Abstract
Rice is a staple that is cooked so that it becomes rice for daily consumption. The type of rice that is often used for daily consumption is white rice. There are several types of white rice circulating in the market that are consumed by the public. Each type of rice gives different scent, taste and price. This study compares the accuracy of white rice type recognition based on several camera resolutions. The types of rice used in this study are Jawa Barat rice, Jawa Timur rice, Pandan Wangi rice, Thailand rice and Vietnam rice. The camera resolution used is 5MP, 8 MP, 12 MP, 14 MP, and 16MP. The shooting distance used is ± 9 cm between the camera and the object of rice. The recognition method used is BackPropagation Artificial Neural Networks, while for feature extraction using the Gray Level Co-occurrence Matrix (GLCM) which consists of contrast, energy, homogeneity, and correlation. The highest results obtained at 12 MP camera resolution with the results of the recognition of 25 of 50 test data and the results of the calculation with confusion matrix obtained an average accuracy of 82%, precision of 55%, and recall of 50%. The results of this study can be used as a reference for research that uses objects of similar character, or further research with the same object in developing applications that are ready to use.
Highlights
Rice is a staple that is cooked so that it becomes rice for daily consumption
Hasil tertinggi didapatkan pada resolusi kamera 12 MP dengan hasil pengenalan sebanyak 25 dari 50 data uji serta hasil dari perhitungan dengan confusion matrix diperoleh rata-rata accuracy sebesar 82%, precision sebesar 55%, dan recall sebesar 50%
N : Number of gray levels used in quantization process μ : Gray Level Co-occurrence Matrix (GLCM) mean σ2 : The variance of the intensities of all reference pixels in the relationship that contributed to the GLCM
Summary
Beras merupakan makanan pokok yang dihasilkan dari tanaman padi yang ditanam di sawah. Beras merupakan bahan pokok yang dimasak atau Penelitian klasifikasi mutu buah pisang dengan metode ditanak sehingga menjadi nasi untuk dikonsumsi oleh pengenalan jaringan syaraf tiruan menggunakan sebagian besar masyarakat Indonesia sehari-hari. Penelitian identifikasi varietas cabai berdasarkan morfologi daun dengan metode pengenalan jaringan Jenis beras yang digunakan dalam penelitian ini adalah syaraf tiruan mendapatkan rata-rata akurasi sebesar beras dengan sebutan beras pandan wangi, beras jawa 97.92% [10]. Kamera sensor pada perbandingan tingkat akurasi beberapa tingkat resolusi kamera pada pengenalan lima jenis beras putih Faktor terpenting dalam menentukan kinerja kamera dengan metode pengenalan jaringan syaraf tiruan dan secara keseluruhan adalah ukuran sensor kamera, ekstraksi ciri berasal dari GLCM (Gray Level Co- karena hal ini berpengaruh pada kualitas, warna, detail, Occurrence Matrix), sehingga penelitian ini dan tingkat noise pada gambar [15]. N : Number of gray levels used in quantization process μ : GLCM mean σ2 : The variance of the intensities of all reference pixels in the relationship that contributed to the GLCM
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