Peramalan Jumlah Permintaan Container Dengan Algoritma Regresi Linear - Hsb, Khoiri Sutan; Kurniawan R, Rakhmat (Forum Kerjasama Pendidikan Tinggi (FKPT), 2024)
The rapid growth of the logistics industry demands effective management of containers as essential transportation tools. Unpredictable container demand can lead to either overstocking or understocking, which impacts operational efficiency. This study aims to forecast container demand using the simple linear regression algorithm. The data used is historical data from PT. Bintika Bangunnusa (BBN) from January 2022 to August 2024. The independent variable used in the model is the amount of goods exported from Indonesia. The results of the study indicate that the simple linear regression algorithm is capable of predicting container demand with a reasonable level of accuracy. The model evaluation, using Root Mean Square Error (RMSE), shows that this model can serve as a decision support tool in container stock planning. However, the study also finds that the forecasting accuracy could be improved by incorporating additional external variables into the model. This research provides significant contributions to logistics management, particularly in container demand forecasting, which can help optimize the company's operational capacity.