Penerapan Algoritma Artificial Neural Network dan Economic Order Quantity dalam Memprediksi Persediaan Pengendalian BBM

Abstract

Motor vehicle production in Indonesia increases every year along with increasing demand for fuel as a raw material. Generally, gas stations carry out the process of ordering fuel from Dempo on an irregular basis, the frequency of orders does not have a certain time, orders depend on sales transactions and the amount of fuel inventory available depends on the fuel in storage. Regarding prediction and control of fuel supplies, the risk at gas stations is that the volume of fuel received is different from that ordered. It is suspected that tank trucks carrying fuel during delivery from the depot to gas stations tend to experience evaporation in the tank (loses), so that the fuel quantity decreases. Requests for fuel filling are only based on monitoring without any special calculations resulting in stock being maintained and not covering consumer demand. This research is to analyze the Artificial Neural Network algorithm in predicting fuel, and determine inventory control using Economic Order Quantity. The research was conducted using data from November 2020 - October 2023. The data was processed using the ANN algorithm using Google Colab, and continued with EOQ using Microsoft Excel. The ANN parameters are 1 hidden layer with 100 units, Adam optimizer, learning rate 0.001, batch size 8 and epoch 200. Pertalite ANN test results are MSE 248852593.81 and MAE 12749.45, while Pertamax Turbo MSE 803842.94 and MAE 672, 74 provides predictions for November and December of 11,1436.82 L and 11,1960.83 L and Pertamax Turbo of 3,782.46 L and 3,660.70 L. Furthermore, in 2023 the fuel EOQ of Pertalite and Pertamax Turbo will be 8,445 L and 5,261 L, Safety Stock 3,516 L and 1,064 L, Maximum Inventory 6,042 L and 5,153 L, Re order point 2,403 L and 108 L, Order frequency 149 times and 6 times with Total Inventory Cost Rp. 178,830,302 and Rp. 7,700,459.