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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. 1 (2024): Mei 2024" : 15 Documents clear
Implementation of K-Means Clustering in Mapping Teacher Distribution Using Geographic Information System Muttaqin, Khairul; Nurhidayah, Rif'ah; Novianda, Novianda; Ihsan, Ahmad; Sultan, Jumriani; Rifqiyah, Fina
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.v9i1.76884

Abstract

The placement of teachers in Indonesia has not been evenly distributed across several regions due to inaccurate recruitment and placement processes. The quality of education, particularly in rural areas, is negatively impacted by this uneven distribution. Teachers play a crucial role in enhancing education, making it essential to address this issue. This study seeks to equilibrate the allocation of teachers in Langsa City using the K-Means Clustering method based on the number of teachers, students, and study groups at the Madrasah Ibtidaiyah, Madrasah Tsanawiyah, and Madrasah Aliyah levels. The clustering results are then mapped using the Quantum Geographic Information System. The study identifies 20 schools with a shortage of teachers, 7 schools with sufficient teachers, and 3 schools with a surplus. The utilization of the K-Means Clustering method demonstrated a high accuracy rate of 92.8%. The implications of these findings suggest that educational authorities can use the clustering results to strategically address teacher shortages by reallocating teaching resources more effectively, thus potentially improving educational outcomes in underserved areas. Moreover, the GIS mapping offers a practical tool for ongoing monitoring and decision-making regarding teacher distribution.
Implementation of Black Box Testing with the Application of Equivalence Partitioning Techniques in the M-Magazine Android Application at Semen Gresik High School Frayudha, Angga Debby; Pande, Ivan Roy; Juwita, Mario Benson
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.v9i1.70382

Abstract

This research aimed to improve the M-Magazine application at Semen Gresik High School to enhance its usability for students and teachers. The study focused on developing a digital platform called "mading" to improve the dissemination of information within educational institutions. The research addressed security issues and limitations associated with physical media by transitioning from traditional wall-mounted magazines to a digital format. The research followed a three-phase approach consisting of preparation, analysis, and implementation, which involved a literature review, data collection, system design, and testing. The resulting digital Magazine system offers a centralized platform for schools to manage and share information. Students can actively contribute by submitting content through the website. The system provides features to sort, filter, and publish information, ensuring timely and relevant updates. The findings demonstrated that the Digital Magazine effectively improves information dissemination in schools by providing better security, accessibility, and efficiency than traditional methods. This technology-powered platform empowers schools and students to share knowledge and stay informed.
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.
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.
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.

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