Classification of Crude Palm Oil Quality Using Artificial Neural Networks Based on Chemical Components

Abstract

Palm oil, known as Crude Palm Oil (CPO), is a flagship product in the palm oil industry, playing a crucial role in the economies of various tropical countries such as Indonesia. This study aims to classify the quality of CPO based on chemical components using the Artificial Neural Networks (ANN) method with a backpropagation algorithm. The research data was obtained from PT. Perkebunan Lembah Bhakti (PLB) 2 from January to October 2023, consisting of 225 entries with five main chemical variables: impurity level, moisture content, free fatty acid (FFA) level, Deterioration of Bleachability Index (DOBI), and carotenoids. The data preprocessing stage involved transforming and normalizing the data using the Z-score method. The ANN model used has a 5-5-2 architecture with ReLU activation functions for the input and hidden layers and a Softmax function for the output layer. Model evaluation was conducted using accuracy metrics, which showed that the ANN model could classify CPO quality with an accuracy of 97.78% on the test data. The research results show that ANN can classify CPO quality with a high level of accuracy. This indicates that this method has great potential for use in the industry to improve CPO quality assessment. The improvement in accuracy and validity of the ANN classification results has significant implications for the industry. With high accuracy, ANN can reduce human errors in quality assessment, speed up the process, and increase the consistency of assessment results. This is crucial because consistent quality assessment can enhance operational efficiency and reduce production costs.