Model PNN Untuk Estimasi Kandungan Lignin pada Dedak Padi Bercampur Sekam Berbasis Citra Warna YCbCr - Aziz Kustiyo; Bahri, Zuhdi Mukarom; Ardiansyah, Firman; Agmalaro, Muhammad Asyhar (Jurnal Informasi, Sains dan Teknologi, 2025)
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.