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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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Trends in E-Commerce And Social Media Research in Asia: Five Years of Scientometric and Content Analysis Hilmi Aulawi; Novie Susanti Suseno; Khairul Hafezad 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.v5i1.2420

Abstract

This paper aims to provide scientometric and content analysis towards e-commerce and social media research in Asia. The Web of Science (WoS) and Scopus databases were used in searching for articles. There were 884 (433 publications from the Web of Science and 451 articles from Scopus) papers analysed. Based on the analysis of two databases, the number of publications from the Web of Science database showed a significant increase yearly. In comparison, the Scopus database showed fluctuating growth every year. One of the countries that enormously contributed to the research was China, which can be seen from the author’s and country’s analyses. The ACM International Conference Proceeding Series was the most contributing conference proceedings. Based on the keyword results, there are five keywords that appear most often. Referring to the data from the last two years (2021–2022), the keywords “machine learning” and “social media marketing” are the most frequently used. These two keywords are most often associated with e-commerce and social media keywords. These findings are expected to provide a substantial understanding towards e-commerce and social media research, particularly in the Asian region. This paper will assist researchers in understanding new topics, collaborating with other researchers, and determining relevant sources and countries. Analysed keywords can inspire new research. Consequently, researchers can learn about new technology, societal changes, and impending challenges and opportunities by tracking keyword trends.
Enhancing Onion Supply Chain Using The Smart Contract Platform: A Meta-Analysis Kenneth L. Armas; Bren C. Bondoc; Rhea Lyn La Penia
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.v5i1.2447

Abstract

In the ever-evolving landscape of global agricultural supply chains, ensuring traceability, transparency, and sustainability is paramount to guaranteeing food safety, combating fraud, and meeting consumer demands. The Philippine onion industry, a vital component of the nation's horticultural sector, grapples with challenges related to traceability and transparency that impact customer trust and economic sustainability. While the adoption of smart contract platforms has revolutionized traceability and transparency in various agricultural sectors worldwide, their potential in the Philippine onion market remains underutilized. This study employs a comprehensive meta-analysis approach to evaluate the existing traceability and transparency mechanisms within the Philippine onion industry, drawing insights from a diverse set of studies. The meta-analysis reveals a consistently positive impact of these mechanisms on traceability and transparency. The findings, supported by a range of studies, underscore the value of these mechanisms in improving product quality, supply chain efficiency, and transparency. The study further investigates the potential impact of smart contract platforms in enhancing traceability and transparency throughout the onion industry's supply chain. Meta-analysis results suggest that the adoption of smart contract platforms holds promise in furthering these objectives. Through automated record-keeping and real-time data sharing, smart contracts have the potential to address existing challenges related to data fragmentation and limited technological integration. Identifying barriers to smart contract platform adoption in the context of traceability and transparency, the study proposes a set of strategic initiatives and recommendations. These recommendations cater to various stakeholders, including government bodies, academic institutions, local authorities, onion farmers, and industry players, aiming to promote the widespread adoption of smart contract platforms. This study extends beyond the confines of the Philippine onion industry, offering valuable insights into the role of smart contract platforms in enhancing traceability, transparency, and sustainability within agricultural supply chains. As the world works towards achieving the Sustainable Development Goals of the United Nations, this research contributes to the realization of "Zero Hunger" and "Responsible Consumption and Production" by promoting transparent and sustainable supply chains. By bridging the gap in understanding the potential of smart contract platforms in enhancing traceability and transparency, this study paves the way for innovative solutions, inspiring trust, and fostering sustainable farming practices within the onion industry and, potentially, in similar sectors worldwide.
Improving Text Summarization Quality by Combining T5-Based Models and Convolutional Seq2Seq Models Arif Ridho Lubis; Habibi Ramdani Safitri; Irvan Irvan; Muharman Lubis; Al-Khowarizmi Al-Khowarizmi
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.v5i1.2503

Abstract

In the natural language processing field, there are several sub-fields that are very closely related to information retrieval, such as the automatic text summarization sub-field. obtained from the convolutional T5 and Seq2Seq models in summarizing text on hugging faces found features that can affect text summary such as upper- and lower-case letters which have an impact on changing the understanding of the text of the document. This study uses a combination of parameters such as layer dimensions, learning rate, batch size, and the use of Dropout to avoid model overfitting. The results can be seen by evaluating metrics using ROUGE. This study produces a value of ROUGE-1 on 4 documents that are tested which produces an average of 0.8 which is the optimal value, for ROUGE-2 on 4 documents that are tested which results in an average of 0.83 which is an optimal value while ROUGE-L on 4 documents conducted tests that produce an average of 0.8 which is the optimal value for the summary model.
The Impulse Buying of Gen Z When Using E-Wallet In Indonesia Lim Sanny; Gilang Rafi Chandra; Kitsy Chelles; Laurent Angelica Santoso
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.v5i1.2600

Abstract

This research aims to analyze the impulse buying of Gen Z when using e-wallets in Indonesia. This type of research is quantitative research using the Partial Least Squares Structural Equation Modeling (SEM) framework. The sampling technique involved convenience sampling with a total of 393 Gen Z e-wallet users in Indonesia who had been surveyed online. Theoretical implications in this study are implementing the S-O-R framework on e-wallet enriches. This research provides a new perspective using Generation Z as a research subject. The results found in this study revealed that the model could explain 60.2% of variance satisfaction and 5.9% of impulse buying. In addition, the factors that encourage satisfaction include perceived interactivity, perceived risk, and subjective norms that significantly affect satisfaction with a small effect. Perceived usefulness is the most significant factor with a substantial impact that positively influences satisfaction. This satisfaction is proven to control impulse buying positively, but it can only be explained in a small part. The research's practical implication is that these results can provide input for e-wallet development companies to satisfy Gen Z using e-wallets in impulse buying.
Examining the Practicality of Mobile-Based Gamification Assessment in Electrical Machine Course: A Study in Industrial Electrical Engineering Doni Tri Putra Yanto; Ganefri Ganefri; Sukardi Sukardi; Rozalita Kurani; Jelpapo Putra Yanto
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.v5i1.2803

Abstract

Mobile-based gamification learning is increasingly popular for enhancing student interest and motivation in the learning process, including its application in evaluating learning outcomes. However, the practicality of its use, from the perspective of students as users, needs further evaluation. This study aims to assess the practicality of mobile-based gamification assessment (M-BGA) in evaluating student learning outcomes in the Electrical Machine Course (EMC). M-BGA was developed using Kahoot! application. A survey-based quantitative research design was employed, using the Practicality Assessment Instrument (PAI) as the data collection tool. The practicality of M-BGA was evaluated based on student assessments after its implementation in an EMC. This research involved 83 second-year students from the Industrial Electrical Engineering Study Program, Faculty of Engineering, Universitas Negeri Padang, Indonesia. The results indicate a high level of practicality in several aspects. The Ease of Use aspect scored 92.23% (highly practical), the Reliability aspect scored 89.82% (highly practical), the Student Engagement aspect scored 88.55% (highly practical), and the Learning Impact aspect scored 90.19% (highly useful). Overall, based on student responses, the M-BGA proved to be highly practical in evaluating student learning outcomes in the EMC. M-BGA can serve as an alternative approach for assessing student learning outcomes with an innovative approach.
Comparison Between Face and Gait Human Recognition Using Enhanced Convolutional Neural Network Fatima Esmail Sadeq; Ziyad Tariq Mustafa Al-Ta'i
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.v5i1.2806

Abstract

Identifying people at distance is an important task in daily life Because of the increase in terrorism. Biometrics is a better solution to overcome personal identity problems, and this applies to soft biometrics also. Soft biometric are features that can be extracted remotely and do not require cooperation with people. This paper introduces a comparison between human face recognition and human gait recognition using soft biometric features. Nine face attributes and nine gait attributes are taken from a dataset built by researchers. The constructed dataset is composed from (66) videos for (33) persons. Features are extracted using Haar and MediaPipe methods. The extracted features are classified using enhanced convolutional neural network. This work achieves an accuracy of 95.832% in human face recognition and an accuracy of 89.583% in human gait recognition. From the above results it turns out that the proposed method achieved promising results with regard to Recognize people remotely
A Conceptual Aquila Merged Arithmetic Optimization (AIAO) Integrated Auto-Encoder Based Long Short Term Memory (AUE-LSTM) For Sentiment Analysis Sangeetha J; Maria Anu 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.v5i1.2825

Abstract

Sentiment analysis is a branch of analysis that uses disorganized written language to infer the opinions and emotions of people's critiques and attitudes toward entities and its features. In order to produce acceptable results, the majority of sentiment analysis models that employ supervised learning algorithms require a large amount of labeled information during the training stage. This is typically costly and results in significant labor expenses when used in practical applications. In this study, an intelligent and unique sentiment prediction system is developed for accurately classifying the positive, negative, and neutral comments from the social media dataset. Data preprocessing, which entails noise reduction, tokenization, standardization, normalization, stop word removal, and stemming, is done to ensure that the data is of a high enough quality for efficient sentiment prediction and analysis. The preprocessed data is then used to extract a mix of features, including hash tagging, Bag of Words (BoW), and Parts of Speech (PoS). Consequently, in order to choose the best features and speed up the classifier, a new hybrid optimization method called Aquila merged Arithmetic Optimization (AIAO) is used. Furthermore, an Auto-Encoder based Long Short Term Memory (AuE-LSTM), an innovative and clever ensemble learning technique, is used to precisely anticipate and classify user feelings based on the chosen data. This study uses a variety of open source social media datasets to evaluate the performance of the suggested AIAO integrated AuE-LSTM model.
SQL Injection Detection Using RNN Deep Learning Model Abdulbasit ALAzzawi
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.v5i1.2864

Abstract

SQL injection attacks are a common type of cyber-attack that exploit vulnerabilities in web applications to access databases through malicious SQL queries. These attacks pose a serious threat to the security and integrity of web applications and their data. The existing methods for detecting SQL injection attacks are based on predefined rules that can be easily circumvented by sophisticated attackers. Therefore, there is a need for a more robust and effective method for detecting SQL injection attacks. In this research, we propose a novel method for detecting SQL injection attacks using recurrent neural networks (RNN), which are a type of deep learning model that can capture the syntax and semantic features of SQL queries. We train an RNN model on a dataset of benign and malicious SQL queries, and use it to classify queries as either benign or malicious. We evaluate our method on a benchmark dataset and compare it with the existing rule-based methods. Our experimental results show that our method achieved high accuracy and outperformed the rule-based methods for detecting SQL injection attacks. Our research contributes to the field of web application security by providing a new and effective solution for protecting web applications from SQL injection attacks using deep learning. Our method has both practical and theoretical implications, as it can be easily integrated into existing web application security frameworks to provide an additional layer of protection against SQL injection attacks, and it can also advance the understanding of how deep learning models can be applied to natural language processing tasks such as SQL query analysis.
Intensive Malware Detection Approach based on Data Mining Israa Ezzat Salem; Karim Hashim Al-Saedi
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.v5i1.2865

Abstract

Malicious software, sometimes known as malware, is software designed to harm a computer, network, or any of the connected resources. Without the user's knowledge, malware can spread throughout their computer system. Malware is typically disseminated via online connections and mobile devices. While malware has always been a problem in the digital age, its effects have gotten increasingly serious. Traditional malware detection methods seek to locate specific malware samples and families to recognize harmful codes and can be located using traditional signature- and rule-based detection methods. The research focuses on developing malware detectors using data mining techniques. The proposed method outlined below sets itself apart by emphasizing the processing of malware behaviors significantly dependent on aspects. Finding more dependable intelligent detecting techniques is a crucial component of this paper. In order to identify the cluster of the most essential malware features and use decision tree classifiers for malware detection, the study, a common methodology for creating malware detectors based on data mining, is implemented and investigated. Our approach can identify the most significant features of malware that can significantly determine and detect a malware code.
Fuzzy Genetic Particle Swarm Optimization Convolution Neural Network Based On Oral Cancer Identification System R Dharani; S. Revathy; K. Danesh
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.v5i1.2874

Abstract

Oral cancer is the eighth most common type of cancer in the world. Every year, 130,000 people in India die from mouth cancer. Getting a diagnosis from a clinical exam by skilled doctors and a biopsy takes time. When a problem is found early, it is always easier to treat. The primary goal of this work is to recognise disease-affected oral regions in a given oral image and classify the oral cancer disorder. This study employs unique Deep Learning algorithms to detect the location of disease-affected oral areas. This work employs the most effective feature extraction techniques, including appearance and patter-based features. Following feature extraction, the Bee Pulse Couple Neural Network (BeePCNN) algorithm is used to choose the best feature. Finally, Deep Learning is used to classify these attributes. An innovative FGPSOCNN reduces the computational complexity of CNN. On an additional real-time data set from Arthi Scan Hospital, a secondary evaluation is conducted. The experimental results indicate that the innovative FGPSOCNN performs better than existing methods.  

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