Sistem Klasifikasi Tingkat Kematangan Buah Cabai Katokkon Berdasarkan Fitur Warna LAB Menggunakan Artificial Neural Network Backpropagation
Keywords:
cabai katokkon, kematangan, klasifikasi, LAB, ANNAbstract
Chili is one of the horticultural commodities that has a very significant economic and cultural value in Indonesia. One type of chili that is unique but widely cultivated in Indonesia is katokkon chili (Toraja chili). Seeing the great potential that katokkon chili has, the chili is finally widely cultivated. However, various problems in the cultivation process until harvest have emerged. One of them is in the process of identifying the level of maturity. The stage of identifying the maturity level of chilies is an important aspect of cultivation and post-harvest handling. This is because the maturity level significantly affects the quality, nutritional content, and market value of chili katokkon. Many studies have utilized digital image processing and machine learning in fruit ripeness detection. However, until now, the detection of the maturity level of katokkon chili fruit is still done manually, which has an impact on the potential inaccuracy of classification results due to various factors. Therefore, this research proposes a classification system for the maturity level of katokkon chili fruit based on LAB color features using artificial neural network backpropagation. The proposed method consists of six main stages, namely image acquisition, preprocessing, segmentation, morphological operations, LAB feature extraction, and backpropagation artificial neural network modeling. The proposed method can classify the maturity level of chili katokkon into three classes with 96,00% accuracy, 96,40 % precision, and 96,00% recall. These results show that the proposed method can classify the maturity level of chili katokkon accurately.
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