Hermantoro .
Fakultas Teknologi Pertanian, Institut Pertanian Stiper

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APLIKASI PENGOLAHAN CITRA DIGITAL DAN JARINGAN SYARAF TIRUAN UNTUK MEMPREDIKSI KADAR BAHAN ORGANIK DALAM TANAH ., Hermantoro
Jurnal Keteknikan Pertanian Vol. 25 No. 1 (2011): Jurnal Keteknikan Pertanian
Publisher : PERTETA

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Abstract The objective of this research is to determine organic matter content in soil using image processing and artificial neural network. The images of soil were captured using digital camera and processed using image process algorithm. The images parameter data i.e. red, green, blue, hue, saturation, intensity, mean, entropy, energy, contrast, and homogeneity were extracted from sixty soil sample with different organic matter content. Parameter images data were used as the inputs data for ANN analysis. Output layer of ANN is organic matter content in soil. Based on experiment found that application of image processing and ANN for predicting organic matter content in soil have the high accuracy with coefficient determination of  90.75 % and mean square error (MSE) of 0.002762. Keywords : soil organic matter, images process, artificial neural network Abastrak Tujuan penelitian adalah menentukan kadar bahan organik dalam tanah menggunakan pengolahan citra digital dan jaringan syaraf tiruan. Citra tanah diambil menggunakan sebuah camera digital dan diolah menggunakan algoritma pengolahan citra. Parameter citra yang digunakan adalah : red, green, blue, saturasi, intensitas, rerata, entropi, energi, kontras, dan homogenitas diambil dari 60 contoh tanah dengan kadar bahan organik yang berbeda. Parameter citra tersebut digunakan sebagai data masukan dalam analisis ANN., sebagai lapisan keluaran dari ANN adalah kadar bahan organik dalam tanah. Berdasarkan hasil penelitian dan analisis pengolahan citra dan ANN dapat digunakan untuk emprediksi kadar bahan organik dalam tanah dengan akurasi tinggi dengan kooefisien determinasi 90,75% dan MSE 0,002761. Kata kunsi : bahan orgaik tanah, pengolahan citra, jaringan syaraf tiruan.Diterima: 12 Agustus 2010; Disetujui: 03 Januari 2011 
APLIKASI MODEL ARTIFICIAL NEURAL NETWORK TERINTEGRASI DENGAN GEOGRAPHYCAL INFORMATION SYSTEM UNTUK EVALUASI KESESUAIAN LAHAN PERKEBUNAN KAKAO ., Hermantoro; ., Rudiyanto; Suprayogi, Slamet
Jurnal Keteknikan Pertanian Vol. 22 No. 1 (2008): Jurnal Keteknikan Pertanian
Publisher : PERTETA

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Land evaluation for specific purpose in plantation sector become very important due to increasing the competition in land use and the development of plantation sector. Land evaluation produces information of land economic values for specific land use. The objective of the research is to develop land evaluation method for cocoa estate using integrated model Artificial Neural Network (ANN) and Geographical Information System (GIS). Back propagation ANN model were used to predict cocoa yield base on land qualities parameter. The result shows that the best of ANN model to predict cocoa yield have 15 input layer, 15 hidden layer, and 1 output layer. with the determination coefficient (r2) of 0.99 and Root Mean Square Error (RMSE) of 93.83 in the training process, otherwise in the testing found the r2of O. 76 and RMSE of 113.83. In verification stage the integrated model ofANN and GIS was used to evaluate land suitability of Wijayaarga Cocoa Plantation is seem accurate in predicting cocoa yield and easers to mapping the land suitability unit. Keyword: ANN, GIS, Land Evaluation, Cocoa Diterima: 04 Juni 2007; Disetujui: 18 September 2007