Prediksi Persediaan Obat Pada Apotek Menggunakan Algoritma Decision Tree
DOI:
https://doi.org/10.30865/klik.v4i4.1681Keywords:
Prediction; Medicine Stock; Decision Tree; Accuracy; Precision; Recall; F1-ScoreAbstract
A pharmacy is a place for buying and selling drugs and must have an adequate stock of drug supplies so that it can serve consumers in need. In some pharmacies there are problems related to drug supply. Often the drugs needed by the community are empty in stock, while the drugs that are less needed are stored in the warehouse. Therefore, this study aims to conduct a prediction model of drug supply so that it can meet consumer needs. This study uses drug inventory data at Kaligandu Pharmacy, the data has 2745 rows and 5 attributes consisting of "Item Name", "Unit", "Previous Stock", "Rill Stock", and "Restock". The method used in this case is the Decision Tree algorithm with Accuracy, Precision, Recall, and F1-Score evaluation methods to see which drugs are available and not available based on "Unit". The results showed that the Decision Tree algorithm obtained good results by using a data comparison of 80 to 20 resulting in an accuracy value of 98.71%. In addition, the resulting values of Precision, Recall, and F1-Score are not much different, namely 0.9872, 0.9872, and 0.9867. The 70 to 30 data comparison produces a smaller value but is not much different from the results of 80 to 20, namely the accuracy of 98.28%, Precision 0.9832, Recall 0.9828, and F1-Score 0.9804. with these results this research can be continued by implementing drug inventory prediction using Decision Tree into an application
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