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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 239 Documents
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Design of Automatic Security System Based Internet of Things at the Museum Yuwana, Rahmat Ageng; Fajaryati, Nuryake; Jie, Ferry
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.v9i2.77707

Abstract

This research aims to determine the procedures for developing an Internet of Things (IoT)-based Automatic Security System as a security device for museum objects and to test its feasibility based on user feedback in museums. The Design Thinking model used consists of five stages: Empathize (understanding the user's perspective), Define (gathering information on the encountered problems), Ideate (generating innovative ideas), Prototype (creating a prototype of the obtained ideas, ensuring the functionality of each device feature, and validating the product through expert judgment for feedback and product revision), and Testing (evaluating the system's feasibility using Usability standards based on ISO/IEC 9241-11, focusing on Effectiveness, Efficiency, and Satisfaction with user involvement). The research results indicate that the IoT-based Automatic Security System has met optimal outcomes based on Usability aspects, namely: the Effectiveness analysis showed a ratio above 100% with a Very Effective level of achievement; the Efficiency aspect analysis received user feedback with a device usage rate of 1.35 goals per second; and the Satisfaction analysis, using the System Usability Scale (SUS) questionnaire, scored 78.25, indicating an Excellent level, suggesting that the system is worthy of use.
Multi-Objective Optimization of MEMS-based Box Pattern Microheaters Using Response Surface Method Setyawati, Onny; Choiron, Moch. Agus; Bangert, Axel; Sandhagen, Carl
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.v9i2.77133

Abstract

We present a response surface method to evaluate multi-objective optimization for MEMS-based microheater design. Box pattern, the standard microheater shape, was selected in this study since it has a uniform temperature distribution compared to other patterns. The optimum parameters are used to obtain the maximum total current density and Joule heat.  Based on a hybrid of the Response Surface Method and Central Composited Design, the model simulation emerged with 25 sets of Design Experiments.  As expected, the voltage is proportional to the increased output temperature and Joule heat of the microheater. Material thermal conductivity, anchor length and thickness of the heating element are included as design variables for the optimization. The microheater thicknesses of 4.23 - 4.55 µm, length of 40 µm and thermal conductivity of materials of approximately between 52 to 66 Wm-1K-1 became the optimized results at 1 V input voltage to obtain a maximum Joule heat of 4.9x105 W/mm3 and total current density of 5.6x107 mA/mm2.
Revolutionizing Ethnographic Collection Introduction through Augmented Reality Technology in Museum Mewengkang, Alfrina; Sumual, Herry; Teruna, Ilyas
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 1 (2024): Mei 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.v9i1.66191

Abstract

The North Sulawesi Provincial State Museum is one of the tourist destinations that offers a profound experience in exploring the historical, cultural, and tourism heritage of Indonesia. Visitors can learn about various valuable historical collections, including ethnographic collections, which hold significant historical value. However, the information provided about the ethnographic collection is very limited, making it challenging for visitors to understand and appreciate it. This research aims to develop an application for recognizing the museum's ethnographic collection using Augmented Reality (AR) technology, which will make it easier for visitors to obtain information about the collection. This application was designed and developed using the Multimedia Development Life Cycle (MDLC) method, which consists of six stages: conceptualization, design, material gathering, assembly, testing, and distribution. The result of this research is an Android-based AR Museum application that can display information about the ethnographic collection in the form of text, sound, and 3D images. This application can enhance the visitor experience, especially in understanding and appreciating the cultural heritage and traditions represented in the ethnographic collection of the North Sulawesi Provincial State Museum. Additionally, this application can improve the accessibility of information about the ethnographic collection and promote the museum as a center for heritage preservation, education, and culture.
Development of Smart Building Training Kit for Control System Competencies in Vocational High Schools Pujirianto, Pujirianto; Haryanto, Haryanto
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.v9i2.77160

Abstract

The unavailability, lack of updates and inappropriateness of learning media results in low-quality learning outcomes. The right learning media is an important part of the learning process, especially practicum learning in Vocational High School (VHS). The objectives of the research are to obtain: (1) Products in the form of smart building training kits as learning media equipped with Supervisory Control and Data Acquisition (SCADA) system based on Industrial Internet of Things (IIoT) for VHS in learning control systems; (2) The feasibility level of the training kit; (3) The effectiveness of the use of training kits. Development research refers to the ADDIE model developed by Branch. The study results are as follows: (1) The product in the form of a training kit consists of a component training kit and a simulator equipped with a job sheet and a manual. (2) The training kit is considered very feasible. The expert assessment of the feasibility of the product is 95.5% (very feasible category). The teacher's user response gave a rating of 95.5% (very feasible category). The user response by graduates gave an assessment of 90.8% (very feasible category). (3) The use of training kits has significant effectiveness on students' learning outcomes based on the results of the Paired Sample T-test and Independent Sample T-test with a sig value (2-tailed) (0.000) < 0.05. It can be concluded that a smart building training kit is an appropriate, very feasible, and effective learning medium for use in learning control systems.
A Bibliometric Analysis of Green Skills Research in Vocational Education: 2018-2022 Fitriyanto, Muhammad Noor; Wagiran, Wagiran; Zannah, Fathul; Novian, Dian; Mansur, Hamsi
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 1 (2024): Mei 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.v9i1.71890

Abstract

Green skills (GSs) have become one of the important strategies for achieving sustainable development. It can make the environment effective for social and economic development. The aim of this research is to reveal data on the evolution and development of green skills and trends in the development of green jobs throughout the world. This research uses R Studio and Bibliometrix to analyze 198 papers related to green skills in green jobs published from 2018 to 2022 in the Scopus database using Bibliometrix and visualization mapping methods. The results show a substantial increase in the number of studies GSs in recent years, with the focus area in European and American countries leading this research. Aspects of Will, manufacturing and Energy, and Journals of Green Environmental Management are the first three journals cited in the study of GSs.  Studying the cited literature together on environmentally friendly skills includes, among other things, the relationship between other skills and ecosystems with human good health, green construction, evaluation and green management of environmentally friendly competencies, and analysis of specific aspects of environmentally friendly skills. The results of the analysis of green skills grouping keywords show that there is research that concentrates on green skills in the fields of education, ecosystem services, climate change, and biodiversity protection. In conclusion, this research provides a reference for future studies of green skills necessary for the development of a sustainable educational environment, such as a multidisciplinary approach, adaptation of new technologies, partnerships with sustainable technology green industries, and environmental awareness.
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.
Optimizing Bitcoin Price Prediction with LSTM: A Comprehensive Study on Feature Engineering and the April 2024 Halving Impact Purnama, Panji Satria Taqwa Putra
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 1 (2024): Mei 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.v9i1.72518

Abstract

This research aims to develop a Bitcoin price prediction model using machine learning techniques, with a specific focus on Long Short-Term Memory (LSTM) neural networks. The Bitcoin market is characterized by unique features such as high volatility and the influence of various external factors, which differ significantly from traditional financial markets. As such, precise feature engineering is crucial for accurately modelling Bitcoin prices. Utilizing historical Bitcoin price data from 2014 to 2023, this study extensively evaluates LSTM models. The results indicate that LSTM models provide highly accurate predictions, with a Mean Squared Error (MSE) of 0.0001798 and a Mean Absolute Error (MAE) of 0.0101322. These results demonstrate that LSTM effectively captures the complex and dynamic patterns of Bitcoin prices, outperforming other methods. The findings have significant implications for financial market analysis, especially within the rapidly evolving domain of crypto assets. By leveraging machine learning methodologies, this research enhances understanding of the complexities of the crypto market and offers potential strategies for smarter investment decisions. The success of the LSTM model in improving Bitcoin price prediction accuracy underscores its importance in navigating the volatile and dynamic nature of the crypto market. Overall, this study highlights the substantial potential of machine learning approaches, particularly LSTM models, in analyzing and predicting crypto market behavior. It contributes to the growing academic discourse on the application of advanced technologies in finance and can stimulate further discussions on how machine learning can address challenges and opportunities in the crypto market.
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.

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