Model PNN Untuk Estimasi Kandungan Lignin pada Dedak Padi Bercampur Sekam Berbasis Citra Warna YCbCr
DOI:
https://doi.org/10.55606/isaintek.v8i2.383Keywords:
histogram, lignin, PNN, rice bran, rice huskAbstract
Adulteration of rice bran is commonly done by mixing it with materials of similar appearance but lower nutritional value, such as ground rice husk. A key indicator of such adulteration is increased lignin content. Adding phloroglucinol solution to the mixture produces a red color that varies with lignin levels. This study aims to estimate lignin content in rice bran-husk mixtures using artificial intelligence and digital image processing. YCbCr color model images of eleven rice bran-husk compositions, treated with phloroglucinol, were analyzed. The lignin content of each variation was measured in the lab and used to define eleven classes. A Probabilistic Neural Network (PNN) was employed as the classifier, with image histograms of varying bin sizes as input. PNN performance was evaluated using 4-fold cross-validation. Results showed the highest average accuracy of 85.80% with 32 bins and histograms from all three YCbCr channels.
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