JST: Prediksi Perkembangan Produksi Tanaman Sayuran Dalam Upaya Pemenuhan Gizi Masyarakat dengan Algoritma Resilient - Manurung, Azwar Anas; Satria, Indra; Wanto, Anjar (STIKOM Tunas Bangsa Pematangsiantar, 2023)
Vegetable plants are very important in human life because they have a significant role as a source of nutrition and fulfillment of community nutrition. Therefore it is important to predict the production of vegetable crops. This study will use the Resilient algorithm which is one of the algorithms from Artificial Neural Networks (ANN) which is commonly used to predict data. This study uses times series data on vegetable crop production in North Sumatra Province from 2013 to 2022, obtained from the Indonesian Central Statistics Agency (BPS) website. The research topic will be analyzed using 5 ANN models, including: 8-8-1, 8-16-1, 8-24-1, 8-32-1 and 8-40-1. Based on the analysis results, model 8-32-1 was chosen as the best model, because it has an accuracy rate of 89% (the highest compared to other models). The results showed that the Resilient algorithm was able to predict vegetable crop production well. This research has important implications in supporting the sustainability of agricultural and food systems by providing information on developments in vegetable crop production to help farmers, producers and governments plan agricultural activities more effectively.