Predicting Tablet Drug Expenditures Using Python-Based Facebook Prophet in Pharmaceutical Installations - Kaka Rizki Maulana; Widiyono Widiyono; Arief Soma Darmawan (JURNAL TEKNOLOGI DAN OPEN SOURCE, 2025)
The increasing complexity of pharmaceutical logistics requires accurate forecasting to ensure drug availability and minimize the risk of stock shortages. This study aims to develop a forecasting model to predict monthly tablet drug expenditure in the Pharmacy Department. The research stages include problem identification, data collection from historical drug expenditure records, data pre-processing, and implementation of the forecasting model. The method used is Facebook Prophet, which was chosen for its ability to capture seasonal patterns, trends, and holidays in time series data. Model performance evaluation was conducted using Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The results showed that the model produced an MAE of 3,621.25 and a MAPE of 4.93%, indicating that the prediction accuracy level was in the good category. These findings prove that the Prophet method is capable of providing reliable results in drug expenditure forecasting. The results of this study are expected to support decision-making in drug requirement planning and improve the efficiency of pharmaceutical logistics management.