Article Per Year (5 Year)
p-Index From 2019 - 2024
1.037
P-Index
This Author published in this journals
All Journal Teknika Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science International Journal of Research in Vocational Studies (IJRVOCAS)
Pratama, Destra Andika
Unknown Affiliation
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Building of Informatics, Technology and Science

 1 
Application of Deep Learning LSTM in Online Power Prediction on Three-Phase Power Transformer Pratama, Destra Andika; Nabila, Sinta
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2690

Abstract

Electrical energy plays an important role in daily life, especially companies. The 3 Phase Power Transformer is one of the important electrical components that is very influential in distributing electrical energy to companies as is the case in PT. Semen Baturaja (Persero). 3-phase power transformers require attention because they are one of the components that are prone to interference, this interference can hinder the effectiveness of using electrical energy as a company support for employees to work. As one of the disturbances for 3-phase power transformers is overload or excessive power usage, overload can raise the temperature at the winding and reduce its service life. Artificial intelligence can be one of the keys to predict the use of power transformers in the future, especially deep learning by utilizing the LSTM algorithm. Optimal power prediction requires a lot of maximum input variables so that in this study, it not only adds an offline learning mode but adds a learning mode that can directly access the company's Power Quality Monitoring (PQM) website online with an average accuracy value of 86.62%.
Co-Authors Ade Silvia Handayani Aditya, M Rizky Vira Alfarizal, Niksen Anisah, Masayu Faisal Damsi, Faisal Irdayanti, Yeni Marlianda, Septi Adila Meranda, Arganda Nabila, Sinta Nyayu Latifah Husni, Nyayu Latifah Zarkasih, M Sandy