Prediksi Penjualan Aerosol Menggunakan Algoritma ARIMA, LSTM Dan GRU - Nendi Sunendar; Harjono P. Putro; Rizki Hesananda (Yayasan Literasi Sains Indonesia, 2025)
The advancement of information technology has significantly enhanced operational efficiency by enabling companies to process data more effectively and make better decisions. In a highly competitive global market, distributors face major challenges, including shorter product life cycles and fluctuating customer demand. These factors impact stock and production management, necessitating more accurate predictive solutions to optimize production planning. This study aims to compare the performance of ARIMA, LSTM, and GRU models in sales forecasting using time series forecasting methods. ARIMA represents a traditional statistical approach, while LSTM and GRU, based on deep learning, are capable of capturing complex data patterns. The models are evaluated using MSE, RMSE, MAE, and MAPE metrics. The results indicate that LSTM outperforms other models with a MAPE of 10.76%, followed by ARIMA (11.23%) and GRU (11.47%). LSTM excels in identifying long-term trends and seasonal patterns, while GRU achieves nearly comparable accuracy with a shorter training time. ARIMA, despite its simplicity, struggles to handle non-linear patterns. These findings provide valuable insights for companies in selecting the most suitable predictive model to optimize supply chain management, enhance operational efficiency, and support more informed decision-making.