eprintid: 20465
rev_number: 3
eprint_status: archive
userid: 1
dir: disk0/00/02/04/65
datestamp: 2026-01-29 06:29:18
lastmod: 2026-01-29 06:29:18
status_changed: 2026-01-29 06:29:18
type: article
metadata_visibility: show
creators_name: Desmira, 
creators_name: Bakar, Norazhar Abu
creators_name: Wiryadinata, Romi
creators_name: Hamid, Mustofa Abi
creators_name: Kholifah, Nur
creators_name: Nurtanto, Muhammad
title: Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance
ispublished: pub
note: Cited by: 3; All Open Access, Gold Open Access
abstract: The energy use that is in excess of practicum students’ needs and the disturbed comfort that the practicum students experience when conducting practicums in the Electrical eengineering vocational education (EEVE) laboratory. The main objective in this study was to figure out how to predict and streamline the use of electrical energy in the EEVE laboratory. The model used to achieve this research’s goal was called the adaptive neuro-fuzzy inference system (ANFIS) model, which was coupled with principal component analysis (PCA) feature selection. The use of PCA in data grouping performance aims to improve the performance of the ANFIS model when predicting energy needs in accordance with the standards set by the campus while still taking students’ confidence in conducting practicum activities during campus operating hours into consideration. After some experiments and tests, very good results were obtained in the training: R=1 in training; minimum RMSE=0.011900; epoch of 100 per iteration; and R=0.37522. In conclusion, the ANFIS model coupled with PCA feature selection was excellent at predicting energy needs in the laboratory while the comfort of the students during practicums in the room remained within consideration. © 2022 Institute of Advanced Engineering and Science. All rights reserved.
date: 2022
publisher: Institute of Advanced Engineering and Science
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135441616&doi=10.11591%2fijeecs.v27.i2.pp970-979&partnerID=40&md5=87197ccd96c5b3a60131a138a03cd895
id_number: 10.11591/ijeecs.v27.i2.pp970-979
full_text_status: none
publication: Indonesian Journal of Electrical Engineering and Computer Science
volume: 27
number: 2
pagerange: 970 – 979
refereed: TRUE
issn: 25024752
citation:   Desmira and Bakar, Norazhar Abu and Wiryadinata, Romi and Hamid, Mustofa Abi and Kholifah, Nur and Nurtanto, Muhammad  (2022) Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance.  Indonesian Journal of Electrical Engineering and Computer Science, 27 (2).  970 – 979.  ISSN 25024752