Prediksi Jumlah Obat Menggunakan Jaringan Syaraf Tiruan RNN pada Data Penjualan Bulan Juli - Billah, Ana; Yolanda, Aulia; Khairani, Dea; Putra, Dimas Triono; Syif, Helma Tiara (Jurnal Sains dan Teknologi (JSIT), 2025)
Accurate drug quantity predictions are crucial in inventory management at pharmacies or hospitals to ensure sufficientdrug availability and avoid overstocking or stockouts. However, these predictions are often difficult to make due tocomplex and dynamic drug sales patterns. This study aims to predict drug sales volume using Recurrent Neural Network(RNN) and Long Short-Term Memory (LSTM). The dataset was collected from pharmacy sales records in July. Theresearch stages included data preprocessing, normalization, constructing a time series dataset with a window size of 3,and splitting into training (80%) and testing (20%) datasets. The models were trained for 100 epochs with a batch size of10. The results show that the RNN model achieved a Root Mean Squared Error (RMSE) of 338.16, while the LSTM modelproduced an RMSE of 433.44. This indicates that RNN outperformed LSTM in predicting drug sales on a simple dataset.The findings suggest that RNN can serve as an alternative method to support drug stock planning to ensure betterdistribution and availability