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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 3 Documents
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Search results for , issue "Vol. 6 No. 2 (2021): November 2021" : 3 Documents clear
Development and Validation of a TPACK Instrument for Preservice Teachers in the Faculty of Engineering UNY Destiana, Bonita; Priyanto, Priyanto; Walipranoto, Ponco; Irfan, Rahmatul
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 6 No. 2 (2021): November 2021
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (435.205 KB) | DOI: 10.21831/elinvo.v6i2.44301

Abstract

It is crucial to assess teachers' competency using TPACK. Therefore, the objective of this study was to develop a TPACK instrument for preservice teachers in the Faculty of Engineering, Universitas Negeri Yogyakarta. This study designed the TPACK instrument through 4 stages, namely: (1) literature study to determine the construct and statement items, (2) expert judgment to meet content validity, (3) revision and refinement of items from the review results, (4 ) validity and reliability testing. The research sample consisted of 200 preservice teachers. Validity and reliability testing used Confirmatory Factor Analysis (CFA) with a SmartPLS software program. The results suggested that the instrument met convergent validity with a loading factor value > 0.4, which ranged from 0.802 to 0.932, and discriminant validity, which indicated that the factor loading value for each observed variable with each latent variable was higher than the factor loading value with other latent variables. Composite Reliability values ranged from 0.908 to 0.954, and Cronbach Alpha ranged from 0.867 to 0.936, indicating that the instrument was reliable. Thus this instrument was considered effective in measuring the TPACK of preservice teachers preparing to be productive teachers in vocational high schools.
Power Monitoring and Passenger Classification on Logistics Elevator Hafidz, Isa; Putra, Aldhitiansyah; Montolalu, Billy; Adiputra, Dimas; Putranto, Rifky Dwi; Daffaldi, Rafly; Ulfa, Dinda Karisma
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 6 No. 2 (2021): November 2021
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (525.938 KB) | DOI: 10.21831/elinvo.v6i2.43689

Abstract

The elevator has an important role in assisting transportation and logistics activities in a building. However, if the elevator is not used wisely, then the power consumption will be inefficient. A policy of elevator usage is necessary to ensure the effectiveness of elevator power consumption. Therefore, in this study, elevator power consumption monitoring is proposed. The power consumption behavior can be learned so a suitable policy can be made accordingly. Two elevators in Telkom Campus Surabaya are monitored to understand the daily electrical energy usage. Internet of Things (IoT) based real-time power monitoring system is used to monitor the electrical energy usage of the elevator. A raspberry pi is used to collect the data of electrical usage via a current and power sensor. The data is sent to the cloud, which later is displayed through a dashboard website. The result shows that the elevator usage on weekdays and weekends is different. The peak power on weekdays is obtained from 15.00 to 16.00, meanwhile, on weekends, the peak occurs from 9:00 to 10:00. On weekdays, the total power consumed by the elevator is 51.74kW, while on weekends, it is 11.94kW. Restrictions on the use of lifts are applied to periods when the lift has few passengers and has a short distance. From the results obtained, the total power consumed can decrease by an average of 37%. It is expected that the suggested policies can reduce elevator power consumption and the monthly cost of electrical energy.
A Comparison of K-Means and Agglomerative Clustering for Users Segmentation based on Question Answerer Reputation in Brainly Platform Cahyo, Puji Winar; Sudarmana, Landung
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 6 No. 2 (2021): November 2021
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (269.454 KB) | DOI: 10.21831/elinvo.v6i2.44486

Abstract

Brainly is a question and answer (Q&A) site that students can use as a media for questions and answers. Students can also use Brainly to find and share educational information that helps students solve their homework problems. In Brainly, users can answer questions according to their interests. However, it could be that the interest is not necessarily following the competencies possessed. It causes many answers to the questions given not to have a high rating because the answers given are of low quality to be prioritized as the main answer. This study aims to apply the K-Means and Agglomerative Clustering methods to segment users based on the reputation of the answerers by conducting clustering based on track records in answering questions on mathematics subjects. This study used the number of the brightest scores and the number of answers that did not get a rating as the basic features for clustering. The comparison between the two methods used is based on the Silhouette Score, representing the quality of the clustering results, calculated by applying the Silhouette Coefficient method. This study result indicates that the K-Means method gives better results than the Agglomerative Clustering. The Silhouette Score generated by the K-Means method is higher at 0.9081 than the Agglomerative Clustering method, which is 0.8990, which produces two clusters or two segments.

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