Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi International Journal of Engineering, Science and Information Technology
Gavinda, Virza
Unknown Affiliation
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Predicting Electricity Consumption in Aceh Province Using the Markov Chain Monte Carlo Method Gavinda, Virza; Nurdin, Nurdin; Fajriana, Fajriana
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.678

Abstract

Electricity is essential to nearly every aspect of modern life, from industrial sectors to household needs. In Aceh Province, the demand for electricity has consistently increased along with economic growth, urbanization, and population expansion. Various studies indicate that rising electricity consumption is closely linked to economic growth and industrialization. This study uses the Markov Chain Monte Carlo (MCMC) method with the Metropolis-Hastings algorithm to predict electricity consumption in Aceh Province. The research addresses the significant increase in electricity consumption driven by economic growth and urbanization in the region. Electricity consumption data from January 2018 to December 2022 was utilized as the basis for modeling. The results indicate a 32.4% increase in electricity consumption over the past five years. The predictive model achieved high accuracy with a Mean Absolute Percentage Error (MAPE) of 2.41%, demonstrating its reliability in forecasting future electricity needs. Projections through 2030 show a continuous increase, reaching 482 GWh by the end of the period. These findings are expected to support decision-making in sustainable energy planning and providing adequate electricity infrastructure in Aceh. This study highlights the effectiveness of the Me-tropolis-Hastings algorithm in handling complex data with high variability, providing valuable insights for long-term energy planning
Implementation of Fuzzy Time Series Markov Chain Method to Predict Electricity Consumption in Aceh Province Gavinda, Virza; Nurdin, Nurdin; Fajriana, Fajriana
Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.4858

Abstract

The use of electrical energy is expected to always increase every year. This is due to the increasing needs of the community that must be met. Electricity has become the foundation for welfare and economic progress as well as a growth engine both domestically and globally, to meet the need for electrical energy in the future so that a system is needed that can predict future electrical energy consumption. Various prediction methods have developed along with the problems that arise. These methods include the Fuzzy Time Series Markov Chain method and the Markov Chain Monte Carlo method. This study aims to apply the Fuzzy Time Series Markov Chain (FTSMC) method to predict electricity consumption (KWH) in Aceh Province until 2030 using the electricity consumption dataset from 2018 to 2022. The FTSMC method combines fuzzy time series modeling with Markov chain state transitions, allowing for effective handling of uncertainty in time series data. The results reveal an impressive forecast accuracy, with a Mean Absolute Percentage Error (MAPE) of 3.2483%, demonstrating the model’s robustness and suitability for electricity consumption forecasting, with a predicted 314,606,308 kWh in January 2023 and 482,982,495 kWh in December 2030, representing an overall increase of 53.5% over the eight-year period. The FTSMC model effectively stabilizes predictions over time, ultimately converging to a stable value. This stability suggests that FTSMC is well-suited for forecasting in contexts where historical patterns are expected to persist. Further application of this model could benefit other sectors requiring accurate, stable forecasts.
Co-Authors Fajriana, Fajriana Nurdin Nurdin