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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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Innovation in Crop Nutrition Planning Based on Rainfall Prediction Using Singular Spectrum Analysis and Boosting to Optimize Agricultural Management Yuslena Sari; Mambang Mambang; Muhammad Zulfadhilah; Subhan Panji Cipta; Muhammad Nursandi; Finki Dona Marleny; Ricardus Anggi Pramunendar; Sunardi Sunardi; Eka Setya Wijaya; Aurelia Monica Sari; Muhammad Alkaff
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/z3mgdv08

Abstract

The high variability of rainfall in tropical climates presents a major challenge for agricultural management, as weather uncertainty often leads to inefficient fertilization practices due to nutrient loss. This study aims to develop a robust framework for rainfall prediction, which can inform a flexible and precise crop nutrient scheduling system. Utilizing an hourly rainfall dataset (n=6,624) obtained from IoT sensors, the research proposes an approach that integrates Singular Spectrum Analysis (SSA) for signal decomposition and noise reduction with Gradient Boosting algorithms (LightGBM and XGBoost). Spline interpolation was employed to handle missing data, while SSA served to disentangle deterministic trends from random noise, enabling the models to perform more effectively on the refined dataset. Empirical evaluation demonstrates that the SSA-XGBoost hybrid model achieves superior performance, with an RMSE of 0.0057 and an R² of 0.8278, significantly outperforming the SSA-LightGBM model (R² 0.2879), which struggled to capture non-linear patterns within this dataset. The high predictive accuracy of the SSA-XGBoost model facilitates the implementation of responsive nutrient management strategies, wherein fertilizer application can be deferred during forecasted periods of high rainfall to prevent runoff and environmental pollution. This research contributes to the field of hydroinformatics by demonstrating the effectiveness of combining SSA and XGBoost as a cost-efficient yet high-performance solution for mitigating climate-related risks in tropical wetland agriculture.
Study on Machining Process of Multi-step Holes by Using The Standard Cutting Tool and Stepped Drill V. L. Trinh; T. S. Nguyen; K. T. Chu; V. H. Tran
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: 10.37385/1fpc2e08

Abstract

Besides technological solutions, machining productivity is a factor that it is always considered by manufacturers. The cutting tool (CT) is one of the key points to boost the cutting efficiency and improve machining productivity of the machining process. The proper cutting tool is a good solution for saving the cutting time, enhancing the machining quality, and improving the time life of machine tools. Traditional drilling method uses a standard cutting tool (SCT) with single cutting diameter that revealed disadvantages of low cutting velocity and high cost. This paper demonstrates a method of using the combination cutting tool (CCT)that equipped more cutting diameters in the same knife body to enhance machining productivity and reduce cutting time during drilling multi-step holes.  By introducing a CCT, the machining productivity increases of about 131.72% and 257.9% in comparison to the CC in the option 1 and option 2, the cutting time deceases of about 72.06% and 56.85% in comparison to the CC in the option 1 and option 2, and the technology cost decreases of about 42.86% and 63.64% in comparison to the CC in the option 1 and option 2, respectively. The results show that the combination cutting tool is a good solution to improve the machining productivity and cost effectiveness during machining multi-step holes.
Optimizing Thermal Load in Compact Buildings: A Comparative Analysis of Single and Hybrid Metaheuristics for Balanced HVAC Efficiency Usman Usman; Fatchul Arifin; Rustam Asnawi
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.v7i2.7618

Abstract

This work is a comprehensive comparison study of five metaheuristic algorithms — namely GA, PSO, SA, GA-PSO, and GA-PSO-SA — using 3,000 model evaluations across 30 independent runs to optimize compact building thermal loads. Statistical analysis demonstrates that the SA, GA-PSO, and GA-PSO-SA present similar accuracy (RMSE: 3.55±0.16 kWh/m²) without any statistical difference (p> 0.97), contradicting the hypothesis that complexity in hybrid promotes the actual performance. GA appears to be the best compromise, with the highest efficiency ratio (11.47), providing 97.5% of SA's accuracy at 42% lower computational cost. The instability shown by PSO is quite alarming (CV: 14.215%, performance spread: 53.6%) and clearly indicates premature convergence, which sharply contradicts its claim of outperforming in continuous optimization [1]. Sensitivity analysis results show that envelope thermal properties, particularly the wall U-value (NSC=1.43), have 7.5× more influence on prediction performance than building orientation, providing evidence supporting the argument that input data quality outweighs algorithm choice for HVAC design-type applications.
seQuRe: an Integrated Dual Digital QR and Invisible Watermark P. Assiroj; B. Hartati; Sohirin Sohirin; B. Mulyawan; R. K. Astuti; I.A Prabadhi; C. Trinata; G.B Hertantyo; C. Susaningsih; M.F. Romdendine; O.P Martadireja
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/y7gew716

Abstract

This study introduces seQuRe, a novel approach that employs a dual-layer QR code system combined with invisible watermarking to enhance security measures in data transmission. The outer layer of the QR code facilitates general data accessibility while the inner layer, encrypted and embedded within the outer, secures sensitive information accessible only through specialized scanning. Utilizing advanced encryption standards (AES), this system ensures data integrity and confidentiality. The invisible watermarking further augments security by embedding additional data that verifies authenticity. Through systematic experimentation using Python and various libraries on Google Colaboratory, this experiment demonstrates the efficacy of seQuRe in resisting common cyber-attacks while maintaining data fidelity. We measured the peak-signal-to noise-ratio (PSNR) and the normalized cross-correlation (NCC) values of the QR images into which we had embedded watermarks, obtaining a PSNR value of 57.53 and an NCC value of 0.999. Subsequently, we also conducted simulation of attacks on the watermarked QR code with salt and pepper noise, speckle noise, and Gaussian noise attacks. From these attacks, we obtained PSNR values of 54.24 and NCC values of 0.6699 for the salt and pepper noise attack, 50.837 and 0.7319 for the speckle noise attack, and 33.17 and 0.0941 for the gaussian noise attack. The result underscores its potential application across industries requiring secure data handling and transmission. The implementation of such technology promises significant improvements in digital security, aiming to keep pace with the evolving landscape of cyber threats.
Cross-Domain Fake Reviews Identification Based on Deep Learning Neural Network With Rolling Collaborative Training Irham Aryandi Basir; Yuliant Sibaroni
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.v7i2.7944

Abstract

Identifying fake reviews in the current digital era has become an interesting study, especially in cross-domain context. This process is based on the limitations of the situation, where not all review domains have labels, and the labeling process takes a long time. In the context of machine learning, cross-domain learning involves learning from data and prediction processes, using data from different domains. Identifying fake reviews, several previous studies have conducted cross-domain, and the results of these studies indicate that there are still several issues in detecting fake reviews. The main problem with cross-domain methods is the difficulty of the model in understanding the differences in characteristics between domains, such as differences in language style, word structure in reviews, and the context present in the reviews. The main problem with the cross-domain method is the difficulty of the model in understanding the differences in characteristics between domains, such as differences in language style, word structure in reviews, and the context present in reviews. Based on these issues, this research adopts an approach to identify fake reviews using the Convolutional Neural Network and Bidirectional Long Short Term Memory models, utilizing the Multi Feature Rolling Collaborative Training (MRCT) algorithm with data from Yelp dan Amazon. The experimental results show that by conducting two scenarios,  Scenario-1 provides better performance with an accuracy of 98.59%, while scenario-2 is only capable of providing an accuracy performance of 79.64%. Additionally, by using multi-features, the model experienced a 24.96% improvement in detecting fake reviews across domains. Based on these results, it can be seen that the use of multi-features and rolling collaborative training with the CNN-BiLSTM model works effectively in identifying fake reviews across domains.
Image Recognition Using a Neural Network (Using Convolutional Neural Networks) Zena Fouad Rasheed; Raghdah A. Abdulrazzq; Mohammed Taher A. Mohammed; Sara Sadeq
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: 10.37385/389h1615

Abstract

An essential decision in constructing a neural network for any application is determining the appropriate representation of the data for presentation. Advancements in training techniques, such as changes to data augmentations and optimization methods, have greatly contributed to the notable progress made in the field of image classification research. Identifying and categorizing animals presents a substantial obstacle for researchers. The classification of animals consists of five main categories: mammals, amphibians, reptiles, fowls, and fish, each including a wide range of species. Therefore, we present an innovative method for recognizing and assessing classifications of vertebrate organisms by the use of deep Convolutional Neural Networks (CNN).  The main objective of this article is to improve an intelligent model based on CNNs for the precise classification of vertebrate animals using image data. Basically, the goal is to create an efficient system that can be applied in real-world scenarios, including environmental monitoring, automated biological research, and educational applications. This research focuses on developing an efficient approach for classifying vertebrate animals using a deep CNN. CNNs, inspired by the human brain’s structure, are powerful deep learning models eligible of processing large image datasets to achieve high precision in recognition tasks. The study utilizes CNN architectures trained on the Kaggle dataset to evaluate their performance in animal image classification. Through the application of real-time data augmentation and dropout techniques, the proposed models demonstrated exceptional precision, achieving an accuracy rate of 99.6%.
Enhancing Privacy in Real-Time Video Streams: Techniques, Challenges, and Benchmark datasets Powered by Deep Learning Emad I. Nyaz; Mohammed S.H. Al-Tamimi
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.v7i2.8200

Abstract

The exponential growth of video surveillance, live streaming platforms, and AI-driven analytics has created unprecedented threats to visual privacy. Traditional de-identification methods (pixelation, blurring) fail to balance privacy protection with contextual utility in dynamic environments. This systematic review of 30+ peer-reviewed studies uses a taxonomical framework to classify machine learning-based privacy preservation techniques into three domains: intervention methods (sensor saturation, broadcasting commands), obfuscation strategies (encryption, morphing, adaptive blurring), and secure processing pipelines.  We test convolutional neural networks (CNNs), YOLO-based object detection systems, and hybrid approaches including GAN-driven synthetic data substitution using public datasets (MARS, DukeMTMC, Market-1501). CNN-YOLO hybrid architectures provide 30+ FPS real-time performance with 92-98% detection accuracy, while GAN-based anonymization preserves visual usefulness better than traditional approaches.  Dataset scalability, illumination variability handling (accuracy drops 15-23% in low-light settings), occlusion resilience, and adversarial attack vulnerability remain key shortcomings.  Although promising, lightweight encryption approaches for edge devices cost 12-18% processing speed and lack defined privacy-utility trade-off measures. Implications: This work unifies computer vision, cryptography, and privacy engineering into a single taxonomy, showing that context-aware frameworks need multi-level security designs to manage varied threat scenarios.  Our findings help practitioners choose strategies depending on deployment restrictions (computational resources, latency, privacy regulations), yet 67% of reviewed methods lack real-world validation outside controlled datasets.This review uniquely synthesizes intervention, obfuscation, and secure processing research to provide uniform standards, context-adaptive privacy frameworks, and adversarially-robust de-identification systems.  Five key research directions—federated learning for distributed privacy, attention-mechanism-enhanced detection under occlusion, and explainable AI for privacy-utility optimization—will shape the next generation of ethical, scalable visual privacy solutions in pervasive video analytics.
YOLOv11-LCA: YOLOv11 Enhanced with the Low-Complexity Attention Mechanisms for a Robust Waterway-Floating Trash Detection Muhammad Rafly Arjasubrata; Mahmud Dwi Sulistiyo
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/dpqnng18

Abstract

Accurate trash detection in aquatic environments remains a significant challenge for detection models, which exhibit persistent limitations in identifying small and partially submerged objects. Additionally, a notable gap exists in methodologies for fine-tuning the detection model to optimize performance for a specific waterways. To address these limitations, the first objective is to develop a detection model designed to enhance performance on small and partially submerged trash, and the second is to establish a framework for efficiently adapting the model to achieve high accuracy within local waterways. First, the YOLOv11 architecture is enhanced by integrating LCAM and LCBHAM attention mechanisms and pre-trained on various combinations of public datasets to establish a robust, baseline model. For the second objective, this baseline model is adapted using a data-efficient framework. This study process introduces the BojongTrash dataset, captured from a specific waterway, and involves systematically fine-tuning the model on incremental subsets of this data to determine the minimum quantity of images and training epochs required to achieve high accuracy in the target environment. The proposed YOLOv11s-LCA architecture demonstrated a statistically validated improvement over its baseline, increasing the mAP50 score from 0.779 to 0.836 on the FloW-Img dataset with only a 0.1% parameter increase. Furthermore, the research establishes a highly efficient fine-tuning framework, demonstrating peak mAP50 performance of 0.908 that achieved by fine-tuning on 1,000 images for only 3-5 epochs. Therefore, this research validates lightweight attention mechanisms as an efficient strategy for enhancing detection in complex environments and provides a practical framework that enables the rapid deployment of tailored, high-accuracy monitoring systems.
Backpropagation Artificial Neural Network For Classification Arrhythmia in ECG Signals  Nurista Wahyu Kirana; Ervin Masita Dewi; Vanesha Putri Anggita; Yana Sudarsa; Dodi Budiman Margana; Sugondo Hadiyoso
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.v7i2.8413

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, accounting for approximately 17.9 million deaths annually. Among these, arrhythmias represent a significant concern due to their potential to lead to severe cardiac events. Traditional methods for detecting arrhythmias often require specialized equipment and healthcare facilities, which may not be readily accessible, especially in remote areas. This paper proposes the development of a portable electrocardiogram (ECG) device integrated with an Artificial Neural Network (ANN) using the Backpropagation algorithm to classify arrhythmias, thereby facilitating early detection and management. Arrhythmia is a heart condition characterized by an irregular heartbeat, where the heart may beat faster or slower than normal. Classification of arrhythmia can assist patients in monitoring their heart condition without needing to visit the hospital. This final project implements the Artificial Neural Network (ANN) method due to its ability to perform fast and accurate classifications. Prior to classification, feature extraction is carried out to detect the R wave interval, T wave interval, and the differences between the R and T wave intervals. The classification results are then displayed through a graphical user interface (GUI). The development of this ANN-based arrhythmia signal classification tool aims to help patients detect heart abnormalities at an early stage, potentially preventing the condition from worsening. Testing was conducted on 11 individuals, with 9 identified as having normal heart signals and 2 diagnosed with arrhythmia. When compared to a simulator, the classification system achieved 100% accuracy.
A Performance-Based Approach to Safety Management: Mapping Safety Indicators and Targets in Multi-Approval Training Organizations Dwi Lestary; Elfi Amir; Akbar Hidayatullah
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.v7i2.8518

Abstract

Safety Management Systems (SMS) are formally required across aviation training organizations; however, institutions holding multiple operational approvals frequently encounter difficulties in translating regulatory requirements into coherent and measurable safety performance indicators and targets. In practice, safety performance monitoring is often fragmented, approval-specific, and insufficiently aligned with a performance-based oversight philosophy. This study addresses this gap by examining how safety indicators and targets can be systematically mapped within a unified SMS framework for multi-approval aviation training organizations. This research adopted a qualitative design-based approach, combining document analysis, internal stakeholder interviews, comparative benchmarking with peer training organizations, and focus group discussions involving regulators, operators, and safety managers. Data triangulation was applied to ensure consistency and analytical validity throughout the framework development process. The results demonstrate that safety performance expectations differ substantially across approvals due to distinct operational risk characteristics. Nevertheless, these differences can be integrated through a common safety assurance structure without compromising regulatory specificity. The study identifies approval-specific safety performance indicators and targets for pilot training, aircraft maintenance, and maintenance training activities, and shows that an integrated performance-based mapping improves safety oversight, strengthens compliance mechanisms, and enhances organizational accountability. From a theoretical perspective, this study extends performance-based safety management literature by situating safety performance measurement within a multi-approval governance context. Practically, it offers aviation training organizations and regulators a structured and adaptable framework for harmonizing safety performance monitoring across approvals. The study contributes a transferable model that supports resilient and performance-oriented SMS implementation in complex aviation training environments.

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