Implementasi Deep Learning Untuk Menentukan Harga Buah Sawit - Romtika Manurung; Volvo Sihombing; Mila Nirmala Sari Hasibuan (Jurnal Informatika, 2024)
This study aims to analyze the price of palm oil using Convolutional Neural Network (CNN) method in deep learning. CNN was chosen for its ability to process complex data and recognize patterns from diverse data. The stages of research include data analysis, data pre-processing, predictive model design for CNN method, CNN classification model prediction results, CNN method evaluation, and CNN method evaluation results. This study aims to produce a model that can predict the price of oil palm with high accuracy, based on data covering a variety of characteristics of farmers and the quality of oil palm crops. Prediction results were conducted using data from 50 oil palm farmers. From the prediction, as many as 23 data farmers get a price of IDR 2,300, 13 other farmers get a price of IDR 2,000, and the remaining 14 data farmers get a price of IDR 1,800. The results of this prediction are based on data from farmers and the quality of oil palm crops they grow and produce. By utilizing the CNN method, the model can capture various factors that affect the price of palm oil, including the quality of palm fruit and agricultural conditions. Evaluation of the CNN method showed very good results, with almost perfect accuracy. This method managed to predict palm oil prices very precisely, showing that CNN can be an effective tool in the analysis of palm oil prices. The results of this evaluation confirmed that the CNN method can be relied upon to provide accurate predictions, helping farmers and palm oil industry players in determining prices that are in accordance with the quality and condition of the crop.