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All Journal Jurnal Pendidikan Teknologi dan Kejuruan ELKHA : Jurnal Teknik Elektro Jurnal Pengembangan Energi Nuklir TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Semnastek ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Syntax Literate: Jurnal Ilmiah Indonesia Energi & Kelistrikan Jurnal Teknik Elektro dan Komputasi (ELKOM) International Journal of Computer and Information System (IJCIS) CITIZEN: Jurnal Ilmiah Mulitidisiplin Indonesia Andalas Journal of Electrical and Electronic Engineering Technology Journal of Advanced Technology and Multidiscipline (JATM) International Journal of Engineering Continuity Journal of Business, Social and Technology Eduvest - Journal of Universal Studies Leuser Journal of Environmental Studies International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Iwa Garniwa
Department Of Electrical Engineering, Faculty Of Engineering, Universitas Indonesia, Depok 16424, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Neuroscience Social Sciences Other

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Machine Learning-Based Model Predictive Control for Energy Efficiency Optimization in Vertical Roller Mill Cement Grinding Elman Rudolf Pakpahan; Iwa Garniwa
Journal of Business, Social and Technology Vol. 7 No. 3 (2026): Journal of Business, Social and Technology
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jbt.v7i3.704

Abstract

Background: Vertical Roller Mill (VRM) is the newest type of equipment in the cement milling process, which consists of grinding, drying and separation processes that have high energy efficiency. Objective: This research was conducted to create and develop a Model Predictive Control (MPC) Random Forest Regressor (RFR) in a process system that aims to improve the performance of the cement grinding process, where currently process control is still carried out using a conventional control system by humans/operators. Methods: Model creation is carried out by preparing input variable data, manipulated and output variables, data conditioning, statistical analysis, model development, validation, testing, and evaluation. Results: The MPC-RFR model achieved R²=0.99936, MAE=2.488, MSE=122.354, with SEC reduced from 35.47 to 29.46 kWh/ton (16.94% reduction) using MPC-RFR, and further to 27.47 kWh/ton (22.55% reduction) with SLSQP optimization, yielding potential annual savings of IDR 8.6–11.5 billion. Conclusion: The MPC-RFR-SLSQP approach achieved 22.55% SEC reduction in VRM cement grinding, demonstrating significant potential for industrial energy efficiency and production cost optimization in the cement sector.
Analysis of the Effect of an IoT Monitoring System on the Rate of Voltage Decline in 18650 Li-Ion Batteries Using Deep-Sleep and Non-Deep-Sleep Strategies Fathur Dwipa Syaveyenda; Iwa Garniwa
Eduvest - Journal of Universal Studies Vol. 6 No. 6 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i6.53251

Abstract

IoT-based monitoring systems often rely on batteries as their main power source; however, continuous data acquisition and transmission can accelerate battery voltage drop, thereby reducing operational lifespan. This research analyzes the effect of an Internet of Things (IoT)-based monitoring system on the voltage drop rate of 18650 Li-ion batteries, and compares the characteristics of voltage drop in deep-sleep and non-deep-sleep operating modes using a comparative quantitative experimental approach. The ESP32-based monitoring system was tested under three operating conditions: baseline (without a monitoring system), deep-sleep, and non-deep-sleep. Battery voltage measurements were carried out periodically over a predetermined observation duration. The results show that the IoT monitoring system affects the characteristics of battery voltage drop, and that different device operating modes result in different voltage drop rates. The non-deep-sleep condition exhibits the fastest voltage drop, while the deep-sleep strategy is able to reduce the rate of voltage drop more effectively than continuous operation. These findings indicate that the deep-sleep strategy contributes to improved energy efficiency in battery-based monitoring systems and may represent a more appropriate approach to slowing the rate of battery discharge, supporting the development of more energy-efficient and reliable IoT systems.
Spatial Clustering of Electricity Consumption Patterns in Indonesian Higher Education Institutions Rahardjo, Imam Arif; Garniwa, Iwa; Sudiarto, Budi; Jan, Pidanic
ELKHA Vol. 18 No.1 April 2026
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v18i1.104516

Abstract

Higher education institutions represent a significant contributor to electricity consumption in the public sector, particularly in developing countries such as Indonesia. This study aims to identify spatial patterns and provincial disparities in electricity consumption across Indonesian higher education institutions. This research method uses spatial autocorrelation analysis with Moran's I and hierarchical clustering based on Ward’s method. The results show that the observed Moran’s I (0.5129679) is higher than the expected Moran’s I (-0.03030303), and the spatial pattern of electricity consumption by higher education institutions is clustered. This result is confirmed by the negligible p-value (0.0003618787 < 0.05), indicating a strong clustered spatial pattern. Hierarchical clustering was used to identify three groups of provinces representing the level of electricity consumption. The findings highlight significant regional disparities in electricity consumption patterns and provide a quantitative basis for energy management strategies and sustainable higher education policy planning in Indonesia.
Co-Authors Abdul Multi Abdul Multi Agus Sugiyono Ahmudi, Ali Andi Amar Thahara Aulia, Hilmi Zaky Azhar, Fakhrudin Budi Sudiarto CHRISTIONO CHRISTIONO Citra Candranurani Daulay, Andini Dwi Khairunnisa Efendi, Juwani Elman Rudolf Pakpahan Erwin Elysar Fathur Dwipa Syaveyenda Fauzan Hanif Jufri Femy Sanana Sanvia Fikri, Miftahul Frederick Sakaja Ginting Hanafi Anis Alamri Harahap, Saskia Saraswati Hudaya, Chairul Jan, Pidanic Krishananto, Thomas Puri M Ahsin Sidqi Monika, Ratih Muhammad Pinandhito Adi Dharma Nainggolan, J M Novi Indriani Oh, Seongmun Purba, Kevin Pangestu Putra, Kharisma Darmawan Putranugraha, Derry Rahardjo, Imam Arif Raharjo, A Samual, Muhammad Gillfran SAPUTRA, DWI CAHYA AGUNG Semedi, Jarot Mulyo Sikumbang, Supriyanto Suwargono, Son Uno Sudibyo Widya Astriyani Widya Faisal Wahyudi Widyantara, Dwitiya Bagus Yumnaristya, Syefiara Hania Z Day, Faizatul Hasanah