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INDONESIA
ELINVO (Electronics, Informatics, and Vocational Education)
Published by Universitas Negeri Yogyakarta
ISSN : 25806424     EISSN : 24772399     DOI : 10.21831
Core Subject : Science, Education, Engineering,
Computer Science & IT Education Electrical & Electronics Engineering
ELINVO (Electronics, Informatics and Vocational Education) is a peer-reviewed journal that publishes high-quality scientific articles in Indonesian language or English in the form of research results (the main priority) and or review studies in the field of electronics and informatics both in terms of their technological and educational development.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 15 Documents
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Search results for , issue "Vol. 9 No. 2 (2024): November 2024" : 15 Documents clear
A Local-level App vs A National-level App: Evaluation of M-Grocery Apps From The UX and The Service Performance Perspective Asyari, Hasyim; Ulya, Devy Alfianur Fathu; Muhammad, Katon; Prakoso, Indro; Al Hakim, Reza Azizul Nasa
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 2 (2024): November 2024
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v9i2.77602

Abstract

In Indonesia, the increase in internet users, reaching 215.63 million in 2022-2023, has facilitated the growth of mobile shopping applications like Sayurbox and Beceran, which cater to daily needs such as groceries and fresh food items. Despite receiving high user ratings, these applications face technical issues that could impact user satisfaction and company revenue. This study aims to evaluate the service quality and user experience of the Sayurbox and Beceran applications using the M-S-QUAL and UX Benchmarking methods. The M-S-QUAL method assesses service quality across nine dimensions, while UX Benchmarking evaluates user satisfaction, ease of use, error counts, success rates, and task completion times. The results of UX Benchmarking reveal that Sayurbox has fewer errors and higher user satisfaction compared to Beceran, which has faster loading times but more errors. The M-S-QUAL analysis indicates that both applications have negative gap values in most service attributes, with Sayurbox performing slightly better than Beceran. Sayurbox received a score of -0.25 for the value gap on the M-S-QUAL scale, while Beceran received a score of -0.41 for the value gap.
Implementation of 48V/350W BLDC Motor Speed Control With PID Method Using Microcontroller-Based Sensorless Techniques Maula, Nurizka Fitra; Ilman, Sofyan Muhammad; Yahya, Sofian
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 2 (2024): November 2024
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v9i2.77124

Abstract

A brushless direct current (BLDC) motor is widely used in automotive and industrial applications due to its low noise and high performance. However, traditional BLDC motor control relies on Hall-effect sensors, which increase costs, enlarge motor dimensions, and risk errors from sensor failures. This research focuses on implementing a sensorless control system for a 350W, 48V BLDC motor. The goal is to achieve stable operation at a set speed of 250 rpm, with a steady-state error ≤3%, under varying loads from 0 Nm to 2.7 Nm. Using the Ziegler-Nichols PID tuning method, the study was conducted in the Electrical Machinery Laboratory at Bandung State Polytechnic. The results show that the sensorless control system effectively maintains setpoint speeds of 90 rpm, 120 rpm, 200 rpm, and 250 rpm. At 250 rpm, the system achieved an average steady-state error of 2.44% using PID parameters Kp = 3.13, Ki = 8.69, and Kd = 0.25. The motor's output power ranged from 136.88W at minimum load to 297.92W at maximum load, demonstrating improved efficiency and system performance
A Benchmark Study of Protein Embeddings in Sequence-Based Classification Simanjuntak, Humasak Tommy Argo; Siahaan, Lamsihar; Margaretha, Patricia Dian; Manurung, Ruth Christine; Purba, Susi; Lumbantoruan, Rosni; Barus, Arlinta; Gonzales, Helen Grace B.
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 2 (2024): November 2024
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v9i2.77389

Abstract

Proteins play a vital role in various tissue and organ activities and play a key role in cell structure and function. Humans can produce thousands of proteins, each consisting of tens or hundreds of interconnected amino acids. The sequence of amino acids determines the protein's 3D structure and conformational dynamics, which in turn affects its biological function. Understanding protein function is very important, especially for biological processes at the molecular level. However, extracting or studying features from protein sequences that can predict protein function is still challenging: it takes a long time, is an expensive process, and has yet to be maximized in accuracy, resulting in a large gap between protein sequence and function. Protein embedding is essential in function protein prediction using a deep learning model. Therefore, this study benchmarks three protein embedding models, ProtBert, T5, and ESM-2, as a part of function protein prediction using the LSTM Model. We delve into protein embedding performance and how to leverage it to find optimal embeddings for a given use case. We experimented with the CAFA-5 dataset to see the optimal embedding model in protein function prediction. Experiment results show that ESM-2 outperforms from ProtBert and T5. On training, the accuracy of ESM-2 is above 0.99, almost the same as T5, but still above ProtBert. Furthermore, testing on five samples of protein sequence shows that ESM2 has an average hit rate of 93.33% (100% for four samples and 66.67% for one sample).
The Implementation of Project-Based Learning (PBL) with ADDIE Model to Improve Students' Creative Thinking Ability Wahyudin, Wahyudin; Qobus, Muhammad Shofwan; Fatimah, Nusuki Syariati; Riza, Lala Septem; Adedokun-Shittu, Nafisat Afolake
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 2 (2024): November 2024
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v9i2.77240

Abstract

Creative thinking ability is one of the thinking concepts used to find ideas that people are starting to be interested in. Creative thinking can be used as a relevant tool in building innovation and as a method for building innovation models, one of which is a learning model. The project-based learning model is a solution that influences students' activeness and creativity in learning. The purpose of this research is to apply a project-based learning model that is expected to improve students' creative thinking abilities on creative product and entrepreneurship subjects on the Internet of Things material. The development model used in this research is ADDIE (Analyze, Design, Development, Implementation, Evaluation) with a One Group Pretest-Posttest research design. Based on the research results, there are several conclusions, including the following: 1) Students' creative thinking abilities by implementing the project-based learning model can be seen from the average pretest score of 38.24 and the average posttest score of 70.15. 2) The normalized gain test results obtained a mean of 0.517 with the "Medium" criteria, which means there is a difference in creative thinking abilities after the treatment process. There are four aspects given when giving the TAM questionnaire to students, namely the user's perception of usefulness with a percentage of 86.67%, the user's perception of ease of use with a percentage of 84.71%, attitude towards use with a percentage of 83.53 and attention. With a percentage of 86.27%, and the average obtained for the four aspects was 85.29% in the "Very Good" category.
Analyzing the Impact of Academic and Financial Factors on the Employment Prosperity of Engineering Graduates: A Case Study from Universitas Negeri Yogyakarta Indrihapsari, Yuniar; Luthfi, Muhammad Irfan; Ardy, Satya Adhiyaksa; Shittu, Abdul Jaleel Kehinde
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 2 (2024): November 2024
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v9i2.77111

Abstract

The rapid technological advancements and evolving job markets present a pressing need to understand how academic experiences shape the career outcomes of engineering graduates. This understanding is crucial for educational institutions aiming to align their curricula with industry demands and for graduates seeking to maximize their career prospects. Notably, the role of financial support, academic performance, and early career experiences in influencing graduate prosperity remains underexplored. This study aims to analyze the correlation between finance support, GPA, study period, job waiting times, salary details, and the prosperity of graduates from the Faculty of Engineering at Universitas Negeri Yogyakarta. The prosperity of graduates is defined as earning wages equal to or exceeding the Indonesian minimum average wage. Using data from a tracer study questionnaire, the research employed logistic regression and correlation analysis to investigate these relationships. The data underwent several stages of filtering, resulting in a refined dataset of 70 records for analysis. This study used SPSS software for statistical analysis, focusing on descriptive statistics, correlation, and logistic regression models. The results highlighted significant predictors of graduate prosperity, including GPA and types of financial support, while illustrating the limited predictive power of early career experiences on long-term earnings. The study also indicated that extended study periods do not necessarily correlate with higher wages. In conclusion, the study underscores the importance of targeted educational strategies and student support systems that are responsive to the dynamics of the job market, enhancing the readiness and prosperity of engineering graduates.

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