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International Journal of Applied Science and Technology Application
Published by CV. Raskha Media Group
ISSN : -     EISSN : 31249132     DOI : https://doi.org/10.62712/ijapset
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The International Journal of Applied Science and Technology Application (IJAPSET) aims to publish high-quality research that advances the application of scientific knowledge, artificial intelligence, and engineering technologies to address real-world challenges. The journal promotes interdisciplinary research that integrates computational intelligence, engineering systems, and technological innovation to develop practical and scalable solutions for industry, infrastructure, and digital transformation. IJAPSET provides an international platform for researchers, engineers, and technology practitioners to disseminate innovative findings, validated technological implementations, and applied research that contribute to scientific advancement and sustainable technological development.
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Articles 8 Documents
 1 
Integrated Multi-Domain Modeling Framework for Energy Efficiency and Range Prediction in Modern Electric Vehicle Systems Siti Khodijah; Cindy Atika Rizki; Muhammad Hasanuddin
International Journal of Applied Science and Technology Application Vol. 1 No. 1 (2026): Optimization and Computer Science
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/ijapset.v1i1.1

Abstract

The rapid advancement of electric vehicle (EV) technology has intensified the need for comprehensive theoretical frameworks capable of accurately evaluating energy efficiency and driving range under realistic operating conditions. This study presents an integrated multi-domain modelling approach that combines drivetrain physics, battery dynamics, drive-cycle analysis, control strategy optimization, and data-driven prediction to assess energy consumption in modern EV systems. A mechanistic model was developed to capture longitudinal vehicle dynamics, resistive forces, motor–inverter efficiency, battery behavior, and regenerative braking processes. The model was evaluated under standardized driving cycles, including the New European Driving Cycle (NEDC), Worldwide Harmonized Light Vehicles Test Procedure (WLTP), and Indian Driving Cycle (IDC), to investigate the impact of speed profiles and acceleration patterns on energy performance. The results demonstrate that energy consumption varies significantly across drive cycles, with aerodynamic drag and vehicle mass emerging as dominant influencing factors. Regenerative braking contributes meaningful energy recovery in urban conditions, though its effectiveness depends on control strategy and battery constraints. Comparative analysis between mechanistic modelling and machine learning approaches reveals that data-driven models improve predictive accuracy, while physics-based models provide interpretability and theoretical robustness. Furthermore, advanced control strategies such as Model Predictive Control (MPC) show superior performance in reducing energy consumption and range uncertainty compared to conventional PI-based controllers. Overall, the findings confirm that EV energy efficiency is an emergent property shaped by the interaction of design parameters, operational conditions, and intelligent control. The proposed integrated modelling framework provides a reliable foundation for next-generation EV design optimization, accurate range estimation, and sustainable mobility planning.
Smart Home Automation Using IoT Sensors and Microcontrollers Cindy Atika Rizki; Nabila Khairuniza; Muthiah Habibah
International Journal of Applied Science and Technology Application Vol. 1 No. 1 (2026): Optimization and Computer Science
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/ijapset.v1i1.2

Abstract

The rapid development of Internet of Things (IoT) technology has significantly influenced the way residential environments are managed and controlled. Smart home automation has emerged as an effective solution for improving comfort, security, and energy efficiency through the integration of sensors, microcontrollers, and network communication systems. This study presents the design and implementation of a smart home automation system using IoT sensors and microcontrollers to monitor environmental conditions and automatically control household devices. The proposed system utilizes several sensors to detect parameters such as temperature, humidity, light intensity, and motion, which are processed by a microcontroller to determine appropriate system responses. Based on predefined conditions, the system can automatically activate or deactivate devices including lighting systems, ventilation fans, and security alerts. Wireless communication enables remote monitoring and control through internet-connected devices, allowing users to manage their home environment from different locations. The results show that the system operates reliably and responds quickly to environmental changes, demonstrating effective automation performance. In addition, the implementation of sensor-based control contributes to improved energy efficiency by ensuring that electrical devices operate only when necessary. Overall, the proposed IoT-based smart home automation system provides a practical and scalable approach for developing intelligent residential environments supported by modern digital technologies.
Urban Vegetation Cover Prediction Using Sentinel-2 NDVI and Random Forest: A Brief Narrative Review Muhammad Hasanuddin; Abil Alwi Prayoga
International Journal of Applied Science and Technology Application Vol. 1 No. 1 (2026): Optimization and Computer Science
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/ijapset.v1i1.3

Abstract

A predictive model of urban vegetation cover is developed by integrating remote sensing technology, cloud computing, and machine learning algorithms. The study used the Normalized Difference Vegetation Index (NDVI), calculated from Sentinel-2 satellite imagery and analyzed in Google Earth Engine (GEE), to monitor vegetation conditions at a wide spatial scale. The research approach uses quantitative methods, including spatial analysis based on satellite imagery and predictive modeling with the Random Forest algorithm. The research process includes acquiring Sentinel-2 Level-2A images, pre-processing them with cloud masking and atmospheric correction, calculating NDVI values, and developing vegetation prediction models using machine learning methods. The results showed that the Random Forest model predicted vegetation cover with high accuracy, as indicated by a Coefficient of Determination (R²) of 0.85 and a Root Mean Square Error (RMSE) of 0.045. The resulting vegetation distribution map shows significant variations in vegetation density between natural vegetation areas, agricultural land, and built-up areas. The findings of this study show that integrating NDVI from Sentinel-2, Google Earth Engine, and the Random Forest algorithm is an effective approach for monitoring and predicting urban vegetation cover. The results of this study make a methodological contribution to the development of remote sensing-based geospatial analysis and provide a scientific basis for sustainable urban planning and green open space management in urban areas.
K-Means Clustering for Market Basket Data Segmentation Eka Pandu Cynthia; Maulidania Mediawati Cynthia; Dessy Nia Cynthia
International Journal of Applied Science and Technology Application Vol. 1 No. 1 (2026): Optimization and Computer Science
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/ijapset.v1i1.4

Abstract

The rapid growth of retail transaction data has created new opportunities for businesses to analyze customer purchasing behavior and improve decision-making strategies. Market basket data contains valuable information about product combinations purchased together within a single transaction, which can reveal hidden patterns of consumer behavior. This study aims to apply the K-Means clustering algorithm to segment market basket transaction data based on similarities in purchasing patterns. The research method involves several stages, including data preprocessing, transformation of transaction data into a binary feature matrix, determination of the optimal number of clusters, and clustering analysis using the K-Means algorithm. The results show that the clustering process successfully groups transactions into several clusters representing different purchasing characteristics. Each cluster reflects distinct consumer behavior patterns such as routine household purchases, breakfast-related items, snack-oriented transactions, and fresh product selections. These findings demonstrate that K-Means clustering can effectively identify meaningful patterns within market basket datasets. The clustering results provide useful insights that can support retail strategies such as targeted promotions, product bundling, store layout optimization, and inventory management. Overall, the application of clustering techniques in market basket analysis contributes to improving data-driven decision-making and enhancing the understanding of customer purchasing behavior in retail environments.
Web Based Academic Data Management System for Higher Education Nur Syifa'u Sitha; Raihan Syafawi Batubara; Siti Khodijah
International Journal of Applied Science and Technology Application Vol. 1 No. 1 (2026): Optimization and Computer Science
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/ijapset.v1i1.5

Abstract

The scholarly publishing ecosystem has experienced significant transformation in recent years, influencing how scientific knowledge is produced, evaluated, and disseminated. Central to this system is the peer-review process, which serves as a fundamental mechanism for maintaining research quality, credibility, and methodological rigor. This study aims to analyze the key components of the contemporary scholarly publishing environment, including peer-review practices, editorial governance, publication models, ethical standards, scientific writing competencies, and the global dissemination of research. The study employs a qualitative descriptive approach using a systematic literature review method. Relevant academic sources were collected from reputable databases such as Scopus, Web of Science, and Google Scholar. The collected literature was analyzed using thematic analysis to identify major patterns and themes related to the functioning and evolution of scientific publishing. The results indicate that peer review continues to play a critical role in ensuring research reliability, although challenges such as reviewer shortages and increasing submission volumes persist. Editorial governance and transparent evaluation procedures are essential for maintaining publication standards and credibility. Additionally, the growth of open access and hybrid publishing models has improved the accessibility of research outputs but also introduced financial and ethical considerations. The findings further highlight the importance of scientific writing competence and adherence to ethical guidelines in improving manuscript acceptance and research integrity. Moreover, disparities in language, resources, and institutional support continue to influence the global visibility of research. Overall, the study emphasizes the need for collaborative efforts among authors, reviewers, editors, and institutions to strengthen the sustainability, transparency, and inclusiveness of the scholarly publishing ecosystem.
Design and Evaluation of an IoT–Augmented Reality-Based Smart Agriculture Information System Chairul Rizal
International Journal of Applied Science and Technology Application Vol. 1 No. 1 (2026): Optimization and Computer Science
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/ijapset.v1i1.6

Abstract

This study aims to design and evaluate a Smart Agriculture Information System model that integrates Internet of Things (IoT) and Augmented Reality (AR) technologies to enhance irrigation efficiency and support data-driven decision-making processes. A mixed-method approach was employed within the Design Science Research (DSR) framework, involving 32 participants consisting of farmers, agricultural extension officers, and system administrators. The system was developed by deploying IoT-based sensors to monitor real-time field conditions, including soil moisture (45%–72%), temperature (24–31°C), and pH levels (5.5–6.8). These data streams were further integrated with an AR application to provide contextual, in-situ visualization for users in the field. The findings indicate a significant improvement in irrigation decision-making efficiency, as reflected by a reduction in processing time from approximately ±15 minutes to ±9–10 minutes, equivalent to an efficiency gain of around 33–35%. Furthermore, the system enhances the accuracy of land condition monitoring and enables more adaptive irrigation management based on dynamic environmental parameters. Usability evaluation using the System Usability Scale (SUS) yielded an average score of 78, which falls into the “good” category, indicating a favorable level of acceptance among users, including those without technical backgrounds. The primary contribution of this study lies in the development of an integrative IoT–AR model that supports precision irrigation through real-time data utilization and interactive visualization. These findings reinforce the implementation of Agriculture 4.0 by emphasizing not only technological innovation but also user interaction aspects. However, the study is limited to a relatively small-scale implementation and does not directly measure its impact on crop yield productivity
Design and Evaluation of Smart Medical Mechanical Systems for Real-Time Rehabilitation Monitoring Kodai Kitagawa; Edi Ismanto
International Journal of Applied Science and Technology Application Vol. 1 No. 2 (2026): Internet of Things and Intelligent Infrastructure
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/ijapset.v1i2.8

Abstract

This research aims to develop and evaluate Smart Medical Mechanical Systems based on the integration of mechanical engineering, medical sensor engineering, embedded systems, and the Internet of Medical Things (IoMT) to support real-time rehabilitation monitoring. The research uses a Research and Development (R&D) approach with stages of needs analysis, mechanical design, medical sensor integration, embedded system development, laboratory testing, and initial clinical validation. The research subjects involved 42 participants consisting of post-stroke rehabilitation patients, mechanical engineers, biomedical engineers, and rehabilitation doctors. The research instruments include Electromyography (EMG) sensors, Inertial Measurement Units (IMU), load cells, motion capture, usability testing, and a cloud-based rehabilitation monitoring system. The research results show that the system successfully performed real-time monitoring of patients' biomechanical and physiological parameters with a sensor accuracy rate of 94.2%, a 28% increase in movement efficiency, and a 31% increase in user comfort. The system also supports more objective rehabilitation evaluations thru a cloud-based monitoring dashboard. In addition, the ergonomic mechanical design and multimodal sensing integration have proven to enhance the quality of human-rehabilitation device interaction. This research concludes that the integration of smart medical engineering and IoMT can enhance the effectiveness of modern rehabilitation and support the development of data-driven rehabilitation within the smart healthcare ecosystem. This research also contributes to the development of smarter rehabilitation systems that are more adaptive, personalized, and integrated for both clinical rehabilitation and telemedicine.
Development of an IoMT Rehabilitation System with EMG Sensor Integration and Ergonomic Design for Adaptive Medical Rehabilitation Mohamed Wajdi Ladghem-Chikouche; Eka Pandu Cynthia
International Journal of Applied Science and Technology Application Vol. 1 No. 2 (2026): Internet of Things and Intelligent Infrastructure
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/ijapset.v1i2.9

Abstract

This study aims to develop an IoMT Rehabilitation System based on the integration of Electromyography (EMG) sensors, embedded Internet of Medical Things (IoMT), adaptive actuators, and ergonomic design to improve the effectiveness of real-time adaptive medical rehabilitation. The study employed a Research and Development (R&D) method using a multidisciplinary approach integrating mechanical engineering, biomedical engineering, and embedded systems. The research subjects consisted of 42 participants, including mechanical engineers, biomedical engineers, rehabilitation physicians, and post-stroke patients with mild to moderate conditions. The research process included mechanical device design, prototype fabrication, EMG and IoMT sensor integration, biomechanical testing, and limited clinical validation. The results showed that the developed rehabilitation system achieved an EMG sensor reading accuracy of 94.2%, improved patient movement efficiency by 28%, and increased device comfort by 31% compared to conventional rehabilitation systems. Furthermore, the implementation of IoMT enabled real-time rehabilitation monitoring through a digital dashboard, allowing medical personnel to evaluate therapy progress more objectively and systematically. The integration of EMG-based adaptive actuators successfully created a closed-loop rehabilitation system capable of providing dynamic movement assistance according to the patient’s physiological conditions. This study contributes to the development of smart rehabilitation engineering based on biomechanics, IoMT, and ergonomic design as a more adaptive, precise, and efficient solution for modern medical rehabilitation.

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