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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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Strengthening the University-Maritime Industry Collaborations (UMICs): Technology Issues Ummu Ajirah Abdul Rauf; Nusra Izzaty Aziz; Nor Amirah Syairah Zulkarnaini; Mazzlida Mat Deli; Maryam Jamilah Asha’ari; ‘Ainul Huda Jamil; Siti Intan Nurdiana Wong Abdullah
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): 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.v5i1.3211

Abstract

In management practise and research, the university-maritime industry collaborations (UMICs) have grown in significance. This trend is reinforced by the necessity for innovation in the current industry environment and the desire of policymakers to commercialise knowledge from academia. Much less is known about these collaborations, although significant research efforts have been made to identify the success factors for these collaborations. Therefore, the aim of this study is to identify and explore the key factors that strengthen UMICs and propose a framework to enhance collaboration, so that a research agenda for the future will be developed based on an assessment of the existing literature. This study adopted a method of systematic literature review using published and unpublished theoretical literature to conduct analysis using five research databases in order to propose a framework aimed at identifying the key factors to strengthen UMICs. The findings of this study concluded that effective communication, trust, and adequate fund resources are essential for UMICs to succeed. Open communication channels, mutual trust, and shared vision can help build strong partnerships, while adequate funding can support research and development of new technologies, practices, and solutions. Based on previous research, none of them treated combined fund resources, effective communication, and trust as an independent variables towards UMICs relationship specifically. Hence, this study fills the gap by proposing a framework to test the relationship between fund resources, effective communication, and trust towards UMICs. Thus, the proposed framework can be used as a benchmark to strengthen UMICs in the future. This study also will encourage the managers in the maritime industry to drive innovation, establish strategic collaborations, actively involve stakeholders, and foster innovation and economic growth in the maritime industry to strengthen UMICs. The existing limited body of knowledge and literature will also benefit from this study.
Fetal Heart Disease Detection Via Deep Reg Network Based on Ultrasound Images S. Magesh; P.S. RajaKumar
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): 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.v5i1.3226

Abstract

Congenital heart disease (CHD) is the most prevalent congenital ailment. One in every four newborns born with serious coronary artery disease will require surgery or other early therapy. Early identification of CHD in the fetal heart, on the other hand, is more critical for diagnosis. Extracting information from ultrasound (US) images is a difficult and time-consuming job. Deep learning (Dl) CNNs have been frequently utilized in fetal echocardiography for CAD identification to overcome this difficulty. In this work, a DL based neural network is proposed for classifying the normal and abnormal fetal heart based on US images. A total of 363 pregnant women between the ages of 18 and 34 weeks who had coronary artery disease or fetal good hearts were included. These US images are pre-processed using SCRAB (scalable range based adaptive bilateral filter) for eliminating the noise artifacts. The relevant features are extracted from the US images and classify them into normal and CHD by using the deep Reg net network. The proposed model integrates the Reg net -module with the CNN architecture to diminish the computational complexity and, simultaneously, attains an effectual classification accuracy. The proposed network attains higher accuracy of 98.4% for the normal and 97.2% for CHD.  To confirm the efficiency of the proposed Reg net is compared to the various deep learning networks.
The Readiness of Minang Weaving Towards Halal Fashion Adoption: A Clustering Analysis of Toe Framework Ratni Prima Lita; Nilda Tri Putri; Meuthia Meuthia; Devi Yulia Rahmi
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): 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.v5i1.3242

Abstract

This research focuses on clustering the readiness of halal practice implementation in Minang weaving businesses, then based on the clusters that are very ready, it is then carried out the identification of the determinant factors of the readiness to implement the Halal Assurance System in Minang weaving businesses. Sampling was conducted using the non-probability sampling technique through purposive sampling from 103MSMEs of weaving in West Sumatra. The results of this research through K-means cluster analysis show that the grouping of Technology, Organization, Environment (TOE ) halal adoption in weaving MSMEs consists of 6 readiness groups, namely termination (15 MSMEs), maintenance (43 MSMEs), action (23 MSMEs), preparation (13 MSMEs), contemplation ( 2 MSMEs), pre-contemplation (7 MSMEs) spread across five cities/regencies in West Sumatra (Sawahlunto City and 50 Cities Regency which consists of 34 MSMEs respectively, 30 MSMEs from Tanah Datar Regency, 3 MSMEs from Sijunjung Regency, and 2 MSMEs from Payakumbuh City). Of the three determinants of TOE adoption tested, MSMEs' perceptions of technology readiness through the dimensions of compatibility and perceived benefits are higher than organizational and environmental factors. This research has theoretical and practical implications through exploration of the TOE model so that the results of this study can become a further research agenda in encouraging halal certification through developing a halal adoption strategy based on HAS 23000 in weaving MSMEs in West Sumatra.
Priority RPL for IOT Based Smart Manufacturing Industries Krishna Priya M; Angeline Prasanna G
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): 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.v5i1.3247

Abstract

A routing protocol used in heterogeneous transport networks for low-power, lossy networks. This is a routing protocol for wireless networks. This protocol follows the same specifications as Zigbee, 6 lopan is IEEE 802.15. 4 Enables both many-to-one and one-to-one communication. To address the need for enhancing in this study proposes a novel methodology called RPL-PG (Routing Protocol for Low-Power and Lossy Networks Priority Generation). Initially sensors like Temperature, Humidity, Vibration, Proximity, Gas and Current Monitoring Sensors are used for smart manufacturing. Consequently, Destination Oriented Directed Acyclic Graph (DODAG) is used for RPL configuration. Based on selected RPL configuration the priority is generated using assign priority count and priority-based queuing. Finally, Fuzzy rules are used to select the RPL path and then update the DODAG finally reached the destination. The study involves setting up a simulated environment using appropriate tools, such as MATLAB. Experimental findings evaluate and compares performance measures, such as Energy Consumption, Network Life Time, Packet Loss Ratio, Packet Delivery Ratio (PDR), E2E Delay, and Network Throughput. The Energy Consumption of the proposed RPL-PG method achieves 43.6 % lower than 38 % and 35.8 % in terms of OMC-RPL and RMA-RP respectively.
A Modified KLEIN Encryption-based Knight Tour for Image Encryption Emaan Oudha Oraby; Rafeef Mohammed Hamza
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): 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.v5i1.3296

Abstract

The security considerations should be balanced with the specific use case and potential risks associated with using lightweight encryption. The security offered by lighter encryption algorithms could not be as high as that offered by heavier encryption techniques. In this paper, a Modified KLEIN Algorithm is proposed for image encryption based on the Knight Tour movement in Chessboard. The required key generation is represented by inputting an image as a key image and then applying a specific operation based on knight tour movement to produce a key scheduled in the proposed encryption algorithm. The movement of Knight Tour applied in modifying the proposed algorithm for increasing security. The experimental results explain the efficiency of a modified algorithm when comparing the histogram of the input image with the encrypted image also the correlation is tested before and after encryption in three directions horizontal, vertical, and diagonal which explains there are very low values of them in all directions. The similarity test also explains there are high differences between the plain and encrypted images. The chessboard movement might be useful when used with another encryption algorithm which increases the confidentiality of transferring data. The contribution of this work is the use of an image as a key for encryption with a specific planning method which helps in key management.
Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence Arathi Chandran R I; V Mary Amala Bai
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): 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.v5i1.3384

Abstract

With more than 2.1 million new cases of diagnosis each year, breast cancer is considered to be the most prevalent women disease. Within 10 years, nearly 30% patients who got cured at early-stages experienced cancer recurrence. Recurrence is a crucial aspect of breast cancer behaviour that is inseparably linked to mortality. Despite its importance, the significant proportion of breast cancer datasets rarely include it, which makes research into its prediction more challenging. It is still difficult to predict who will experience a recurrence and who won't, which has implications for the treatment that goes along with it. Clinicians treating breast cancer may be able to avoid ineffective overtreatment if Artificial Intelligence (AI) methods are developed that can forecast the likelihood of breast cancer recurrence. This work proposes a novel automatic breast cancer recurrence classification and prediction system incorporating novel Deep Convolutional Neural Network (DCNN) algorithm. The proposed DCNN model is deployed on Wisconsin Breast Cancer dataset for further evaluation. The role of AI in forecasting recurrence is examined in this work. The experimental results were analysed for various combination of train and validation dataset. The accuracy, precision, recall and F1-score for the proposed DCNN was calculated as 97.63 %, 98.57 %, 96.84 %, 97.89 % respectively.
Depend Ability Analysis on CPS Using Machine Learning Techniques Krishna Narayanan S; Dhanasekaran S; Vasudevan V
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): 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.v5i1.3411

Abstract

A Cyber-Physical System (CPS) is an entity that effortlessly monitors and controls physical operations by integrating computational and physical elements. Dependability on the CPS application program and also proposes a real-time analysis approach to CPS application dependability based upon Artificial Intelligence and Machine Learning (ML). For starters, pick complicated networks to determine tips within the system topology on the CPS application process. Unsupervised mastering category by a quick density clustering algorithm to classify the value of nodes could be successfully put on the crucial analysis of nodes in CPS application program as well as help support the setting up of CPS software phone Secondly, a real-time CPS dependability automated internet analysis technique is suggested. Unreliable methods are able to imply big losses, each monetarily in addition to within man's life. On a good mention, CPS has information like the main component of the operation of theirs. The prevalence and availability of information demonstrate a brand-new chance to change the methods within what dependability evaluation continues to be usually performed. This process utilizes printer mastering tips to create an analysis framework, design, and style an internet queuing algorithm, as well as put into action real-time internet evaluation and analysis of CPS dependability. Preventive steps make sure that the device works ordinarily as well as with no interruption, which significantly betters method dependability. Last but not least, simulation final results verify the usefulness of the analysis technique and the broad application prospects of its.
The Role of Artificial Intelligence in Diagnosing Heart Disease in Humans: A Review Tamara Hameed Yousiaf; Mohammed S. H. Al-Tamimi
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): 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.v5i1.3413

Abstract

The electrical activity of the heart and the electrocardiogram (ECG) signal are fundamentally related. In the study that has been published, the ECG signal has been examined and used for a number of applications. The monitoring of heart rate and the analysis of heart rhythm patterns, the detection and diagnosis of cardiac diseases, the identification of emotional states, and the use of biometric identification methods are a few examples of applications in the field. Several various phases may be involved in the analysis of electrocardiogram (ECG) data, depending on the type of study being done. Preprocessing, feature extraction, feature selection, feature modification, and classification are frequently included in these stages. Every stage must be finished in order for the analysis to go smoothly. Additionally, accurate success measures and the creation of an acceptable ECG signal database are prerequisites for the analysis of electrocardiogram (ECG) signals. Identification and diagnosis of various cardiac illnesses depend heavily on the ECG segmentation and feature extraction procedure. Electrocardiogram (ECG) signals are frequently obtained for a variety of purposes, including the diagnosis of cardiovascular conditions, the identification of arrhythmias, the provision of physiological feedback, the detection of sleep apnea, routine patient monitoring, the prediction of sudden cardiac arrest, and the creation of systems for identifying vital signs, emotional states, and physical activities. The ECG has been widely used for the diagnosis and prognosis of a variety of heart diseases. Currently, a range of cardiac diseases can be accurately identified by computerized automated reports, which can then generate an automated report. This academic paper aims to provide an overview of the most important problems associated with using deep learning and machine learning to diagnose diseases based on electrocardiography, as well as a review of research on these techniques and methods and a discussion of the major data sets used by researchers.
Analysing The Causes of Management and Production Delays in The Implementation of Construction Project Work Putri Lynna Luthan; Nathanael Sitanggang; Syahreza Alvan; Wisnu Prayogo
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 2 (2024): 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.v5i2.1994

Abstract

This study aims to analyse the causes of delays in the implementation of construction projects caused by management (owners/consultants) and production (contractors). The research sample consists of 56 respondents who are directly involved in the implementation of construction projects (owners, consultants, contractors, field supervisors, and estimators). The quantitative data analysis technique used was descriptive analysis technique, while the qualitative data obtained by interviewing 5 construction project experts was analysed by qualitative descriptive method. To analyse the causes of delays in project implementation, an analysis technique using the Relative Importance Index (RII) formula was used with a reference value of RII> 0.710. The results showed that 1) the average RII on management factors was 0.895> 0.710. This means that management factors (owner/consultant) can cause delays in the implementation of construction projects; 2) the average RII on production factors is 0.917> 0.710. This means that production factors (contractors) can cause delays in the implementation of construction projects. This means that production factors (contractors) can cause delays in the implementation of construction projects. This research is directly useful for construction service providers and contributes to the development of Project / Construction Management science.
Shear Strength Characteristics of Calcium Oxide and Guar Gum Treated Loose Petobo Silty Sand Yohanes Albrecht Montol; Aswin Lim; Paulus Pramono Rahardjo
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 2 (2024): 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.v5i2.2583

Abstract

This article presents the shear strength characteristics of Petobo Silty Sand which are treated with Calcium Oxide and Guar Gum. The purpose of this experimental works is looking for other binding agents to replace the application of cement which is considered not an environmentally friendly material. The shear strength of treated soils was examined using the direct shear test. Guar gum and Calcium Oxide provides additional cohesion to Petobo silty sand. The cohesion and internal friction angle could increase to about 900 kPa and 47.5°, respectively. The treated sample also shows the dilation behavior in dry conditions. However, after 24 hours soaking period, the soil behavior returned to the contraction behavior. This behavior is unfavorable in the case of the treated sample below the groundwater table. Hence, these two binding agents are effective for dry soil conditions. In addition, Scanning Electron Microscope images of treated silty sand were obtained which aims to examine the microscopic behavior of the fibers and matrices that were formed through the hydration process.

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