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All Journal Indonesian Journal of Electrical Engineering and Renewable Energy (IJEERE) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Nanda Putri Miefthawati
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Materials Science & Nanotechnology

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Analisis Potensi Minyak Jelantah Sebagai Bahan Baku Produksi Biodiesel di Kota Pekanbaru : Analisis Potensi Minyak Jelantah Sebagai Bahan Baku Produksi Biodiesel di Kota Pekanbaru Tegar Setiadi, Rexy; Nanda Putri Miefthawati
Indonesian Journal of Electrical Engineering and Renewable Energy (IJEERE) Vol 3 No 1 (2023): IJEERE Juni 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijeere.v3i1.713

Abstract

Fuel Oil is a basic need for society, with a consumption rate of 42% in Indonesia. The high level of consumption, especially for diesel fuel, with supplies and availability of raw materials dwindling makes scarcity a common phenomenon in urban areas. Alternative innovations in the supply of raw materials need to be carried out, one of which is by using alternative raw materials in the form of Biodiesel derived from urban waste cooking oil. The aim of this research is to utilize the 40,000 Kg/month waste of used cooking oil in Pekanbaru City to be converted into Biodiesel with the Transesterification Method in the form of a chemical reaction Fat reacted with methanol and catalyst with the help of Superpro Design software is capable of producing a volume of biodiesel of 33,407 liters per month, a fuel density of 866/km3 with a production cost of IDR 6,000/liter and a selling price of IDR 11,000 per liter which has the potential to make biodiesel an alternative to diesel for the people of Pekanbaru City.
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 Tegar Setiadi, Rexy Tengku Reza Suka Alaqsa Zulfatri Aini