Classification of Longan Edibility using Machine Learning

Longan fruit (Dimocarpus longan) originating from Southeast Asia, is a fruit that is commonly consumed in the region. The fruit is susceptible to damage during the distribution process. The selection of fruit that is suitable for consumption and that which is not is still made manually. This study aims to build a model using machine learning that can automatically classify longan edibility. Two classification algorithms are used, namely the k-nearest neighbor and the support vector machine. The model is trained with a number of images consisting of two classes, namely, fit (accepted) and unfit (rejected) to eat. The experimental results show that the algorithm's accuracy for k- nearest neighbors and the support vector machine are 93% and 98%, respectively. These results indicate that the classification of longan using machine learning is quite promising.