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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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Development of Rammed Earth Material Technology by Utilizing Plastic Waste as Reinforcement on The Partition Walls of The Building Room Kinanti Wijaya; Sutrisno Sutrisno; Harun Sitompul; Nono Sebayang; Ruri Aditya Sari; Iswandi Idris
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.v6i2.5959

Abstract

In order to improve the compressive and bending strength of rammed earth materials for use in partition walls, this study investigates the incorporation of plastic trash. The goal of the research is to enhance the performance of sustainable construction materials while addressing the environmental problem of plastic waste. Using a Universal Testing Machine (UTM), compressive and bending strength tests were performed after 30 days for rammed earth mixtures containing four different amounts of plastic trash (0%, 1%, 3%, and 5%). According to the findings, adding plastic trash can increase compressive strength by up to 3%, reaching a maximum strength of 5.17 MPa. However, compressive and bending strength significantly decreased when the plastic percentage was increased over 3%, with the 5% plastic showing the worst performance. According to these results, plastic trash can enhance material performance, but its use requires careful optimization. By putting forth a novel technique for recycling plastic trash, the study supports sustainable building practices and provides a workable substitute for non-load-bearing applications such as partition walls. This study advances our understanding of green building technologies and offers workable ways to cut down on plastic waste in the building industry.
Integrating Fibonacci Retracement To Improve Accuracy of Time Series Prediction of Gold Prices Bagus Priambodo; Ruci Meiyanti; Samidi Samidi; Gushelmi Gushelmi; Rabiah Abdul Kadir; Azlina Ahmad
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.v6i2.6073

Abstract

The prediction of gold prices is crucial for investors and policymakers due to its significant impact on global financial markets. Machine learning and deep learning have been used for predicting gold prices on time series data. This study employs MLR, SVM and CNN LSTM with Fibonacci retracement levels to forecast gold prices based on time series data. The experiment results demonstrate that combining Fibonacci retracement with model prediction significantly enhances predictive performance compared to prediction without Fibonacci. The use of Fibonacci levels has resulted in a higher R² score and lower RMSE score showing that Fibonacci levels influence the accuracy of gold price predictions and strengthen the overall reliability of gold price forecasts. The findings underscore the potential of combining machine learning models with technical analysis tools in financial forecasting. Integrating the Fibonacci retracement level offers valuable insights for market participants, enabling more informed investment decisions and effective risk management strategies.
Naive Bayes Analysis for Nutritional Fulfillment Prediction in Children Satrio Agung Wicaksono; Satrio Hadi Wijoyo; Fatmawati Fatmawati; Tri Afirianto; Diva Kurnianingtyas; Mochammad Chandra Saputra
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.v6i2.6105

Abstract

Stunting in children remains a significant global health challenge, particularly in low- and middle-income countries. Addressing this issue requires an effective approach to predicting and preventing inadequate nutritional fulfillment. This study uses the Naïve Bayes approach to forecast nutritional needs for children's growth and development, providing practical information for stunting prevention efforts. The data used were sourced from 174 infant and toddler examinations at the Puskesmas Lawang, involving eight key attributes: gender, age, weight, height, head circumference, pre-screening, vision tests, and nutritional status. Key performance metrics were evaluated to validate the model's predictive capabilities, including accuracy, precision, recall, and F1-score. Six test scenarios were conducted using different percentages of training data (90%, 80%, 70%, 60%, 50%, and 40%) to evaluate the reliability of the Naïve Bayes method. Results indicated that the highest accuracy of 78.84% was achieved in the sixth test scenario. The third test scenario produced the highest precision at 97.5%, while the highest recall (100%) was observed in the first three scenarios. The highest F-measure of 90.3% occurred in the fourth scenario. These results suggest the algorithm's potential for early detection to decrease the number of stunting children. The study’s implications are twofold: practically, the model can be integrated into health monitoring systems to assist healthcare professionals and policymakers in designing more effective nutrition programs; theoretically, it highlights the adaptability of Naive Bayes for handling complex, multi-dimensional health data.
Liquefaction Potential Analysis Using Various Methods (Case Study of Railway Bridge in Sintuk Toboh Gadang District, Padang Pariaman Regency, West Sumatera) Didi Yoriadi; Andriani Andriani; Abdul Hakam
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.v6i2.6115

Abstract

The earthquake that rocked West Sumatra with a magnitude of 7.9 SR, a depth of 71 km, and an epicenter of 0.84 LS - 99.65 BT around 57 km Southwest of Pariaman on 30 September 2009 has caused damage to infrastructure and buildings and caused 383 fatalities. One of the problems caused by the earthquake is the liquefaction phenomenon. Liquefaction was reported to have occurred in Padang in the form of sand ejection coming out of cracks in the ground after the 7.9 SR earthquake in 2009. This study aims to determine the liquefaction potential of the Sintuk Toboh Gadang railway, Pariaman, using various liquefaction potential analysis methods so that the most practical and convincing method is obtained among these methods. In this study, the methods used to predict liquefaction are the Tsuchida (1970), Seed & Idriss (1971), Shibata & Teparaksa (1988), and Hakam (2020) methods. Field testing was conducted at four CPT test points, four NSPT test points, and machine drilling tests. The results showed that using the Tsuchida (1970) method, soil deposits at the four points tended to have liquefaction potential. The Seed & Idriss (1971) method showed that points 3, with depths of 8m and 14m, and point 4, with a depth of 8m, had liquefaction potential, while the Shibata & Teparaksa (1988) method using CPT data showed that at depths <10 meters there was a tendency for liquefaction to occur at the four points reviewed. The study's results using the Hakam (2020) method resemble the method proposed by Seed & Idriss (1971). It can be concluded that among the four methods, the most practical and convincing method is the Hakam (2020) method.
Modification of Multilayer Perceptron Using Detection Rate Model for Prediction of Nominal Exchange Rate Al-Khowarizmi Al-Khowarizmi; Romi Fadillah Rahmat; Michael J Watts; Akrim Akrim; Arif Ridho Lubis; Muhammad Basri
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.v6i2.6117

Abstract

An artificial neural network (ANN) is a network of a group of units to be processed which is modeled based on the behavior of human neural networks. ANN has one of its tasks, namely prediction. Multilayer perceptron (MLP) is one of the ANN methods that can be prediction all of data. Where the prediction needs to be reviewed because the prediction process does not always run normally. So, it takes a good measurement accuracy in order to get an accuracy sensitivity. The accuracy technique in this paper is carried out using Mean Absolute Percentage Error (MAPE) based on absolute error and detection rate. The results obtained with absolute error achieve an accuracy of 99.73% while the accuracy based on the detection rate achieves an accuracy of 99.49%. this can be seen in the case of the prediction of (Indonesian Rupiah) IDR exchange rate against United State Dollar (USD) with the MLP algorithm by testing using MAPE to achieve sensitivity with absolute error.
Weather-Baglog Parameters Monitoring System Based IoT-MQTT-Nodered For Mushroom Cultivation Room: A Precision Agriculture
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.v6i2.6138

Abstract

Mushroom cultivation methods are continually being refined to meet increasing demands for quantity and quality. However, frequent weather fluctuations often pose challenges. They can influence the optimal growth of mushrooms and the baglog's nutrient-chemical. This study aims to implement precision agriculture by developing a weather-baglog parameters monitoring system based on IoT-MQTT-Nodered technology. It seeks to analyze and evaluate the dominant parameters influencing ideal oyster mushroom cultivation room conditions using machine learning classification models and capability process analysis. Sample data was collected from an oyster mushroom cultivation room using a 24-hour monitoring system over seven days. The monitoring tool's system design comprises three parts: multi-sensor data acquisition, communication protocol to the server, and smartphone-based data monitoring. The results demonstrate the system's effectiveness, mobile-access, and durability in monitoring and acquiring weather-baglog parameters data. The best model shows that light, temperature, and humidity are the dominant parameters influencing the ideal oyster cultivation room. Capability process analysis reveals that the dominant parameters in the cultivation room are currently less than ideal.  The implications for improvement are needed an IoT-based control system to regulate them and make them ideal. This finding has been tested as an effective, mobile-access, durable, and data-centering monitoring system.
Secure E-Voting System Utilizing Fingerprint Authentication, AES-GCM Encryption and Hybrid Blind Watermarking Asia Abdullah; Nada Hussein M. Ali
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.v6i2.6223

Abstract

Ensuring security, integrity, and reliability of the election process consider as the main challenges in the electronic voting system. This paper describes the e-voting system by integrating the biometric authentication, advanced encryption, and watermarking techniques towards meeting such challenges. The system employs the fingerprint authentication by utilizing the Scale-Invariant Feature Transform (SIFT) for verifying the identity of the voter to ensure genuineness and non-repudiation of the service. The vote will be encrypted with the AES-GCM technique to be employed in securing the voting process, thus ensuring both data privacy and integrity. Hybrid Blind Watermarking employs the technique of Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for embedding the encrypted vote into the colored watermark cover image. Increased robustness against attacks with maintained vote confidentiality is achieved through this approach. The experimental results proved its imperceptibility to reach a Peak Signal-to-Noise Ratio (PSNR) of 40.7 and Normalized Correlation (NC) of 1. Thus, the proposed system enhances the theoretical foundation of secure e-voting and provides an implementation strategy for a reliable and tamper-proof voting system.
Design and Implementation of HMI For Monitoring The Imbalance of Current and Voltage Based on Calculation of Maximum Deviation Mean Value Method
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.v6i2.6270

Abstract

Voltage and current (V-I) imbalance in a three-phase power system can cause decreased efficiency, increased power losses, equipment heating, induction machine faults, and neutral currents. The main causes of this problem are uneven load distribution, phase failure, or network disturbances. Therefore, monitoring imbalance is critical to determine the right corrective steps. This study aims to design and implement a Human-Machine Interface (HMI) as a tool to monitor voltage and current imbalance using the Calculation Maximum Deviation Mean Value (CMDMV) method. This method calculates the maximum deviation of V-I from each phase to obtain an accurate imbalance value. Current and voltage sensors are used to collect real-time data, which are then processed using CMDMV in the HMI software. The results are displayed in the form of graphs, status indicators, and percentage figures, then compared with a power quality analyzer for accuracy validation. The results show that this HMI system can display V-I imbalance in real-time with a reading error rate when the imbalance condition is below 5%, and when it detects an imbalance in V-I, the indicator turns yellow (WARNING). With the creation of this device, it can help identify V-I imbalances in each phase.
A Heuristic Enhancing Artificial Immune System for Three-dimensional Loading Capacitated Vehicle Routing Problem Peeraya Thapatsuwan; Warattapop Thapatsuwan; Chaichana Kulworatit
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.v6i2.6295

Abstract

This study addresses the Three-Dimensional Loading Capacitated Vehicle Routing Problem (3L-CVRP), a highly complex NP-hard problem that combines vehicle routing with spatially constrained three-dimensional bin packing. To tackle this challenge, we propose an enhanced Artificial Immune System (En-AIS) that integrates a novel local search heuristic called “Bring-i-to-j,” designed to improve routing feasibility and loading efficiency. The En-AIS algorithm is further refined through rigorous parameter tuning using a full factorial design and ANOVA analysis. Comparative experiments were conducted against conventional AIS and the Firefly Algorithm (FA) across 27 benchmark instances. Results demonstrate that En-AIS consistently outperforms both baseline methods in terms of solution quality, achieving an average improvement of 15–20% while maintaining competitive computational times. These findings highlight the algorithm’s robustness and its practical potential for application in logistics and supply chain optimization tasks involving joint routing and loading decisions.
From Virtual to Reality: How Metaverse and VR Technologies Influence Travel Decisions Randi Rian Putra; Ika Devi Perwitasari; Dewi Mahrani Rangkuty; Virdyra Tasril; Sri Handayani; Adinda Silvana Dewi
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.v6i2.6314

Abstract

This study investigates the impact of Virtual Reality (VR) and the Metaverse on travel decisions, specifically focusing on lesser-known tourist destinations. The purpose is to understand how immersive digital experiences can influence potential visitors' perceptions and travel intentions. A mixed-methods approach, combining qualitative interviews with tourism stakeholders and quantitative surveys with 500 participants, was used to collect data. The results show that VR and Metaverse experiences significantly enhance user engagement, emotional attachment, and the likelihood of visiting the destination in person. The study's findings offer practical insights for tourism marketing strategies, suggesting that VR and Metaverse platforms can complement traditional marketing approaches. Theoretical implications include contributing to the understanding of digital transformation in tourism, particularly in how immersive technologies shape travel behavior. This research contributes to both theory and practice by highlighting the potential for VR and Metaverse technologies to increase the appeal of lesser-known destinations like Lake Toba.

Page 31 of 36 | Total Record : 358

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