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All Journal Jurnal Agritech
Hadi K Purwadaria
Departemen Teknik Pertanian, Fateta IPB

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Identifikasi Tingkat Ketuaan dan Kematangan Pepaya (Carica papaya L.) IPB 1 dengan Pengolahan Citra Digital dan Jaringan Syaraf Tiruan Syaefullah, Enrico; Purwadaria, Hadi K; Sutrisno, Sutrisno; Suroso, Suroso
Agritech Vol 27, No 2 (2007)
Publisher : Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (474.465 KB) | DOI: 10.22146/agritech.9496

Abstract

The objective of this research was to identify the maturity and ripeness of papaya using image processing and artificial neural network. The images of papaya IPB 1 were captured using digital camera. And then processed using image processing algorithm. The image processing algorithm was developed and applied to 150 samples of papaya from three level of ripeness; growth, mature and ripe and 150 samples of papaya from three level of maturity based on their harvest time. The color indexes and shape factors were extracted from sample images using the developed image processing algorithm. The features extracted from the image processing were used as input to develop artificial neural network that modelled into 7 inputs with the level of maturity and ripeness as output. Neural network program used the value of momentum constant 0.5, learning rate value contant 0.6, sigmoid function value 1 and 10000 iteration. The result showed that the use of 7 image processing features as input on 3 hidden layers provided the highest accuracy of validation of 97.8% in validation process, and 100% accuracy in classifying the papaya based on its maturity and ripeness.ABSTRAKPenelitian ini bertujuan mengidentifikasi ketuaan dan kematangan buah pepaya dengan menggunakan pengolahan citra dan jaringan syaraf tiruan. Citra pepaya diambil menggunakan kamera digital. Citra diproses menggunakan algoritma pengolahan citra. Algoritma pengolahan citra dibangun untuk 150 contoh pepaya dari tiga tingkat kematangan yaitu muda, tua dan matang dan 150 contoh pepaya dari tiga tingkat ketuaan berdasar pada umur petiknya. Indeks warna dan tekstur didapat dari contoh citra menggunakan algoritma pengolahan citra yang dibangun. Hasil pengolahan citra digunakan sebagai input untuk membangun jaringan syaraf tiruan yang dimodelkan dengan 7 input dengan tingkat ketuaan dan kematangan sebagai output. Hasil penelitian menunjukkan bahwa dengan konstanta laju pembelajaran 0.6, konstanta momentum sebesar 0.5, nilai fungsi aktivasi 1 dan dilatih sampai 10000 iterasi serta 3 lapisan tersembunyi pada jaringan syaraf tiruan yang digunakan diperoleh tingkat keakuratan yang tinggi mencapai 97.89% dan 100% pada klasifikasi pepaya berdasarkan ketuaan dan kematangan .