International Journal on Advanced Science, Engineering and Information Technology, Vol. 8 (2018) No. 5, pages: 1845-1849, DOI:10.18517/ijaseit.8.5.6511

Predicting the Amount of Digestive Enzymes Medicine Usage with LSTM

Adhistya Erna Permanasari, Abi Mahan Zaky, Silmi Fauziati, Ida Fitriana

Abstract

Medicines are widely used to prevent or cure illness. One of the medicines which often used to relieve stomach pain is a medicines that contains digestive enzymes. This type of medicines is much needed by hospitals and other health institutions. Hospitals and other health institutions should ensure the availability of medications for patients. This situation forces health institutions to deal with the uncertainty of medicine usage. Hospitals as one of the health institutions have some challenges. One of the challenges that must be faced is to ensure the availability of medicines for patients. The ability to predict can help ensure medicines availability in hospital. In this study will presents the forecasting model using Long Term Short Memory (LSTM) method to predict the need for medicines that contain digestive enzymes in the hospital. This method is chosen because it is known to have a high accuracy to predict stationary data. One of the methods used in input identification for the LSTM method is by using the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF). The results of this study indicate that the use of LSTM method suitable for time series forecasting in historical dataset, with 12.733 Root Mean Square Error (RMSE) value.

Keywords:

LSTM; forecasting; medicine

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