Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal CYCLOTRON
Giovanni Dimas Prenata
Universitas 17 Agustus 1945, Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Analisis Perbandingan Kinerja Jaringan Saraf Tiruan (JST) yang Dioptimalkan Metaheuristik dan Regresi Vektor Pendukung untuk Prediksi Konsumsi Energi Listrik Giovanni Dimas Prenata
CYCLOTRON Vol 9 No 02 (2026): CYCLOTRON
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/ct.v9i02.30809

Abstract

This study analyzes the performance of electricity consumption prediction by comparing a metaheuristic optimization-based Artificial Neural Network (ANN) model from previous studies with a standalone Support Vector Regression (SVR) model. The SVR model uses an ε-insensitive linear regression approach with regularization, and feature normalization is performed to maintain training stability. Experimental results show that SVR is capable of producing very high prediction accuracy on training data with a relative accuracy per sample of 96.74%–99.90% and an average training accuracy of 99.09%, while maintaining generalization on test data with an accuracy of 92.56%. Compared to ANN with metaheuristic optimization (GA/PSO), which generally requires a more complex training process and relies on initialization, SVR offers the added value of more stable, deterministic, and easily reproducible training with lower model complexity. These findings confirm that SVR can be an effective and efficient alternative for electricity consumption prediction as well as a strong comparison to the ANN metaheuristic approach.
Co-Authors