Peramalan Alokasi BBM Subsidi Kereta Api dengan Metode Statistika dan Machine Learning (Studi Kasus: Badan Pengatur Hilir Minyak dan Gas Bumi Jakarta)

  • Yelita Iskandar Pertamina University
  • Nadia Carolina Universitas Pertamina
DOI: https://doi.org/10.21009/logistik.v15i01.26497
Keywords: Forecasting, Refined Fuel Oil, Statistics, Machine Learning, MAPE

Abstract

Based on the results of the on-desk verification on June 2nd, 2021, it was found that there was a quite significant overstock of subsidized refined fuel oil for PT KAI (Indonesian Railways Company) during the Covid-19 pandemic, 42.9% in the first quarter of 2021 for passenger train types. The purpose of this study is to find out forecasting with statistical methods and machine learning in solving the overstock problem by finding the best fuel oil allocation scenario for PT KAI with the benchmark is the measurement that yields the smallest error using the Mean Absolute Percentage Error (MAPE). Experiment results show that exponential method with a MAPE value of 7.37% is good in predicting the allocation for the passenger train section of PT KAI of 8,474.52 and 7,836.58 kiloliters for the 3rd and 4th quarter of 2021, 7,246.65 and 6,701.14 kiloliters for the 1st and 2nd quarter of 2022. This research was conducted based on condition that there was no previous research that forecast the refined fuel oil needs of the passenger train section of PT KAI that was heavily affected by Covid-19.

Published
2022-04-22
Issue
Vol 15 No 01 (2022): Logistik
Section
Articles

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