MODEL PERAMALAN UNTUK PERENCANAAN PERSEDIAAN MATERIAL PESAWAT B737-800 DI PT GARUDA MAINTENANCE FACILITY AERO ASIA TBK MENGGUNAKAN LSTM DAN GRU - Kafa Atriantio, Marchiano
As the COVID-19 epidemy sweeps by, PT Garuda Maintenance Facility Aero Asia Tbk (GMF) was experiencing a net loss of $328 million and $127 million in 2020 and 2021 respectively. This was due to the incorrect amount of order that subsequently resulted in inventory circulation dropping the in value below one. In accordance to the aforementioned case, this research intends to develop an effective forecasting model to help expendable material planning for Boeing 737- 800 in GMF’s inventory. The research methods used in this paper involves classification of materials based on Fast, Slow, Non-Moving (FSN) analysis technique, material demand data pattern recognition through data decomposition, and implementation of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network model to forecast material demand. Evaluation is done with Mean Squared Error (MSE), and order method with Economic Order Quantity (EOQ) which will be utilized to determine the optimal order quantity.