Optimasi Parameter Extreme Learning Machine pada Prediksi Penjualan Kopi Guna Efisiensi Stok UMKM

Heribertus Ary Setyadi; Pudji Widodo; Supriyanta Supriyanta

doi:10.47065/josh.v8i1.9345

Heribertus Ary Setyadi * Universitas Bina Sarana Informatika, Surakarta, Indonesia
Pudji Widodo Universitas Bina Sarana Informatika, Banyumas, Indonesia
Supriyanta Supriyanta Universitas Bina Sarana Informatika, Surakarta, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/josh.v8i1.9345

Keywords: Artificial Neural Network; Coffee Baverage; Extreme Learning Machine; Forecasting; Sales

Abstract

Pait Karanganyar Coffee Shop does not currently implement a structured forecasting technique to accurately estimate and predict demand for its coffee products. Prior studies demonstrate that the Extreme Learning Machine (ELM) method offers superior accuracy and highly efficient computation time compared to traditional models. For instance, a separate study using ARIMA achieved a Mean Absolute Percentage Error (MAPE) of 17.05%, while Long Short-Term Memory (LSTM) recorded a 7.5% MAPE. Based on these advantages, the ELM algorithm was selected for this study. This research utilizes this artificial neural network method to assist in predicting daily coffee beverage sales. Structurally, the ELM model is comprised of an input layer, an output layer, and a single connecting hidden layer. A dataset containing 215 daily sales data samples was collected at Pait Coffee Shop from April to October 2025 for testing purposes. The results and error values analysis using the MAPE method demonstrated an initial average error of 1.06%. Furthermore, a rigorous comparison between the actual and predicted data revealed that the optimal configuration of input features and hidden neurons achieved an average MAPE value of 3.39%. Ultimately, this research contributes to providing an efficient inventory forecasting model for retail coffee shop MSMEs to accurately minimize raw material waste. With this best configuration, an error rate measured using MAPE calculation was 3.39%. This research contributes to providing an efficient inventory forecasting model for retail coffee shop MSMEs to minimize raw material waste accurately.

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