PENGGUNAAN TEKNIK DATA MINING UNTUK PERAMALAN PERMINTAAN SEMEN DI PT X

• Main Author: Alby Assyauqi, Mikail
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/54201
• Institution: Institut Teknologi Bandung

PT X is a national company (BUMN) that produces construction materials in the form of various types of cement and its derivatives needed for construction. This company is a holding company that has many production plants located in various regions in Indonesia and abroad. One of the production plants of PT X is located in Tuban Regency, East Java Province. The PT X factory, which is located in Tuban Regency, East Java Province, has a production target of four to five million tons of cement per year. To achieve this production target, PT X requires a better production planning process. To support its production planning, PT X requires a production planning system that can assist in aggregate planning and disaggregate planning for cement production. Departemen Perencanaan Produksi dan Evaluasi PT X requires a cement demand forecasting model. Therefore, this study was conducted to develop a demand forecasting model for cement. There are six forecasting models developed in this research. Three forecasting models consisting of ARIMA, XGBoost, and LSTM are used for time series forecasting, while the other three models consisting of the Croston method, the TSB method, and the modified SBA method are used for forecasting intermittent demand. The deep learning model with LSTM architecture was chosen because it achieved the best score on five time series forecasting models with root mean square error (RMSE) score of 8082.30, 3679.61, 5753.43, 3657.19, dan 2248.68. And mean absolute percentage error (MAPE) score of 18.63%, 29.42%, 19.26%, 30.32%, dan 9.67%. For intermittent demand, the TSB model was chosen for three intermittent demand forecasting models with the best RMSE score of 107,82, 3555.34, and 3334.93. The Croston model was chosen for intermitent data model with the best RMSE score of 3914.23.