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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi Jurnal Sistem Informasi, Manajemen, dan Akuntansi (SIMAK) METIK JURNAL Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer Green Intelligent Systems and Applications Jurnal Teknologi Sistem Informasi International Journal of Multidisciplinary Sciences and Arts MDP Student Conference "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika jurnal informasi teknologi dan sistem KOMPUTASI Global Science: Journal of Information Technology and Computer Science
Willay, Thommy
Universitas Widya Dharma Pontianak
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Design an Electricity Consumption Prediction Information System Using the Monte Carlo-Based Regression Tree Method Ng, Junira Merrylin; Hoendarto, Genrawan; Willay, Thommy
Green Intelligent Systems and Applications Volume 5 - Issue 2 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i2.910

Abstract

Electricity became an essential component in every industry and was widely used in organizations and households. Improper handling of electricity consumption resulted in unnecessary energy loss and increased costs. The objective of this study was to develop an online electricity consumption prediction information system that was efficient, reliable, and capable of rapid forecasting. The system used IoT sensor data from Universitas Widya Dharma Pontianak, and the Monte Carlo based Regression Tree (MCRT) method was employed to mitigate the unpredictability of the data. Feature selection was conducted using Monte Carlo simulation to identify the most important features, which in this case were the year, month, and day, and these were used in the regression tree model. The developed system was able to provide estimations of hourly and daily energy consumption and the associated costs based on the MCRT model. The MCRT model predicted daily energy consumption with an accuracy of 91.61%, outperforming the Monte Carlo simulation (85.39%) and the Regression Tree method (84.29%). The results demonstrated that the MCRT model was the most efficient in capturing non-linear relationships and regression patterns in the energy consumption data. The constructed system featured an easy-to-use web interface that captured real-time data inputs and visualized predicted consumption for operational use. The system was suitable for public and private sectors, as well as educational and household applications. This approach improved effectiveness in energy management and streamlined resource allocation decision-making. The study highlighted the potential of integrating the Internet of Things (IoT) with predictive analytics to provide actionable, reliable, and precise energy management and monitoring services.
A Hybrid Federated-Edge Learning Framework with Dynamic Model Pruning for Real-Time Anomaly Detection in Smart Manufacturing Networks Genrawan Hoendarto; Thommy Willay; Pavan Kumar
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 3 (2025): September: Global Science: Journal of Information Technology and Computer Scien
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i3.173

Abstract

The rapid advancement of intelligent systems has accelerated the adoption of data-driven solutions across diverse industries, creating an increasing need for models that are both efficient and privacy-preserving. While traditional centralized machine learning approaches offer strong predictive capabilities, they often struggle with challenges related to data privacy, network latency, and computational inefficiency-especially in distributed environments with heterogeneous devices. To address these limitations, recent research has explored hybrid learning frameworks that integrate federated learning, edge computing, and dynamic model optimization techniques. These hybrid approaches enable models to process and learn from data closer to the source while maintaining stringent privacy requirements by keeping raw data localized. Additionally, the incorporation of pruning strategies, adaptive model compression, or multimodal data fusion contributes to improved speed, scalability, and accuracy in real-time inference tasks. Such frameworks have demonstrated notable promise in settings characterized by high data volume, operational complexity, and the necessity for fast anomaly detection or decision-making. However, despite these advancements, several challenges remain, including synchronization delays across edge nodes, variability in hardware capabilities, and the need for more efficient aggregation algorithms. Future developments may involve leveraging next-generation pruning techniques, energy-aware edge scheduling, decentralized orchestration protocols, or the integration of digital twin technologies to further enhance performance. Overall, hybrid distributed learning frameworks represent an important evolution toward more intelligent, secure, and autonomous computational ecosystems capable of supporting the next wave of smart applications.
Co-Authors Antonius Antonius Brandon, Brandon Cangputri, Lyeslin Caturyadi, Fabianus Charlen, Charlen Christina Iil Damayanti Darmianto, Amok DIANA NOVIANTY Evanti, Julia Fernando, Darmadi Andrew Figolius Herik Genrawan Hoendarto Giovanna, Aurelyn Gultom, Manorang Haryanto Hendro Hendro, Hendro Iskandar, Riyadi J. Jimmy Iskandar, Riyadi Jimmy Tjen Kiong, Ritchie Konstan Dasit, Frederick Kristina Ng, Junira Merrylin Paskalia Kartini Pavan Kumar Riri Supriani Riyadi J. Iskandar Sigan, Agustinus Stephanie, Dea Sudjalmo, Sugito Tarigan, Jekkri Parlindungan Thannia, Yvonne Tony Darmanto Tony Darmanto Vania, Klara Veronica, Stefanny Gracya Wirawan, Cipto