Comparing of Artificial Neural Network and Multiplicative Holt Winters Exponential Smoothing Methods in Forecasting Demand
Perbandingan Metode Jaringan Syaraf Tiruan dan Multiplicative Holt Winters Exponential Smoothing dalam Meramalkan Permintaan
DOI:
https://doi.org/10.21070/ups.3913Keywords:
Forecasting, Demand, Multiplicative Holt-Winters, Artificial Neural Network, RapidMinerAbstract
One of the challenges faced by business operators is the fluctuation in the quantity of raw material orders, leading to both shortages and excesses, particularly during specific months such as approaching holidays and the new year. To address this issue, this research aims to forecast the demand for wallet products from the UMKM Pengerajin Dompet Khas Tanggulangin (PDKT) by comparing two methods: Artificial Neural Networks capable of extrapolating data to forecast future periods, and the Multiplicative Holt Winters method designed specifically for data with seasonal patterns. A comparative analysis is conducted to determine the method with the highest accuracy. The research results indicate that the Artificial Neural Network method yields an RMSE value of 14.249, whereas the Holt Winters method produces an RMSE value of 93.436. From this comparison, it can be concluded that the Artificial Neural Network method exhibits better accuracy compared to the Holt Winters method.
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