Article Per Year (5 Year)
p-Index From 2020 - 2025
1.745
P-Index
This Author published in this journals
All Journal Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Sistemasi: Jurnal Sistem Informasi Jurnal Pengabdian Masyarakat AbdiMas Building of Informatics, Technology and Science TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Mobile and Forensics JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Intechno Journal : Information Technology Journal JURNAL MULTIDISIPLIN BHATARA
JOKO DWI SANTOSO
Teknik Informatika STMIK AMIKOM Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy 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 Education Electrical & Electronics Engineering Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Nursing

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Pemodelan Prediktif Konsumsi Energi Listrik di Pabrik Baja Berbasis XGBoost untuk Pengelolaan Sumber Daya Berkelanjutan Utama, Hastari; Santoso, Joko Dwi
BHATARA: Jurnal Multidisiplin Ilmu Vol 2 No 3 (2025): October (In progress issue)
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/jmb.v2i3.234

Abstract

This research aims to develop a predictive model for electrical energy consumption in steel plants based on XGBoost Regressor, with the goal of supporting sustainable resource management. The dataset used was obtained from the UCI Machine Learning Repository with DOI: 10.24432/C52G8C , covering real-time operational data from a steel plant during 2018, including variables such as energy consumption (Usage_kWh), reactive power, power factor, and production load status. The research process included data exploration, preprocessing, time and categorical feature extraction, and training the XGBoost Regressor model with hyperparameter optimization using Grid Search and time-series split validation. Evaluation results showed outstanding performance with an MAE of only 0.41 kWh, RMSE of 0.81 kWh, and an R² value of 0.9993, indicating that the model successfully explained nearly all variations in actual data. Feature importance analysis revealed that Lagging_Current_Reactive.Power_kVarh and CO2(tCO2) were the most influential features in predicting energy consumption. This model is not only technically accurate but also holds significant practical potential for use in industrial energy management systems, helping companies plan production schedules, avoid peak loads, and improve energy efficiency sustainably.
Co-Authors Achmad Fauzi Ali Mustopa, Ali Ardiansyah, Ipung Ardika, Kadek Febry Aresta, Redho Maland Azmi, Bela Fataya Devi Wulandari Geraldino, Vicky Lokita, Raden Arditya Mutwara Mulyatun, Sri Nauvaldhi , Muhammad Sachib Farhan Pratomo, Ero Wahyu Putra, Andriyan Dwi Putro, Miko Kastomo Sekarningrum, Anggun Anindya Seniwati, Erni Setiawanto, Irwan Sholeha, Eka Wahyu TONNY HIDAYAT Utama, Hastari Vinanda, Rufki Ade