IDENTIFIKASI VARIETAS BERAS BERDASARKAN CITRA DIGITAL MENGGUNAKAN IMAGE PROCESSING DAN NEURAL NETWORK; DIGITAL IMAGE BASED IDENTIFICATION OF RICE VARIETY USING IMAGE PROCESSING AND NEURAL NETWORK

Sumaryanti, Lilik, Sri Hartati

2015 | Disertasi | FMIPA

Rice is one of the main food products for most world population, including Indonesians. The increased of consumer concern on the originality of rice variety and the quality of rice leads to originality certification of rice by existing institutions. As a consequence, there should be an evaluation method for the originality of a product which is able to perform identification. In food industry, traditional evaluations are performed by several operators (trained instructors) using human sight. Along with technology development, it helps human to perform evaluations of food grains using images of objects. This research developed a system used as a tool to identify rice varieties. Identification process was performed by analyzing rice images using image processing. The analyzed features for identification consisted of six color features, four morphological features, and two texture features. Classification of rice varieties used learning vector quantization (LVQ) neural network algorithm which consists of competitive layers learned to classify the input vectors. If several input vectors were close to each other, those input vectors were categorized into the same class The Identification results using a combination of all features gave average accuracy of 70,3% with the highest classification accuracy level of 96,6% for Mentik Wangi and the lowest classification accuracy of 30% for Cilosari. Validation testing with k-fold cross validation gave accuracy of 67.8% .

Kata Kunci : Rice; Image Processing; Neural Network; LVQ; K-Fold Cross Validation