The process of sorting papaya fruit based on quality is one of the processes that greatly determines the quality of papaya fruit that will be sold to consumers. The process of identifying the quality of fruits using the human eye has the disadvantages of requiring more energy to sort, the level of human perception in terms of different fruit sorting, the level of human consistency in assessing fruit quality is also unstable because humans can experience fatigue. Research on fruit using image processing is the current trend, especially for fruit conditions, both qualities weight and size because this system processes faster and avoids or reduces failures that occur as a result of human nature. The process of selecting the level of fruit maturity in the process of recognition and determination and classification of post-harvest agricultural products on papaya fruit, depends on how the system is built. This study aims to build a quality recognition system for papaya fruits using Digital Image Processing technology, to analyze the level of color values (RGB), to determine the maturity level of papaya-callina fruit, so that later can be used as a reference in determining the maturity level of papaya fruit. First, the image of papaya is taken, or the acquisition uses a camera to be used as a database based on the condition of its maturity level. Second, the separation of the fruit image with the background based on the pixels, calculating the pixel value looking for the mean value, min, the max that is used later in the reference in determining the fruit maturity condition: young ripe, the half mature, mature. The results of this study provide information about pixel data in which young ripe papaya, red value does not dominate that is 7.785495, the green value becomes the highest value of 10.23922, papaya the half mature, it can be seen that the red and green composition of the pixel value is almost the same, namely 12.56288 and 12.12431, while the fully mature condition of the papaya, average red pixel value becomes more dominant when compared to green, which is 24,111901 for red and 13,70812 for green.

Teknologi Identifikasi citra sidik jari banyak digunakan diberbagai bidang kehidupan manusia.. Pada prosesidentifikasinya banyak kendala yang dihadapi, diantaranya proses pembacaan luas daerah citra sidik jariyang akan di pisahkan antara foreground (ridge) dan background (valley) dari daerah yang ditangkap olehmesin fingerprint secara keseluruhan. Disini permasalahan yang timbul yakni memisahkan antara citra sidikjari dengan yang bukan bagian dari citra sidik jari ketika proses pengambilan citra sidik jari. Banyak carauntuk proses pemisahan (segmentasi), diantaranya menggunakan metode deteksi tepi canny dan metode tepisobel untuk membatasi daerah yang akan di segmentasi dengan metode morphologi pada pixel citra. Prosespemisahan yang dilakukan pada penelitian ini adalah pembacaan citra (objek citra yang sama), memberikannilai ambang, deteksi tepi, operasi morphologi, struktur elemen, erosi dan dilasi. Hasil proses segmentasiyang dilakukan pada citra sidik jari terlihat ada perbedaan luas daerah pada citra yang sama denganpemberian nilai ambang 170, struktur elemen yang digunakan 00da 900diperoleh luas daerah citra sidikdengan tepi canny sebesar 21817.43 dan luas daerah dengan tepi sobel adalah 21648.93 dan selisih jarak9799.41Kata Kunci : Canny,sobel, deteksi tepi, morphologi, dilasi, erosi.