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Journal of Applied Engineering and Technological Science (JAETS)
Published by Yayasan Pendidikan Riset dan Pengembangan Intelektual
ISSN : 27156087     EISSN : 27156079     DOI : -
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Journal of Applied Engineering and Technological Science (JAETS) is published by Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI), Pekanbaru, Indonesia. It is academic, online, open access, peer reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. Journal of Applied Engineering and Technological Science (JAETS) is published annually 2 times every June and Desember.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 405 Documents
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DAM Price-Based Model Predictive Control for Smart EV Charging under Grid and User Constraints Sarab AL-Chlaihawi; Faris A. Alhaddad
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9472

Abstract

The rapid deployment of Electric Vehicles (EVs) has significantly increased grid congestion, particularly in regions with limited capacity for infrastructure expansion where system operators no longer permit customers to extend grid connections. Dynamic energy pricing has emerged to incentivize consumers to optimize energy use through time-of-day tariffs. However, existing smart charging approaches typically optimize grid constraints, cost, or user preferences in isolation, with limited integration of these objectives. This paper proposes a cloud-based Model Predictive Control (MPC) framework for smart EV charging that simultaneously enforces grid power limits, minimizes charging cost, and satisfies user-defined requirements. The proposed method incorporates day-ahead market (DAM) electricity prices, real-time building load, photovoltaic (PV) forecasts, and EV user inputs within a multi-objective optimization problem solved using a receding horizon strategy. The approach is validated through both simulation and a real-world deployment in a commercial building with multiple EV chargers. Results show that the proposed strategy achieves charging cost reductions of up to 95% under favorable overnight pricing conditions and up to 87% in real-world operation with grid constraints, while maintaining user satisfaction. The findings demonstrate the practical feasibility and contribution of an integrated, cloud-based MPC approach for scalable, cost-efficient, and grid-compliant EV charging.
Flood Prone Areas Mapping using Remote Sensing and GIS Techniques in Kalal Badrah Basin, Wasit, East of Iraq Athraa Abbas Kadhim; Marwa Razzaq Aya; Zainab N. Jasim
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9476

Abstract

Floods are considered one of the most dangerous natural disasters globally due to the damage they cause, impacting lives and damaging property, infrastructure, and economic activities. Despite the increasing frequency of flash floods in Iraq, particularly in the eastern regions, as a result of climate change, high-resolution spatial assessments remain limited. This study addresses this research gap by integrating the latest Landsat 9 satellite imagery and rainfall data from the Global Precipitation Measurement System (GPM IMERG) within a multi-criteria framework based on geographic information systems (GIS) for the Kalal Badra Basin. The scientific contribution lies in providing a local sensitivity map with a 30-meter resolution that classifies the basin into three risk zones, offering a vital tool for disaster mitigation in data-poor semi-arid environments. The study guides methodology based on DEM SRTM, Landsat image, GPM data. The approach described integrates remote sensing and GIS techniques. Various thematic layers such as slope, elevation, Normalized Difference Vegetation Index (NDVI), drainage density and precipitation were employed to delineate flood prone zones in the Kalal Badrah watershed in Iraq. The individual flood susceptibility maps of each thematic layer were combined with equal weights within the GIS environment to generate the overall flood susceptibility map of the study area. The final map classified the study area to three classes: No flood risk, low flood risk, and high flood risk area according to the selected criteria.
Linking Gamification Technology, Motivation, and Flow to Student Engagement and Problem-Solving in Education Dedy Irfan; Didik Hariyanto; Heri Prabowo; Zulhendra Zulhendra; Edidas Edidas; Andhika Herayono
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/a75mdr10

Abstract

The use of gamification in higher education has been widely explored as a strategy to enhance student engagement and learning outcomes. However, many empirical studies on gamified learning primarily examine direct relationships between gamification and motivational outcomes, while the underlying experiential mechanisms that connect gamification with sustained engagement and higher-order cognitive performance remain insufficiently understood. In particular, few structural modeling studies simultaneously examine the roles of motivation, flow, and presence within a unified experiential framework. This study investigates how gamification technology influences student engagement and problem-solving competence through the experiential mechanisms of motivation, flow, and presence. A quantitative research design was employed involving 100 undergraduate students enrolled in technology-oriented programs (electronics engineering education and informatics engineering education) at Universitas Negeri Padang. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the proposed mediation pathways. The results indicate that gamification technology significantly influences motivation (β = 0.412, p < 0.001), flow (β = 0.508, p < 0.001), and presence (β = 0.436, p < 0.001). Among the experiential constructs, flow shows the strongest influence on student engagement (β = 0.483, p < 0.001), while the effect of motivation on engagement is not statistically significant (β = 0.097, p = 0.18). Student engagement subsequently demonstrates a significant effect on problem-solving competence (β = 0.498, p < 0.001). The structural model explains 30.6% of the variance in student engagement and 24.8% of the variance in problem-solving competence. These findings suggest that immersive experiential states, particularly flow, play an important role in shaping engagement in gamified learning environments. The study contributes to the gamification literature by proposing and empirically testing an experiential pathway model that integrates psychological immersion mechanisms with behavioral learning outcomes in technology-enhanced education.
Machine Learning for Public Sector Performance Prediction: The Role of Communication, Decision-Making, and Strategy Alignment Aris Riyadi; Engkus Kuswarno; Dadang Sugiana; Asep Suryana
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9624

Abstract

This study explores the relationship between communication quality, participatory decision-making, and organizational performance in the public sector using machine learning techniques. Data were collected from government agencies, employing stratified random sampling to survey civil servants at various levels of tenure. A decision-tree classification model was used to identify key predictors of organizational performance, with the model achieving 71% accuracy and a weighted F1-score of 0.71. The results highlight that interpersonal communication quality, employee involvement in decision-making, and strategic alignment were the most significant factors influencing performance. This study demonstrates the value of machine learning in capturing complex, nonlinear relationships in organizational data and provides practical insights for enhancing communication systems and decision-making structures in public institutions. The findings offer a framework for improving public sector performance by promoting participatory governance and aligning strategic priorities.
Islamic Financial Technology Adoption and MSME Performance: Integrating Literacy, Compliance, Accessibility, and Leadership Zulfadli Hamzah; Astri Ayu Purwati; Rosyidi Hamzah
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jryn2128

Abstract

This study aimed to analyze the effect of Sharia financial literacy, Sharia compliance, technology accessibility, and Islamic leadership on the performance of MSMEs in Riau – Indonesia, with the mediation of Sharia Financial Technology Adoption and the moderation of government regulations. This research employed a quantitative study approach using a survey methodology. The sample consisted of 500 MSMEs in Riau that had used services from Sharia financial institutions and had been operating for at least one year. The analytical technique employed was Structural Equation Modeling (SEM) with the assistance of SmartPLS 4 software. The results showed that Sharia financial literacy, technological accessibility, and Islamic leadership had a significant effect on Sharia financial technology adoption and MSME Business performance. Furthermore, Sharia fintech adoption significantly mediated the effect of these three variables on the performance of MSMEs. Conversely, the effect of Sharia compliance on Sharia financial technology adoption and MSME performance was not significant, nor was the moderating effect of government regulations, which failed to strengthen the relationship between Sharia financial technology adoption and MSME business performance.
Prospects of Wind Turbines With Diffuser Augmented Configurations: Bibliographic and Patent Analysis Aydar Kurmanov; Bulat Salykov; Ayap Kurmanov; Uralbay Khassenov
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/sjht2f79

Abstract

Diffuser-augmented wind turbines (DAWTs) are a promising wind-energy concept designed to enhance airflow through the rotor and improve energy capture, especially in low-wind and urban environments. This study evaluates the development prospects of DAWTs through an integrated bibliometric and patent analysis. Bibliographic records were retrieved from Scopus, and patent records were obtained from Google Patents for the period 2013–2023. After screening and eligibility assessment, 334 scientific publications and 401 patent families were included in the final analysis. The results show a sustained increase in research output, with China, the United States, and Japan emerging as leading contributors. Keyword co-occurrence analysis identified four dominant research clusters: aerodynamics and optimization, design and materials, urban and distributed systems, and noise and control. Patent activity peaked around 2020 and was concentrated in China, the United States, and Europe, indicating growing commercial interest. Although DAWTs can achieve rotor-area-normalized performance values above the classical Betz limit, such results should be interpreted within the specific fluid-dynamic framework of ducted systems. Overall, DAWTs demonstrate strong technical potential, but large-scale deployment remains constrained by structural complexity, cost, and limited field validation. Future progress depends on scalable design, field testing, and techno-economic assessment.
Pose Estimation Frameworks in Healthcare: A Systematic Review Egga Asoka Asoka; Fathoni Fathoni; Hadipurnawan Satria; Indra Griha Tofik Isa
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9779

Abstract

Human pose estimation has become increasingly important in healthcare applications such as fall detection, gait analysis, and rehabilitation monitoring. However, existing systematic reviews remain fragmented and largely descriptive, with limited comparative benchmarking and insufficient attention to clinical validation. This study addresses this gap by providing a structured comparison of major pose estimation frameworks in healthcare contexts. A systematic literature review was conducted using the PICOC framework and PRISMA guidelines. Studies published between 2020 and 2025 were retrieved from Scopus, Web of Science, IEEE Xplore, and PubMed based on predefined inclusion and exclusion criteria. Following screening and quality assessment, 41 studies were included in the final analysis. The results indicate that framework performance varies according to application requirements. OpenPose offers high anatomical precision but requires substantial computational resources, whereas MoveNet and MediaPipe enable real-time performance with lower latency, making them suitable for mobile and telehealth settings. Nevertheless, the evidence remains heterogeneous, with challenges related to occlusion, lighting variability, lack of standardized datasets, and limited real-world clinical validation. This study contributes by providing a theoretical synthesis and practical guidance for selecting appropriate pose estimation frameworks in healthcare applications.
Analysis of the Use of Artificial Intelligence in the Form of a Chatbot as an Information Search Engine Joshua Varrelino Rahardjo; Fathy Radhia; Edi Purnomo Putra
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9999

Abstract

The quick development of artificial intelligence has contributed to the use of chatbots as alternative methods for collecting information within organizations. However, empirical studies that combine technology acceptance and system success perspectives to clarify chatbot usage remain lacking. This study analyzes the adoption of chatbots as information search engines by integrating the Technology Acceptance Model (TAM) and the DeLone and McLean Information Systems Success Model (ISSM) through a quantitative approach involving the distribution of questionnaires to 400 employees who have experience interacting with chatbots. The research model includes Information Quality, System Quality, Service Quality, Perceived Ease of Use, Perceived Usefulness, Intention to Use, and Actual Use, and the data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that System Quality and Service Quality substantially impact Perceived Ease of Use and Perceived Usefulness. Perceived Ease of Use significantly influences Perceived Usefulness, which eventually impacts Intention to Use and Actual Use. However, Information Quality doesn't significantly impact Perceived Ease of Use or Perceived Usefulness. These results provide theoretical and practical insights for improving chatbot adoption for employees.
A Parallel Comparative Multi-Scenario Framework For Diabetic Retinopathy Detection Using Three-Tiered Feature Selection Loneli Costaner; Nor Hazlyna Harun
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/xtfckd08

Abstract

Early detection of Microaneurysms (MAs) is vital for diagnosing Diabetic Retinopathy, yet standard deep learning models often struggle with high false-negative rates and overfitting on limited medical datasets. Objective: This study proposes a Parallel Comparative Multi-Scenario Framework to identify the most robust configuration for MA detection. The framework evaluates independent 1D vectorized feature descriptors, each initialized as a high-dimensional 16,384-feature baseline, to avoid the redundancy inherent in feature fusion. Methodology: The system systematically processes six independent descriptors LBP, GLCM, Gabor, Wavelet, Fractal, and LMR across three selection tiers (Filter, Wrapper/RFE, and Embedded). These optimized vectors, reduced from the initial 16,384 dimensions to the most discriminative "Best Subsets," serve as uniform inputs for six classifiers: five traditional Machine Learning (ML) models and a proposed representation-consistent 1D-CNN architecture, resulting in 128 experimental scenarios. Results: Experimental evaluation was conducted on a balanced dataset of 740 fundus images derived from two distinct sources: the publicly available MESSIDOR dataset and a clinically acquired dataset from Hospital Universiti Sains Malaysia (HUSM). The model was trained on MESSIDOR data and subsequently evaluated on an independent HUSM test set to assess generalization performance. The results reveal a significant performance gap. The independent LBP-RFE-SVM scenario achieved the highest performance with an accuracy, recall, and precision of 91.00%. In contrast, the best Deep Learning (DL) configuration, Gabor-ANOVA-1DCNN, reached 87.00% accuracy. Notably, while the 1D-CNN maintained a "performance floor" of 60%, ML demonstrated extreme volatility, dropping to 51.00% with global statistical features. The optimal framework significantly minimized the False Negative Rate (FNR) to 6.76%, missing only 5 out of 74 cases.
The Effect of Quenching With Oil on Annealing Temperature Variations on The Hardness Of ST 37 Equivalent Steel Muh Anhar; Yusuf Yusuf; Irianto SP; Ningrum Astriawati
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.10558

Abstract

ST37 equivalent steel is widely used in engineering applications due to its high ductility; however, its relatively low hardness limits its performance in wear-related components. This study investigates the effect of annealing temperature variations and different oil quenching media on the hardness of ST37 equivalent steel. Specimens were annealed at 750°C, 850°C, and 950°C with a holding time of 10 minutes, followed by quenching in three types of oil: SAE 15W-40 mineral oil, SAE 10W-40 semi-synthetic oil, and SAE 10W-40 fully synthetic oil. Hardness measurements were conducted using the Rockwell B scale (HRB) at five different points on each specimen. The results indicate a consistent increase in hardness with increasing annealing temperature for all quenching media. The highest hardness value, 43.4 HRB, was obtained at 950°C using fully synthetic oil, representing an improvement of approximately 4.58% compared to the untreated material. Among the tested media, fully synthetic oil exhibited the most effective cooling performance. These findings demonstrate that both annealing temperature and oil type significantly influence the hardness characteristics of ST37 equivalent steel.

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