Analisis Klasifikasi Komoditas Harga Pangan Menggunakan Artificial Neural Network pada Kualitas Beras Pulau Jawa

Abstract

Rice is a highly important food commodity that significantly influences the welfare of the Indonesian population, with approximately 98% of the Indonesian population relying on it as their main food source. There are several qualities of rice such as low-quality rice, medium-quality rice, and super-quality rice. The differences in rice quality types are usually influenced by several factors, including the level of processing, nutrient content, and quality level. Thus, in general, many consumers are unable to differentiate rice quality based on its physical appearance when purchasing rice. Instead, they tend to assess rice quality solely based on its price aspect. Therefore, this study classifies rice quality by focusing on the price variable as the main reference. Artificial Neural Network (ANN) method is used to classify rice quality based on price. ANN is a non-linear technique commonly used for model classification. ANN method is one of the famous data mining prediction models known for its accuracy in classification. By using the ANN method, it is found that the best proportion is using an 85% training and 15% testing proportion, and obtaining an accuracy value of 70% which can be considered quite good. The ANN method shows that the ANN model can learn significant patterns from the given data.