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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 358 Documents
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A Comprehensive Review of Deep Learning Techniques for Intrusion Detection in the Internet of Medical Things Muhammad, Aisha Essa; Abdulrahman, Amer Abdulmajeed
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): 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.v7i1.6637

Abstract

The work revisits the security issues of Internet of Medical Things (IoMT) platforms and provides a list of deep learning models used for intrusion detection. The study fills the salient gap in early detection of actual IoMT system intrusions for enhanced medical device and data security. A wide-ranging and systematic investigation of deep learning models, such as CNNs, LSTMs, and hybrid ones (GNNs and BiLSTMs) recently introduced was carried out. These were then analyzed against well-known benchmark datasets, such as ToN-IoT and IoT-Healthcare Security and WUSTL-EHMS-2020, to consider the quality of their detection work on cybersecurity threats for IoMT systems. The results indicated high accuracy in cyber threat detection, reaching even 100% accuracy. But the challenges are still how to decrease false positives and improve the real-time performance of the model on robustness and generalization when making real-world applications. The research is literature-based and aimed to provide some further updates on a secure IoMT framework by identifying recent studies in the security of the IoMT ecosystem and shedding light on future work using hybrid methods, blockchain technology, or federated learning approaches that can contribute to the detection of IDSs. And all can help pave the way for a more secure, privacy-protecting IoMT that safeguards extremely sensitive medical data. The research also enhances the model: utilizing 15+ deep-learning models to propose an IoMT-resistant architecture. This can promote participation in the theoretical research and practical security protocols in the IoMT context, thus drawing attention and comprehension from researchers and practitioners to enhance security protocols.
Quality Control in Small and Medium Enterprises: A Study of Present Challenges and Future Opportunities Widaningrum, Dyah Lestari; Meilia, Amelia; Charis, Samuel Nata; Ariyanti, Fransisca Dini; Amrina, Uly
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): 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.v7i1.6646

Abstract

Quality control is a critical foundation for establishing effective and efficient systems across industries, but small and medium enterprises (SMEs) face unique challenges in implementing effective quality control due to resource constraints. This study aims to identify the specific needs through demographic and operational analysis, and to develop a compact, adaptable Quality Management Framework to enhance their operational excellence. Survey responses from 50 SME managers were analyzed using cluster analysis, utilizing Ward's method and K-means to categorize businesses based on their quality control practices. Findings revealed three distinct SME clusters emerged, with systematic quality practices correlating to 71% higher customer satisfaction. These findings highlight significant variations in managerial perceptions, needs, and priorities.  This research proposes an adaptable quality management framework tailored for SMEs and recommends the integration of technology to enhance scalability and effectiveness. Thus, this study addresses a theoretical gap in SME quality control system design while offering practical, actionable insights for enhancing quality management processes in diverse industrial contexts. 
Assessment of Cyber Security Awareness Using Developed Game From H5P on Users Aged at Elementary and First Secondary School in Madiun City Andria, Andria; Laksono, Ridam Dwi; Sussolaikah, Kelik; Mat Din, Mazura Binti; Mansor, Shaifizat; Dawam, Siti Rafidah Muhamat
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): 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.v7i1.6705

Abstract

The increasing cyber threats among children using digital devices without supervision highlight the importance of early cybersecurity awareness. This study aims to develop and evaluate an H5P-based educational game to enhance cybersecurity awareness among elementary and junior high school students in Madiun City. Using a Research and Development (R&D) approach, the game was developed through four stages: needs analysis, design and development, expert validation, and limited field testing. The game incorporates gamification elements such as points, badges, and instant feedback to engage students in learning topics such as password management, data protection, and phishing recognition. The results indicate that 70% of 68 elementary students and 74% of 92 junior high school students showed improved awareness after playing the game. These findings suggest that interactive educational games can effectively enhance cybersecurity awareness among children. The study recommends broader implementation of gamified approaches in educational curricula to foster safe online behavior from an early age.
Magie Broom: Revolutionizing Cleaning with User-Centered Ergonomic Design Nafi'ah, Roikhanatun; Dewi, Adhe Lingga; Rosya, Kamila Nur
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): 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.v7i1.6850

Abstract

Magie Broom is an innovative cleaning tool designed by considering several principles such as ergonomics and anthropometry. This study aims to determine the optimal ergonomics design that can minimize the risk of musculoskeletal disorders (MSDs) that often arise from the use of traditional cleaning tools. The development of this tool involves several stages: literature review, field observations, and detailed design using the Quality Function Deployment (QFD) method, followed by testing. Anthropometric principles, especially for determining the optimal length and grip, are carefully considered to ensure the design meets user needs for ease of use, comfort, and cleaning effectiveness. The design The Magie Broom prototype was tested by various user groups such as housewives and students to obtain input used in refining the design. The test results showed that this tool was able to increase cleaning efficiency by provide 3in1 function and modularity. Its also provide comfort for users and reduce the potential for MSDs by decrease REBA score from 11 to 3. The design is very easy to carry, modular, and equipped with a rechargeable battery. The Magie Broom serves as a promising model for ergonomic product development especially in houshold tools, illustrating how thoughtful design can minimize physical strain and injury risk. Magie Broom offers a practical solution for everyday cleaning needs and has great potential to be further developed and marketed widely.
Fabrication and Measurement of a Circularly Polarized All-Metal Patch array and Single PRS Layer based Fabry-Perot Antenna for NASA Interplanetary CubeSat missions Omari, Fouad; Benhmimou, Boutaina; Gupta, Nancy; El Khadiri, Khalid; Ahl Laamara, Rachid; El Bakkali, Mohamed
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): 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.v7i1.6904

Abstract

When it comes to pushing the limits of small-scale technologies for interplanetary space missions, NASA has demonstrated that the dream is achievable. NASA has launched a number of interplanetary CubeSat missions, including as MarCO A and B, INSPIRE, LOGIC, and LunaH-map CubeSats, successfully during the past 10 years. Since the antennas of these NASA-certified satellites are responsible for defining the range of communication with Earth, they are the main topic of this study. A high gain and circularly polarized all-metal patch array with PRS-based Fabry Perot antenna is proposed in this paper for NASA’s interplanetary CubeSat missions. The strength of this approach stems from its dependence on a wholly novel concept for developing a durable antenna that is harmoniously compatible with NASA's goals. The resulting all-metal patch array was well-fabricated and tested in an anechoic chamber and using a VNA, and showed measured realized gain of 35.84 dBi and AR of 1.629 dB at 11.5 GHz with 3dB-AR bandwidth higher than 3 GHz. Additionally, the Fabry-Perot design enhances the gains, achieves AR of 0.29 dB at 12.1 GHz, and exhibits RHCP and LHCP making this new technique as suitable candidates for NASA's interplanetary CubeSat missions.
Enhanced CSMA/CA Protocol-Based Optimal Robust Dynamic Query-Driven Clustering for Improved QoS in Heterogeneous WSNs Jubair, Ahmed Mahdi; Thulnoon, Akeel Abdulraheem; Mubarek, Foad Salem
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): 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.v7i1.6928

Abstract

Heterogeneous Wireless Sensor Networks (HWSN) are basically decentralized and distributed systems that playing a crucial role in numerous Internet of Things (IoT) applications, enabling efficient monitoring and data collection. However, these networks often suffer from high latency, routing overheads, and energy consumption. To meet these challenges effectively,  This article proposes an enhanced CSMA/CA protocol based on an Optimal Robust Dynamic Query-Driven Clustering Protocol (ECODQC) model. The enhanced model includes two key components: the improved CSMA/CA protocol, which reduces network collisions, lowering delay and overhead during communication, and the Optimal Robust Dynamic Query-Driven Clustering (ODQC) protocol, which efficiently reduces energy consumption among sensors. In the first phase, the modified CSMA/CA protocol focuses on analyzing communication delays, defining dynamic data transmission, and evaluating data delivery beyond predefined times. In the second phase, the ODQC protocol addresses optimal load balancing and the dynamic process of cluster head selection, aiming to reduce energy consumption during sensor communication. The proposed techniques demonstrate superiority over conventional protocols and are recommended for enhancing the overall quality of service in decentralized, distributed HWSN-based IoT networks.  The ECODQC model is compared against existing methods using the NS2 simulation platform in two scenarios: the varying numbers of nodes and varying speeds. The performance parameters of this proposed model are analyzed in terms of energy efficiency, cluster head efficiency, data success rate, computational delay, and node throughput. The Results demonstrate that ECODQC proves to be superior compared to existing techniques in terms of energy efficiency of 432.23 J, low latency of 85.23 ms, and increased throughput of 813.77 Kbits/s. With these observations, the possibility of using ECODQC with a high level of applicability in real-time IoT scenarios is evident
Redesigning Passenger Distribution System of KRL Commuter Line: An Integrative Approach Ho, Hwi-Chie; Venessa, Venessa; Sopha, Bertha Maya
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): 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.v7i1.6954

Abstract

Commuter line electric rail (KRL) has become a critical mode of public transportation supporting urban mobility in densely populated areas. However, the rapid growth of urban populations and the corresponding increase in daily commuters have created significant challenges in delivering optimal and comfortable services due to overcrowding. This study addresses these challenges by enhancing passenger comfort on KRL commuter lines through the redesign of the passenger distribution system considering personal space along with passenger flow management. An integrative approach combining ergonomic approach in determining carriage capacity, passenger flow management, and simulation-based analysis was employed. Empirical data were collected through observation, empirical survey, and direct anthropometric measurement. Observations on passenger density were conducted on the Cikarang line during peak morning hours, focusing on mixed-gender carriages. Anthropometric measurements involving 238 subjects alongside carriage dimensions were analyzed to determine the capacity of carriage using ergonomic principles of personal space  The results revealed that  the carriage capacity of 150 passengers balancing comfort and efficiency. An innovative passenger distribution system deploying queuing system equipped with integrated sensors providing real-time number of passengers and innovative automatic door-closing mechanism at the carriages were proposed and tested under current passenger density and determined carriage capacity using discrete-event simulation implemented in Arena software. This study provides novel contribution both practically and theoretically demonstrating the application of ergonomics and sensor integration in public transportation system design for improving commuter comfort and safety in highly congested urban transport systems. Future researches are also discussed.
Performance Improvement of Quality Monitoring Systems in Imbalanced Data Conditions for Fat-Filled Powder Quality in The Dairy Industry Asrol, Muhammad; Pratama, Oki
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): 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.v7i1.6996

Abstract

Fat-filled powder has the potential to substitute milk in meeting the nutritional needs of the community, but its product quality remains unstable during continuous production processes. A key challenge in fat-filled powder (FFP) production is the difficulty in quality monitoring, which is influenced by various uncertainty factors that affect product quality. Machine learning can be implemented for quality monitoring system, but the imbalanced data conditions require the development of algorithms with optimal performance. This study aims to design a quality monitoring system for FFP using a machine learning model under imbalanced dataset conditions and the influence of other uncertainty factors. A Random Forest (RF) machine learning model was developed for monitoring FFP quality. In the context of imbalanced datasets, the model was optimized through various scenarios, including data splitting for training and testing, as well as the Synthetic Minority Oversampling Technique (SMOTE) and Distribution Optimally Balanced – Stratified Cross Validation (DOB-SCV) schemes. The results showed that the SMOTE model achieved the best performance in terms of accuracy, precision, and recall with scores of 99.67%, 99.79%, and 99.24%, respectively, on the testing data. Statistically, the RF model with the SMOTE data manipulation scenario also showed significant differences compared to the DOB-SCV model and the traditional data splitting approach. The quality monitoring model for FFP developed in this study can be implemented in the dairy industry, offering more stable, accurate quality monitoring predictions that align with real conditions, helping to avoid quality uncertainties during the production process. The implementation of this model in the industry has the potential to facilitate a broader, more transparent, and optimized product quality evaluation process, which can also be conducted in real time under continuous production conditions.
Using Fuzzy Cognitive Maps For Modeling Environmental Aspect of Sustainable Development in Construction Projects Alsaadi, Atheer M.; Abdulhameed, Ali A.; Alsaadi, Farah M.; Alhashmi, Heba A.
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): 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.v7i1.7041

Abstract

The pillars of sustainable development are representing the interface between environmental, economic, and social sustainability. Sustainable development is a method of planning and managing construction projects to reduce the effect of the construction process on the environment so that there is a balance between environmental capabilities and the human needs of present and future generations. Usually, Environmental sustainability is most important and effective in construction projects. The environment suffers from significant negative impacts as a result of the implementation of construction projects; therefore, this study aims to identify the effecting factors on environmentally sustainable development. The methodology of this study used fuzzy cognitive maps (FCMs) because of adopted simulation approach, after selecting the factors that have RII more than 65% and determine causal relationship between factors by applying fuzzy logic using MATLAB program. Then the effecting factors were analyzed and ranked by static and dynamic analysis. The results showed the static analysis of effecting factors on ESD in first quarter are characterized by influential and affected by other factors of (ESD), were include (C2.4, C4.6, C1.6, C2.1, C3.3, C3.7, C3.6, C6.2), When comparing between dynamic analysis and RII of the factors, it has been noticed a difference in the importance. This is an essential finding in the understanding that dynamic analysis considers the interactions between factors, while the RII takes the reasons independently and neglects interactions between them. The study has provided recommendations for the application of (FCM) model that was proposed depend on these factors in building projects to improve the environment and reduce its negative effects.  
CNN-Based SIBI Sign Language Recognition Alphabet: Exploring the Impact of Hardware on Model Training Rakhmadi, Aris; Yudhana, Anton; Sunardi, Sunardi
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): 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.v7i1.7071

Abstract

The recognition of Sign Language Alphabets (SLA) plays a vital role in human-computer interaction, especially for individuals with auditory disabilities. This study aims to evaluate the impact of different hardware configurations—specifically CPU, GPU, and memory setups—on the training efficiency and recognition performance of a Convolutional Neural Network (CNN)-based model for SLA using the SIBI dataset. The novelty of this research lies in its focus on hardware-aware deep learning optimization for Indonesian sign language (SIBI), an underexplored area. The model was trained on 3,468 labeled hand gesture images representing 24 SIBI alphabet signs. Experiments were conducted on CPU (Intel Xeon 2.00 GHz) and GPU (Nvidia Tesla T4) platforms using a consistent CNN architecture. The training time was significantly reduced by 45.5%, from 1 hour 39 minutes to just 54 minutes, while the accuracy remained consistent at 96.7%, showing no significant change between the two setups. These results demonstrate the significance of parallel processing and memory bandwidth in enhancing model convergence and generalization. The findings are relevant for real-time SLA deployment with hardware constraints on embedded or mobile platforms. Overall, the study underscores the importance of hardware optimization in accelerating CNN training and improving performance in sign language recognition systems.

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