Price Prediction for Agricultural Commodities in Bandung Regency Based on Functional Link Neural Network and Artifical Bee Colony Algorithms

Abstract

In Indonesia, fluctuating agricultural commodity prices often impacts society negatively. In this study, farmers in Bandung Regency, West Java, Indonesia, were chosen as a case study. Fluctuating agricultural commodity prices can lead to farmers suffering losses due to the sale price being smaller or equal to the cost of planting. Price is influenced by crop productivity, while planting productivity is strongly influenced by weather. A system is developed in this study to predict the price of agricultural commodities based on price, productivity and weather history using a Functional Link Neural Network (FLNN) algorithm optimized with the Artificial Bee Colony (ABC) algorithm. The price prediction results can be used as recommendations for farmers as to whether they should plant or not. In addition, the prediction results are compared to the Artificial Neural Network (ANN) algorithm with Backpropagation algorithm as the learning algorithm. From the experimental result, the best Mean Absolute Percentage Error (MAPE) value was obtained with FLNN-ABC: 7.68% for the predicted price of chili and 10.59% for the predicted price of onion.