Business Intelligence Design for Data Visualization and Drug Stock Forecasting

Novenia Eka Warestika (Trisakti University)
Dedy Sugiarto (Information System Study Program, Universitas Trisakti)
Teddy Siswanto (Information System Study Program, Universitas Trisakti)

Intelmatics
Vol. 1 No. 1 (2021): January

Klinik Pratama is one form of service provided by the Ministry of Communication and Information of the Republic of Indonesia in protect employees from health disorders that could affect employee productivity. In its development, the clinic often finds problems, one of them is often a shortage or excess the drug stock on a running period. Therefore, it be required a design of an Business Intelligence that manages complex data into a data visualization forecasting of the future stock of drugs. Historical data processing of the drug is done with process of Extract, Transform and Load (ETL) using the Spoon Pentaho Data Integration tools. While the visualization of drug stock data and forecast results is done using Microsoft Power BI (Business Intelligence) tools and for forecasting is done with Artificial Neural Network method by RStudio tools. The results of forecasting the amount of stock out of drug samples using the Artificial Neural Network method obtained an MSE value of 67.72 and RMSE 8.22 which means that this forecast has a good ability with the resulting error rate is relatively small. From this research, the Klinik Pratama of the Ministry of Communication and Information can easily understand and analyze drug stock data and can support operational decision making The results of forecasting the amount of stock out of drug samples using the Artificial Neural Network method obtained an MSE value of 67.72 and RMSE 8.22 which means that this forecast has a good ability because the resulting error rate is relatively small. From this research, Klinik Pratama of the Ministry of Communication and Information can easily understand and analyze drug stock data and can support operational decision making The results of forecasting the amount of stock out of drug samples using the Artificial Neural Network method obtained an MSE value of 67.72 and RMSE 8.22 which means that this forecast has a good ability with the resulting error rate is relatively small. From this research, Klinik Pratama of the Ministry of Communication and Information can easily understand and analyze drug stock data and can support operational decision making.