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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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Countenance Evaluation of Virtual Reality (VR) Implementation in Machining Technology Courses Waskito Waskito; Rizky Ema Wulansari; Budi Syahri; Nelvi Erizon; Purwantono Purwantono; Yufrizal Yufrizal; Tze Kiong Tee
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (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.v4i2.1917

Abstract

This study aims to evaluate whether virtual reality (VR) learning media can be used in Machining Technology courses which are practical learning but implemented virtually. The research using the Stake Countenance evaluation method was carried out at the Department of Mechanical Engineering FT-UNP in the July-December 2021 semester with 60 students as research subjects. This study was mix method by using sequential explanatory design. which is the collection of quantitative and qualitative data that is carried out sequentially. Data related to the antecedents, transaction, and outcomes phases were collected using questionnaires, interviews, and observations. The research begins with developing VR media that is implemented to learning materials in the field of Machining Technology and then applied to learning. Then first stage is carried out using quantitative then the next stage or the second stage is carried out using qualitative. The result of research showed that this VR application can help students understand the theory of introducing machine tool operations but have not been able to run machine. This study imply that students’ learning process should be enjoyable and also influence existing practices of Student-Centered Learning. The novelty of this study showed the evaluation result of technology, especially virtual reality can be implemented in the practice learning course, it can be reference for educator to consider implementing technology in practice learning. This study will contribute to existing knowledge and various instructional method that can be implemented by educator
Favorite Book Prediction System Using Machine Learning Algorithms Dersin Daimari; Subhash Mondal; Bihung Brahma; Amitava Nag
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (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.v4i2.1925

Abstract

Recent years have seen the rapid deployment of Artificial Intelligence (AI) which allows systems to take intelligent decisions. AI breakthroughs could radically change modern libraries' operations. However, introducing AI in modern libraries is a challenging task. This research explores the potential for smart libraries to improve the caliber of user services through the use of machine learning (ML) techniques. The proposed work investigates machine learning methods such as Random Forest (RF) and boosting algorithms, including Light Gradient Boosting Machine (LGBM), Histogram-based gradient boosting (HGB), Extreme gradient boosting (XGB), CatBoost (CB), AdaBoost (AB), and Gradient Boosting (GB) for the task of identifying and classifying Favorite books and compares their performances. Comprehensive experiments performed on the publicly available dataset (Art Garfunkel's Library) show that the proposed model can effectively handle the task of identifying and classifying Favorite books. Experimental results show that LGBM has achieved outstanding performance with an accuracy rate of 94.9367% than Random Forest and other boosting ML algorithms. This empirical research work takes advantage of AI adoption in libraries using machine learning techniques. To the best of our knowledge, we are the first to develop an intelligent application for the modern library to automatically identify and classify Favorite books
A Critical Study on Group Key Management Protocols and Security Aspects For Non-Networks Rituraj Jain; Manish Varshney
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (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.v4i2.1947

Abstract

The rise in internet usage and advanced communication systems has led to an increase in security issues. The need for more robust and flexible secure communication has led to the introduction of mobile non-network multicast communication systems like MANET or VANET. Multicasting is increasingly being used for group-oriented applications such as video conferencing, interactive games, TV over Internet, e-learning, etc. To address the security concerns, this paper highlighted the confidentiality, authentication, and access control for non-network multicast communication systems like MANET or VANET.  For this, paper explores the group key management protocols. The paper concluded that centralized and asymmetric group key management protocol (GKMP) is most effective for designing secure, and efficient communication models for non-networks. The key findings of the paper are that in group key management protocols (GKMPs) for multicast communication systems adoption of asymmetric GKMPs provides better security, and reduces computational overhead. Therefore, this paper help to improve the robustness and security of multicast communication systems and meet the growing demands of group-oriented applications over the internet.
Classification of Multiple Emotions in Indonesian Text Using The K-Nearest Neighbor Method Ahmad Zamsuri; Sarjon Defit; Gunadi Widi Nurcahyo
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (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.v4i2.1964

Abstract

Emotions are expressions manifested by individuals in response to what they see or experience. In this study, emotions were examined through individuals' tweets regarding the election issues in Indonesia in 2024. The collected tweets were then labeled based on emotions using the emotion wheel, which consisted of six categories: joy, love, surprise, anger, fear, and sadness. After the labeling process, the next step involved weighting using TF-IDF (Term Frequency-Inverse Document Frequency) and Bag-of-Words (BoW) techniques. Subsequently, the model was evaluated using the K-Nearest Neighbor (KNN) algorithm with three different data splitting ratios: 80:20, 70:30, and 60:40. From the six labels used in the modeling process, the accuracy was then calculated, and the labels were subsequently merged into positive and negative categories. Then the modeling was conducted using the same process with the six labels. The results of this study revealed that the utilization of TF-IDF outperformed BoW. The highest accuracy was achieved with the 80:20 data splitting ratio, attaining 58% accuracy for the six-label classification and 79% accuracy for the two-label classification
The Cuckoo Optimization Algorithm Enhanced Visualization of Morphological Features of Diabetic Retinopathy Dafwen Toresa; Fana Wiza; Ahmad Ade Irwanda; Wenti Sasparita Abiyus; Edriyansyah Edriyansyah; Taslim Taslim
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (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.v4i2.1978

Abstract

This research compares strategies for identifying diabetic retinopathy (DR) using fundus image and discusses the efficiency of various image pre-processing techniques to enhance the quality of fundus images. Fundus images in medical image processing often suffer from non-uniform lighting, low contrast, and noise issues, which necessitate image pre-processing to enhance their quality. The study evaluates the effectiveness of several optimization techniques in selecting the best technique for identifying DR. One of the image pre-processing techniques compared in the study involves comparing negative images, dark contrast stretch, light contrast stretch, and partial contrast stretch, which are then evaluated using standard performance metrics such as NIQE, PNSR, MSE, and entropy. The results are further optimized using the Cuckoo Search Algorithm. The proposed technique produces better image quality improvements in several performance metrics, such as MSE, NIQE, PSNR, and entropy. Bright Contrast Stretch outperforms other techniques in NIQE Mean 5.2850, Entropy 5.0193, NIQE Standard deviation 0.2261, and Entropy 0.2612.
Classification Academic Data using Machine Learning for Decision Making Process Elin Haerani; Fadhilah Syafria; Fitra Lestari; Novriyanto Novriyanto; Ismail Marzuki
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (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.v4i2.1983

Abstract

One of the qualities of higher education is determined by the success rate of student learning. Assessment of student success rates is based on student graduation on time. Sultan Syarif Kasim State Islamic University Riau is one of the state universities in Riau, with a total of 30,000 students. Of all the active students, there are some who are not. Students who are not active in this case will affect the timeliness of their graduation. The university always evaluates the performance of its students to find out information related to the factors that cause students to become inactive so that they are more likely to drop out and what data affect students being able to graduate on time. The evaluation results are stored in an academic database so that the data can later be used as supporting data when making decisions by the university. This research used data science concepts to explore and extract data sets from databases to find models or patterns, as well as new insights that can be used as tools for decision-making. After the data was explored, machine learning concepts were used to identify and classify the data. The method used was the Decision Tree Method. The results of the study found that these two concepts can provide the expected results. Based on the test results, it is known that the attribute that influences the success of student studies is the grade point average (GPA), where the accuracy of the maximum recognition rate is 88.19%. Keywords : Data science; Decision Tree; Graduate on Time; Machine Learning;
Identifying Factors for The Success of Halal Management Practices in Leather Industry Tengku Nurainun; Hayati Habibah Abdul Talib; Khairur Rijal Jamaludin; Shari Mohd Yusof; Nilda Tri Putri; Fitra Lestari
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (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.v4i2.1989

Abstract

The need to apply halal management practices to non-food industries today is still merely seen as a necessity to meet the requirements of Islamic rules. Meanwhile, this approach has demonstrated that it can improve organizational efficacy in a variety of contexts. This study seeks to investigate the depth of halal principles implementation among leather industries and comes up with strategies for how small and medium-sized enterprises (SMEs) in the leather industry can use halal management practices to move toward halal certification and enhance its performance. An exploratory-descriptive approach was used to get the current state of halal practices among leather industry SMEs through interviews and survey questionnaires. Five stakeholders were interviewed in a semi-structured manner. A survey questionnaire was distributed to 127 SMEs in the leather industry center of Sukaregang, Garut, Indonesia. This paper discusses the key factors of halal implementation and determines which halal practices need more emphasis. The result showed that the current knowledge, awareness, and implementation of halal requirements among leather SMEs in Indonesia are still low. An action plan for the industry, authority, and supplier was provided.  The implication of this research can contribute to the leather industry players that intent to implement halal management system effectively and stakeholders in making decision to accelerate halal certification process.
Twitter Data Analysis and Text Normalization in Collecting Standard Word Arif Ridho Lubis; Mahyuddin K M Nasution
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (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.v4i2.1991

Abstract

is one of the most important data sources in social data analysis. However, the text contained on Twitter is often unstructured, resulting in difficulties in collecting standard words. Therefore, in this research, we analyze Twitter data and normalize text to produce standard words that can be used in social data analysis. The purpose of this research is to improve the quality of data collection on standard words on social media from Twitter and facilitate the analysis of social data that is more accurate and valid. The method used is natural language processing techniques using classification algorithms and text normalization techniques. The result of this study is a set of standard words that can be used for social data analysis with a total of 11430 words, then 4075 words with structural or formal words and 7355 informal words. Informal words are corrected by trusted sources to create a corpus of formal and informal words obtained from social media tweet data @fullSenyum. The contribution to this research is that the method developed can improve the quality of social data collection from Twitter by ensuring the words used are standard and accurate and the text normalization method used in this study can be used as a reference for text normalization in other social data, thus facilitating collection. and better-quality social data analysis. This research can assist researchers or practitioners in understanding natural language processing techniques and their application in social data analysis. This research is expected to assist in collecting social data more effectively and efficiently.
Smart_Eye: A Navigation and Obstacle Detection for Visually Impaired People through Smart App Bhasha Pydala; T. Pavan Kumar; K. Khaja Baseer
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (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.v4i2.2013

Abstract

Vision is extremely important in our lives. The loss of sight is a serious issue for anyone. According to the WHO, one-sixth of the world's population suffers from vision impairment. According to World Health Organization (WHO) statistics published in December 2021, more than 283 million people worldwide suffer from sight problems, including 39 million blind people and 228 million people with low vision. Navigation in unfamiliar environments is a significant challenge for the partially sighted and visually impaired. Improving visual information on object location and content can aid navigation in unfamiliar environments. Many efforts have been made over the years to develop various devices to assist the visually impaired and improve their quality of life. Numerous efforts have been made over the decades to develop gadgets to support the visually impaired as well as enhance the quality of their lives by trying to make them skilled. There are many existing navigation alternatives that can aid these people. However, in practice, navigation alternatives are infrequently adopted and implemented. For universal use, many of these gadgets are either too heavy or too expensive. While emphasizing related strengths and limitations, it is necessary to produce a minimally expensive assistive device for people with visual disabilities. The proposed model provides an efficient solution for VIPs to roam from place to place by themselves through smart applications with AI and sensor technology. The smart application captures and classifies the images. The obstacles are detected through ultrasonic sensors. The user can get a sense of the obstacles in the path through voice command. The proposed model is very helpful for the VIPs in terms of qualitative and quantitative performance measures. This enables a ranking of the evaluated systems according to their potential influence on Visually Impaired people's lives.
Financial Viability of Business Models For Engineered Vertical Hydroponics Systems For Sustainable Onion Production in The Philippines Kenneth L. Armas; Engr. Gina Lorenzo; Catherine Dela Cruz
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (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.v4i2.2040

Abstract

This study aimed to explore the financial, socio-economic, and environmental benefits of sustainable onion production using vertical farming and hydroponic systems, and to identify key factors affecting the viability of these business models. Data were collected through a survey of onion farmers and producers in the Philippines, and analyzed using descriptive and inferential statistics. Results showed that sustainable onion production using vertical farming and hydroponic systems has the potential to generate higher income for farmers, increase employment opportunities, improve food security, enhance market competitiveness, and promote environmental sustainability. Key financial factors affecting viability included production costs, market prices, yield, labor costs, energy costs, capital costs, financing costs, taxes and regulatory costs, and maintenance and repair costs. Recommendations for optimizing financial viability include reducing production costs, diversifying income streams, and improving market competitiveness. Overall, this study suggests that sustainable onion production using vertical farming and hydroponic systems is a viable and promising approach to achieving socio-economic and environmental sustainability in the agricultural sector.

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