KLASIFIKASI KUALITAS APEL DENGAN PERBANDINGAN MENGGUNAKAN ALGORITMA ANN DAN SVM PADA DATASET APPLE QUALITY
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Apple quality is an important factor in the agricultural and food distribution industries, influencing product sales data and consumer satisfaction. Classification methods such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used to predict apple quality automatically, increasing the efficiency of quality assessment. This research compares the performance of ANN and SVM in apple quality classification using a dataset that includes physical attributes and quality categories. ANN uses a feedforward neural network with backpropagation, while SVM uses Linear, Radial Basis Function (RBF) and Polynomial kernels with hyperparameter optimization. By testing both methods on the same dataset, this research provides an overview of the efficiency and performance of each method, as well as providing a basic overview for selecting a classification method that is suitable for the apple dataset. The results can help farmers and producers accurately predict apple quality, minimize losses and increase product added value.
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