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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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University Students' Intentions Toward Entrepreneurial Careers in The Hospitality and Tourism Sector: Empirical Insights From The Techno-Savvy Generation in Higher Education Asmar Yulastri; Ganefri Ganefri; Feri Ferdian; Elfizon Elfizon; Yudha Aditya Fiandra; Geovanne Farell
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 2 (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.v6i2.6328

Abstract

This study investigates the impact of family support, entrepreneurial passion, entrepreneurial motivation, and techno-savvy culture on the entrepreneurial career intentions of university students in the hospitality and tourism sector, with entrepreneurship education as a moderating variable. Data were collected from 277 students at Universitas Negeri Padang’s Faculty of Tourism and Hospitality who had completed entrepreneurial courses. Partial least squares structural equation modeling was employed to analyze the data. The findings reveal that family support, entrepreneurial passion, and entrepreneurial motivation significantly influence students’ entrepreneurial career intentions, while techno-savvy culture showed no direct impact. However, entrepreneurship education significantly moderated the relationships between these factors and entrepreneurial intentions. These findings provide actionable insights for enhancing entrepreneurship education to foster innovation and career readiness in the hospitality and tourism industry. The study contributes to existing knowledge by elucidating the interplay of support systems, intrinsic motivations, and education in shaping entrepreneurial aspirations, offering a foundation for educational and policy reforms to boost entrepreneurship in the sector.
Advancements In Blockchain Cryptography: Self-Signed Key Applications For Digital Record Protection Pawan Maheshwari; Sunil Gupta
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 2 (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.v6i2.6335

Abstract

The significant deployment of Electronic Health Records (EHRs) has introduced serious issues with data security and confidentiality. The proposed study addresses such issues by investigating innovation in blockchain cryptography, with a special focus on the application of self-signed keys for secure digital record management. The research combines the use of Elliptic Curve Cryptography (ECC) with a blockchain framework to suggest a decentralized and efficient solution for the management and authentication of digital records. The experimental evaluation of the proposed solution indicates the efficiency of the system with 1626.03 seconds of execution time, 0.0018 tps of throughput, and 3.1790 seconds of the average latency for 1000 transactions. Furthermore, the proposed solution reduces the encryption time to 3650 ms and the decryption time to 3968 ms as compared to the traditional implementation of the blockchain, with ensured data integrity. The outcome attests to the practicability of the employment of the application of the self-signed keys for the improvement of security, confidentiality, and integrity of data for healthcare systems. Furthermore, the proposed solution strengthens decentralized systems with the introduction of the optimized mechanism of cryptography that maintains efficiency with the guarantee of security, introducing a practical mechanism for the protection of confidential medical data for real-world systems.
Calorific Value of Palm Kernel Shell Charcoal (PKSC) Briquette as Solid Fuel Hendri Nurdin; Waskito Waskito; Fadhilah Fadhilah; Toto Sugiarto; Andre Kurniawan; Yolli Fernanda; Rudy Anarta; Fathi Aulia DZ
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 2 (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.v6i2.6336

Abstract

The need and utilization of energy in society exceed available production. This condition requires acceleration and efforts to find solutions through the diversification of palm shell biomass into solid fuel briquettes. Palm shells have the potential as biomass and renewable energy sources that are selected based on strategic, technical, and environmental considerations. Its utilization so far has only been burned directly which causes air pollution or used as road paving in oil palm plantations. The environmental impact is the accumulation of solid waste, and global warming in the Crude Palm Oil processing industry. The research objective was to obtain the calorific value of palm kernel shell briquettes with carbonization process. The experimental research method carried out by innovating palm kernel shell briquette raw materials at various percentage variances (90%: 10%, 85%: 15%, 80%: 20%, 75%: 25%) using tapioca adhesive. The technical parameters of briquettes making are molding pressure of 10 MPa, particle grains of 60 mesh, carbonization temperature of 400oC; 450oC; 500oC with a holding time of 1 hour. From this study, the calorific value of palm kernel shell charcoal (PKSC) briquettes at a concentration of 85%;15% at a temperature of 400oC was 25.86 MJ/kg with tapioca adhesives as the highest calorific value parameters. The technology used to make palm kernel shell charcoal briquettes is a potential development that can be recommended as a precursor to solid fuels. The impact of developing PKSC biomass energy briquettes is an innovation in utilizing waste to create solid fuels. The implications of this research can be applied by home industries or households. This research is a contribution to solutions in overcoming energy needs and deficiencies as a form of sustainable energy..
Design of A Digitalization System for Machine Scheduling and Allocation in Flexible Job Shop Heavy Equipment Manufacturing Industry Karim, Mohammad Alfin; Sahroni, Taufik Roni
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.v7i1.5089

Abstract

This study aims to develop a digitalized scheduling system based on the Flexible Job Shop (FJS) model to optimize production efficiency in the heavy equipment manufacturing industry. The heavy equipment manufacturing industry faces significant challenges in achieving production efficiency due to its high-mix, low-volume (HMLV) nature and the complexity of production processes. The research follows a structured approach, beginning with Focus Group Discussions (FGDs) to gather stakeholder requirements. These requirements are translated into a House of Quality (HoQ) matrix to prioritize features for the dashboard. A literature review identifies optimal scheduling methods, with a focus on FJS and heuristic scheduling rules. The dashboard is developed using JavaScript, PHP, Node.js, and PostgreSQL, and deployed on Amazon Web Services (AWS). The system undergoes black-box testing to ensure functionality and reliability before implementation. The study identifies the Earliest Due Date (EDD) method as the most effective scheduling approach, with an average delay of 3.2 days, utilization of 29%, and completion time of 14.33 days. The implementation of the digitalized scheduling system increased on-time production from 70.56% to 92.8% and improved production achievement from 92.78% to 97.4%. The dashboard application successfully integrates real-time data, adaptive scheduling, and operational features, such as a start-stop system and machine load recommendations. The findings highlight the importance of digital transformation in manufacturing, particularly in optimizing resource allocation, reducing delays, and improving production efficiency. This research contributes to the field of digitalized scheduling and real-time production management by providing a practical, data-driven solution tailored to the HMLV characteristics of heavy equipment manufacturing.
A New Strategy to Improve the Performance of Informed RRT* Algorithm in Solving the Global Path Planning of Mobile Robot Suwoyo, Heru; Winiyoga, January Dwidasa; Andika, Julpri
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.v7i1.6194

Abstract

Informed RRT* has been mentioned as a great method to find feasible and optimal solution of path planning. Technically, it uses the prolate hyper-spheroid and a centralized optimization strategy to gain the optimality of path. This optimization process is started when the initial feasible solution is found. Conventionally, the traditional procedure of RRT* is used to connecting starting point and goal point feasibly. Therefore, it is not suppressing if the optimization process begins later in large coverage of path planning problem. For this reason, a new strategy needs to propose with an objective to speed up the convergence rate by reducing the inefficiency of its blind sampling. Sequentially, it is conducted by integrating the bias technique and constraint sampling to replace the traditional sampling method. Next, the nearest node's ancestor is taken into consideration up until the first stage of choosing the parent is less expensive then RRT*. Regarding to these offers and the comparative results, the performance of the proposed method has shown better performance compared to its predecessor in terms of optimality, indicated by a decrease in finding the initial path by an average acceleration of 47.90% and a convergence rate indicated by an average path cost decrease value of 3.94%.
Comparison of Various Sky Model for Daylighting Availability Inside The Classroom with Bilateral Opening Typology in The Tropics Atthaillah, Atthaillah; Iqbal, Muhammad; Badriana, Badriana; Nabila, Putri Sri Alisia
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.v7i1.6204

Abstract

This study compares daylighting performance under four sky models of a classroom in tropical climates to understand the differences in illuminance and uniformity values. This research is significant as it can inform the relevance of the widely used static metric, such as the daylight factor, for daylight performance evaluation in tropical climates in comparison with the climate-based sky model which is utilized for dynamic metric calculation. Computational simulation was employed to achieve the objective. Grasshopper-Rhinoceros was utilized for the classroom model, while Radiance was employed for sky modelling and daylight simulation. The results indicated that static sky models exhibited greater discrepancies in their average illuminance and uniformity values compared to climate-based or dynamic sky models. The pervasive utilization of static metrics, such as the daylight factor, for evaluating daylighting performance within a space may necessitate reconsideration in tropical climates, given the higher error rates observed in this study for a classroom with bilateral opening design.
Developing a Cost-Effective Air Quality Monitoring Solution Using IoT Technology: Addressing Long-Distance Transmission Challenge Khonrang, Jarun; Winyangkul, Seksan; Duangnakhorn, Pairoj; Viratikul, Rungrat; Boonlom, Kamol
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.v7i1.6254

Abstract

This research explores the integration of Internet of Things (IoT) technology and LoRa repeaters to enhance air quality monitoring. IoT enables low-cost, real-time sensors for continuous air quality assessment, while repeaters address the limitations of traditional wireless communication over long distances. Our study demonstrates the effectiveness of a LoRa repeater system, with signal strengths between monitoring stations and repeaters ranging from -84 dBm to -92 dBm, achieving a practical operational range of 850 meters. The highest Packet Delivery Ratio (PDR) recorded was 65% using a Spreading Factor (SF) of 10, while SF 7 resulted in a PDR of 25%. Environmental factors and antenna gain were identified as critical for optimizing transmission power and communication reliability. This research underscores the potential of advanced IoT applications in extending internet connectivity and improving air quality management across various sectors, paving the way for smarter urban environments and public health initiatives.
Blockchain Framework for Secure IoT Operations in Military Applications: Integrating LoRaWAN and Helium Network S, Jebarani Evangeline; Arunachalam, Krishna Prakash; V, Seethalakshmi; A, Senthil Kumar; C, Reeda Lenus; R, Saranya
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.v7i1.6325

Abstract

The traditional IoT is typically based on centralized systems that are susceptible to multiple cyberattacks and a single point of failure. Modern industries regularly embrace block chain technology due to its decentralization and security. This study suggests a block chain-based system that guarantees reliable and secure operations. They suggest a secure compact block chain for handling access to precious information through instruments and controllers. Based on realistic military applications, the current investigation makes evidence for the benefits of merging LoRaWAN and Helium Network technology, and also demonstrates how deliberate research and analysis can bridge the block chain gap for military cyber defense.  To improve the proposed system's computing efficiency, the block chain network has devised a rapid and power-saving decision technique for proof of authentication. The suggested framework for smart industrial environments has survived extensive testing and study to be sustainable. Use the suggested configuration to convert a standard processing system into an intelligent and secure industrial platform. This article aims towards assessing the practicality of Proof of Authority in the block chains network as a consensus algorithm. There are numerous techniques available for creating a consensus among the nodes.
Enhancing the Effectiveness of the YOLO Model Through Caladium Leaf Images Generated by Generative Adversarial Networks Chandra, Rudy; Prasetyo, Tegar Arifin; Simamora, Akdes Simon; Simbolon, Amanda Artha Regina; Sinaga, Ester Krismayani; Perdanasari, Lukie
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.v7i1.6624

Abstract

The need for ornamental caladium plants is very popular, but there are several obstacles to recognizing its type. Caladium species classification using AI is needed to overcome the problem of misidentification among enthusiasts. This study uses the Generative Adversarial Network (GAN) algorithm to generate new images from the Caladium dataset: Amazon Caladium, Bicolor Caladium, White Queen Caladium, and Skull Caladium. We combine GAN with YOLOv5 to detect Caladium in real time to improve accuracy. The quality of the generated images is evaluated using the Kernel Inception Distance (KID) method, with the highest scores of 0.2320 for Amazon Caladium, 0.1966 for Bicolor, 0.1713 for Skull, and 0.1857 for White Queen, indicating close similarity to the original images. We chose the best model to generate three datasets: Original Dataset, Mixed Dataset (original images plus GAN-generated images), and Dataset consisting mainly of GAN images. The Mixed Dataset achieved the best results, with a mean Average Precision (mAP) of 0.695 for an Intersection over Union (IoU) of 0.50:0.95 outperforming the GAN dataset and the original Dataset. This training used 50 epochs, a learning rate of 0.0003, and a batch size of 16, to obtain the best model and significantly improve Caladium detection. From this experiment, it was found that the GAN, combined with the original data, was able to support the accuracy of YOLOv5 for real-time caladium classification and was also able to create new images that resembled the original leaves. In the mobile application, this model allows real-time identification of Caladium types, making it easier for users to buy Caladium according to the desired type.
Hybrid Optimization Model for Integrated Image Data Extraction Expert System in Rice Plant Disease Classification Aldo, Dasril; Kurniawati, Ajeng Dyah; Prabowo, Dedy Agung; Fauzi, Ahmad; Saputra , Wahyu Andi; Sudianto, Sudianto; Yasin, Feri; Agustianto, Satya Helfi; Pangestu, Farhan Aryo; Sulaeman, Gilang
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.v7i1.6633

Abstract

The purpose of this study is to increase the accuracy for rice plant disease classification by developing a hybrid optimization model using Convolutional Neural Network (CNN) in combination with Extreme Learning Machine (ELM), followed by Support Vector Machine. A key issue is to overcome with traditional expert systems that difficult, due the variation differences and complex among rice plant image data set. For feature extraction, plant images are passed through CNN and for classification ELM & SVM used. Experimental results show the best accuracy of 98.63% is attained using CNN+ELM model on images resized to 100x100 pixels and has precision, recall, F1-Score all at value=0.99 By comparison, the CNN+SVM model achieves an accuracy of 91.92% using that same image size. Top AbstractIntroductionMethodsResultsDiscussionConclusionReferencesOverall, the proposed CNN+ELM combination can classify rice plant diseases better than using only a conventional approach (CNN) through various results from devices with limited computing power. The study presents a novel plant disease detection system that can be utilized for the development of precise tools to help improve agricultural management practices.

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