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All Journal JURNAL NASIONAL TEKNIK ELEKTRO Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Tengku Reza Suka Alaqsa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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Evaluasi Pengaruh Tekanan-Arus pada Kehilangan Fiber melalui NIRS DA1650 Tengku Reza Suka Alaqsa; Zulfatri Aini; Liliana
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 3: November 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n3.1233.2024

Abstract

This study focuses on enhancing the yield of crude palm oil (CPO) during the pressing process by thoroughly examining the oil losses that occur throughout production. The primary aim is to evaluate how different pressures and electric currents impact oil losses from palm fiber at a specific palm oil mill in Pantai Cermin, Kec. Tapung, Kampar, Riau. A systematic methodology was employed to achieve this, which involved detailed measurements conducted using the FOSS NIRS DA1650. This advanced technology allowed for precise assessment and quantification of oil losses during the pressing phase. Following the data collection, a rigorous statistical analysis was performed utilizing determination coefficients to interpret the relationship between the variables. The analysis results revealed a coefficient of determination (R²) of 49.96% concerning pressure, suggesting that nearly half of the variability in oil losses can be explained by fluctuations in pressing pressure. Additionally, the examination of current showed a higher coefficient of determination of 60.09%, underscoring a substantial influence of electric current on fiber oil losses. These findings highlight the critical importance of optimizing pressure and current in palm oil extraction. By making informed adjustments to these parameters, mill operators can significantly reduce oil losses, thus enhancing the overall extraction efficiency. The study provides practical recommendations for operators aiming to improve their processes, ultimately contributing to better resource utilization and increased profitability in the palm oil industry.
Forecasting Electricity Consumption in Riau Province Using the Artificial Neural Network (ANN) Feed Forward Backpropagation Algorithm for the 2024-2027 Tengku Reza Suka Alaqsa; Zulfatri Aini; Liliana; Nanda Putri Miefthawati
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/7eeq7029

Abstract

Electricity production in Riau Province fluctuates between surplus and deficit, as reported by the Central Statistics Agency. From a peak of 3,758.75 GWh in 2017, production fell to 525.19 GWh in 2019, mainly due to lack of investment in new power plants and dependence on external electricity supply. This study addresses these challenges by using the Artificial Neural Network (ANN) Feed Forward Backpropagation method to forecast electricity demand from 2024 to 2027. This study aims to analyze the accuracy of the prediction through the Mean Absolute Percentage Error (MAPE), evaluate electricity consumption projections, and calculate the annual growth rate. The gap in this study is the inclusion of previously ignored variables, namely the GRDP of Government Buildings and the number of Government Building customers. The methodology used is Artificial Neural Network Feed Forward Backpropagation. In the training data training, the MAPE was obtained at 4,315%. The electricity consumption prediction obtained is 8,679 GWh in 2024, 9,690 GWh in 2025, 10,959 GWh in 2026, and 12,681 GWh in 2027. The growth rate is also projected to increase, namely 5.67% from 2023 to 2024, 11.65% from 2024 to 2025, 13.10% from 2025 to 2026, and 15.71% from 2026 to 2027.
Co-Authors Liliana Nanda Putri Miefthawati Zulfatri Aini