eprintid: 20473
rev_number: 3
eprint_status: archive
userid: 1
dir: disk0/00/02/04/73
datestamp: 2026-01-29 06:28:29
lastmod: 2026-01-29 06:28:29
status_changed: 2026-01-29 06:28:29
type: article
metadata_visibility: show
creators_name: Desmira, 
creators_name: Bakar, Norazhar Abu
creators_name: Hashim, Mohd Ruzaini
creators_name: Wiryadinata, Romi
creators_name: Hamid, Mustofa Abi
title: Laboratory prediction energy control system based on artificial intelligence network
ispublished: pub
note: Cited by: 5; All Open Access, Gold Open Access
abstract: The use of electrical energy increases globally every year. The laboratory prediction energy control system (LPECS) predicted energy demand. This research was conducted in the Electrical Engineering Vocational Education laboratory by comparing the artificial neural fuzzy system (ANFIS) with the fuzzy logic. The comparison of methods aimed to determine their reliability in the energy demand prediction systems. The results showed that the minimum value of the target data using the conventional method (actual data) was 44.42. Meanwhile, the prediction data using the ANFIS method was 44.33, and the prediction data using the fuzzy method was 59.31. The maximum value of the conventional ways (actual data) of ANFIS and fuzzy was similar by 77.59. The RMSE ANFIS value was 0.1355, the mean absolute percentage error (MAPE) was 0.2791, and the fuzzy logic was 0.1986. Thus, the ANFIS is applicable to determine the minimum and maximum values. Meanwhile, fuzzy can only show the maximum value but cannot reach the minimum value properly. © 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-s2.0-85135208386&doi=10.11591%2feei.v11i3.1821&partnerID=40&md5=07ad1a048b7d9defe055f069441222d0
id_number: 10.11591/eei.v11i3.1821
full_text_status: none
publication: Bulletin of Electrical Engineering and Informatics
volume: 11
number: 3
pagerange: 1280 – 1288
refereed: TRUE
issn: 20893191
citation:   Desmira and Bakar, Norazhar Abu and Hashim, Mohd Ruzaini and Wiryadinata, Romi and Hamid, Mustofa Abi  (2022) Laboratory prediction energy control system based on artificial intelligence network.  Bulletin of Electrical Engineering and Informatics, 11 (3).  1280 – 1288.  ISSN 20893191