod number, and pod weight were measured directly. The data were analyzed using factorial analysis of variance, followed by Duncan’s Multiple Range Test at the 5% significance level when significant differences were detected. Results: LOF application significantly supported early vegetative growth, particularly by increasing the number of leaves at two weeks after planting. Fruit-waste LOF produced the numerically highest leaf number, although its effect was statistically comparable to vegetable-waste and mixed fruit–vegetable waste LOFs. No marked differences were detected among the three varieties, while plant height and yield components showed relatively uniform responses during the first three harvests. Implications: Household fruit and vegetable waste can potentially be processed into an alternative fertilizer input to support the early establishment of yardlong bean in limited-land cultivation. Optimization of LOF concentration, application frequency, and nutrient composition is required to improve its contribution to pod production. Originality: The originality of this study lies in the simultaneous comparison of three waste-derived LOF sources and three yardlong bean varieties within a factorial polybag experiment under limited-land conditions.
Integrated Technical, Thermal, and Techno-Economic Assessment of Hot-Oil and Electric Heating Systems for 15–22 MT Asphalt Storage Tanks Arifuddin Arifuddin; Elbi Wiseno
International Journal Science and Technology Vol. 5 No. 2 (2026): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v5i2.2940

Abstract

The selection of an appropriate heating system for asphalt storage tanks plays a critical role in determining operational efficiency, maintenance requirements, energy consumption, and long-term investment performance in asphalt production facilities. Conventional hot-oil heating systems have been widely implemented because of their ability to provide uniform heat distribution; however, they require complex auxiliary equipment, continuous fuel consumption, and intensive maintenance. Consequently, evaluating alternative heating technologies has become increasingly important for improving industrial efficiency and reducing operating costs. Objective: This study aims to compare the technical design, thermal performance, and economic performance of hot-oil and electric heating systems for asphalt storage tanks with capacities ranging from 15 to 22 MT in order to identify the most suitable engineering solution for medium-capacity applications. Methodology: This research employed a quantitative comparative engineering approach using validated secondary engineering data, industrial equipment specifications, and operating-cost assumptions. The analysis included comparisons of technical design characteristics, initial investment, annual operating costs, payback period, ten-year life-cycle cost, and thermal-performance indicators under identical operating conditions. Findings: The results indicate that the electric-heating system provides a simpler engineering configuration, shorter installation time, lower maintenance requirements, and higher temperature-control accuracy than the hot-oil system. Economically, the electric-heating system reduces annual operating costs by approximately IDR 661.34 million, achieves a simple payback period of approximately 0.38 years (4.5 months), and lowers the ten-year life-cycle cost by approximately IDR 6.86 billion. Furthermore, the electric-heating system maintains nearly 100% point-of-use thermal efficiency, whereas the efficiency of the hot-oil system decreases over time because of combustion losses and equipment ageing. Implications: The findings provide practical engineering guidance for selecting heating systems in asphalt storage facilities by demonstrating that electric heating can improve operational efficiency while reducing long-term investment and maintenance costs. The results may also support engineering decision-making related to industrial process electrification. Originality: The originality of this study lies in the integration of technical design comparison, thermal-performance evaluation, and comprehensive techno-economic analysis into a unified engineering assessment framework for medium-capacity asphalt storage tanks. This integrated approach provides a practical reference for engineers and decision-makers considering the modernization of industrial asphalt-heating systems.  
Perception and Behavior of Farmers Towards the Use of Organic Fertilizers in Rice Plants in Kewayuhan Village, Pejagoan District, Kebumen Regency Ngafiful Furqon; Rennanti Lunnadiyah Aprilia
International Journal Science and Technology Vol. 5 No. 2 (2026): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v5i2.2955

Abstract

The application of organic fertilizer is an important strategy for supporting sustainable rice farming by improving soil fertility, maintaining soil quality, and reducing dependence on synthetic fertilizers. However, the adoption of organic fertilizer among rice farmers remains relatively limited, indicating the need to understand the behavioral factors influencing its utilization. Objective: This study aimed to analyze farmers’ perceptions and behavior regarding organic fertilizer use and to examine the relationship between farmers’ perceptions and organic fertilizer utilization behavior in rice cultivation in Kewayuhan Village, Pejagoan District, Kebumen Regency. Methodology: This study employed a quantitative descriptive design with a survey approach. Data were collected from 79 rice farmers selected through simple random sampling from a population of 380 active farmers. A structured questionnaire was used as the research instrument. The data were analyzed using descriptive statistics, validity and reliability testing, and Spearman Rank correlation analysis. Findings: The results showed that farmers generally had positive perceptions of organic fertilizer, with 51.9% of respondents classified as having a high level of perception. Farmers’ behavior toward organic fertilizer utilization was also relatively favorable, although inconsistencies remained in application dosage, frequency, and continuity. The correlation analysis indicated positive associations between farmers’ perceptions and organic fertilizer utilization behavior, suggesting that more favorable perceptions tend to encourage better implementation of organic fertilizer practices. Implications: The findings indicate that improving farmers’ perceptions and knowledge should be accompanied by continuous agricultural extension, technical assistance, improved fertilizer availability, and stronger institutional support. These efforts are expected to increase the consistency of organic fertilizer application and support sustainable fertilizer management in rice farming. Originality: The originality of this study lies in integrating the cognitive, affective, and conative dimensions of farmers’ perceptions and examining their relationship with organic fertilizer utilization behavior using a non-parametric correlation approach. This study also provides specific empirical evidence from rice farmers in Kewayuhan Village, an area that has received limited attention in previous organic fertilizer adoption studies.
A Systematic Analysis of AI-Assisted Vibe Coding in Software Development: Opportunities, Challenges, and Risks amrin jauhari; Fahmi Fathullah; Indra Adi Permana; Robby Nugraha
International Journal Science and Technology Vol. 5 No. 2 (2026): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v5i2.3000

Abstract

The literature on vibe coding has grown rapidly; however, it remains fragmented and is largely dominated by industry reports, leaving its position relative to traditional manual programming and low-code development insufficiently examined. This gap makes it difficult for both researchers and practitioners to determine when vibe coding is appropriate and what risks should be anticipated. Purpose: This study aims to systematically map the current landscape of vibe coding, develop a comparative framework against manual and low-code software development approaches, and propose practical risk mitigation recommendations for software development practitioners. Methodology: A Systematic Literature Review (SLR) was conducted following the PRISMA protocol. Relevant publications from 2023 to 2026 were retrieved from IEEE Xplore, ACM Digital Library, Springer, ScienceDirect, and arXiv, resulting in 61 studies that were analyzed using thematic analysis. Findings: The results indicate that vibe coding can accelerate software prototyping by approximately 40–60% compared with manual development. However, it introduces a verification bottleneck by shifting developers' workload from code implementation to quality assurance and validation. Compared with low-code development, vibe coding provides greater flexibility in expressing user intent but exhibits lower output predictability. In comparison with manual development, it offers significant gains in development speed while sacrificing architectural control and code security, thereby increasing the risks of technical skill degradation, hidden security vulnerabilities, and accumulated technical debt. Implications: The findings provide practical guidance for software development teams in identifying project phases that are suitable for extensive adoption of vibe coding and those that still require manual architectural review. The study also emphasizes the importance of integrating security auditing and technical debt monitoring into AI-assisted software development workflows. Originality/Value: The novelty of this study lies in its explicit comparative framework, which systematically positions vibe coding alongside manual and low-code development across six technical dimensions, extending previous studies that have generally examined vibe coding in isolation.
Optimization of Support Vector Machine for Imbalanced Credit Risk Classification Andre Pratama Adiwijaya
International Journal Science and Technology Vol. 5 No. 2 (2026): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v5i2.3038

Abstract

Credit risk classification is an important component of financial decision-making because inaccurate classification may increase payment-default exposure and reduce lending quality. Credit datasets commonly contain numerical variables with different measurement scales and imbalanced distributions between default and non-default clients, which can affect the reliability of machine-learning models. Objective: This study aims to develop and evaluate a Support Vector Machine model for classifying credit card clients into default and non-default categories. The study also examines the influence of numerical standardization, kernel selection, hyperparameter optimization, and balanced class weighting on classification performance. Methodology: A quantitative experimental approach was applied using the Default of Credit Card Clients dataset from the UCI Machine Learning Repository. The dataset consisted of 30,000 observations and 23 predictor variables. Data were divided into training and testing subsets using a stratified 80:20 ratio. Categorical variables were encoded, numerical variables were standardized, and several SVM kernels were evaluated. Hyperparameter selection was conducted using five-fold cross-validation. Model performance was assessed using accuracy, precision, recall, F1-score, specificity, balanced accuracy, and ROC–AUC. Findings: The SVM model trained without standardization failed to identify default clients effectively. Numerical standardization substantially improved classification performance, while the radial basis function kernel produced the strongest validation results. The selected balanced RBF-SVM achieved 77.13% accuracy, 48.54% precision, 56.22% recall, 52.09% F1-score, 83.07% specificity, 69.65% balanced accuracy, and 75.10% ROC–AUC. Balanced class weighting improved default detection but increased false-positive predictions. Implications: The model can support financial institutions as an initial credit-risk screening tool. Its predictions should be combined with document verification, repayment-capacity analysis, and manual assessment rather than being used as the sole basis for credit approval. Originality: This study provides a controlled evaluation of SVM performance by integrating feature standardization, kernel selection, hyperparameter optimization, class-imbalance treatment, and class-sensitive performance metrics. The study demonstrates that credit-risk models should be selected based on balanced default detection rather than overall accuracy alone.

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