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All Journal Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Proceeding Mercu Buana Conference on Industrial Engineering
Sutisna, Fazar
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Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Analisis Perencanaan Produksi LPG Menggunakan Pendekatan Forecasting Vikaliana, Resista; Sutisna, Fazar
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 12 Issue 1 June 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i1.25317

Abstract

Production planning in the oil and gas industry is a key element for operational efficiency and response to changes in market demand. This research focuses on smart and adaptive strategies through the application of two main approaches: forecasting. The purpose of this study is to determine the most suitable forecasting model from the five forecasting models (Simple Exponential Smoothing, Naive Method, Simple Moving Average, Weighted Moving Average, and Exponential Moving Average) to be used in LPG production and calculate the value of forecasting in the next five periods. Using LPG historical data from 2017 to September 2023.  Then the results are compared using forecasting error metrics such as MAPE and RMSE. It was concluded that the Simple Exponential Smoothing model showed a forecasting error value of 21.58% for MAPE and 72764.01 for RMSE. The Naive model has a forecast error value of 20.33% for MAPE and 78044.48 for RMSE. Meanwhile, the Simple Moving Average recorded a forecast error value of 20.28% for MAPE and 64449.76 for RMSE. On the other hand, the Weighted Moving Average shows a percentage error of 16.34% with an RMSE value of 48426.57. Finally, the Exponential Moving Average (EMA) shows an optimal level of accuracy, with a forecast error value of 16.01% for MAPE and 46046.42 for RMSE. Thus, from the five models evaluated, it can be concluded that the Exponential Moving Average (EMA) is the best model for forecasting LPG products, considering the lowest level of accuracy and percentage of forecasting errors. This study identifies the EMA as the best method in forecasting LPG production. The implication is a positive contribution to the accuracy of predictions and planning efficiency.
Feasibility Analysis of Using Electronic Vehicles at Grab Indonesia with the IRR Method Furqan, Muhammad Alif; Sahbandar, Aulia Idharizqi Widayani; Barandika, Barandika; Rumalutur, Yizri Ievana Febrianty; Abid, Rasyid; Sutisna, Fazar; Iskandar, Yelita Anggiane; Vikaliana, Resista
Proceeding Mercu Buana Conference on Industrial Engineering Vol 5 (2023): LEAN AND GREEN FOR SUSTAINABILITY DEVELOPMENT GOALS IN THE I4.0 ERA
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/MBCIE.2023.008

Abstract

Indonesia is a country with the fourth largest population in the world, reaching 275.77 million people in 2022. This has caused the need for transportation to increase, thereby creating new business opportunities for public transportation companies, including online motorcycle taxi companies such as Grab which provide service gates - to-door and order food delivery. However, the increase in transportation needs also causes an increase in fuel consumption, one of the factors causing air pollution. This study aims to calculate and determine the feasibility of using electric vehicles by Grab drivers to transport passengers, goods, or food while calculating the carbon emissions created as a result of these various activities. The feasibility analysis is based on the Internal Rate of Return (IRR) of each type of transport vehicle used, using the Sum of Years Digits (SOYD) method, which takes into account depreciation, fuel use, and the carbon emissions produced. The results of the study show that the use of electric vehicles is a viable investment in the long term, both technically and financially.
Co-Authors Abid, Rasyid Barandika, Barandika Furqan, Muhammad Alif Iskandar, Yelita Anggiane Rumalutur, Yizri Ievana Febrianty Sahbandar, Aulia Idharizqi Widayani Vikaliana, Resista