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<http://digilib.unila.ac.id/id/eprint/57012> <http://purl.org/dc/terms/title> "IDENTIFIKASI BUAH PISANG BERDASARKAN KARAKTERISTIK \r\nMORFOLOGI BUAH DENGAN METODE JARINGAN SYARAF TIRUAN"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/eprint/57012> <http://purl.org/ontology/bibo/abstract> "Pisang (Musa paradisiaca) merupakan salah satu komoditas hortikultura dari jenis \r\nbuah buahan. Indonesia sebagai negara berkembang dikenal menjadi salah satu \r\npusat keanekaragaman pisang. Tingginya keanekaragaman ini memungkinkan \r\nmasyarakat Indonesia untuk memilih dan memanfaatkan jenis pisang yang \r\ndiinginkan sesuai dengan kebutuhan. Spesies dan kultivar pisang yang ditemukan \r\ndi Indonesia belum semuanya diklasifikasikan. Identifikasi diperlukan dalam \r\nmengenali perbedaan sifat-sifat pada setiap jenis buah pisang. \r\nPenelitian ini bertujuan mengukur parameter-parameter morfologi buah pisang \r\nMuli, Ambon, Kepok sebagai parameter klasifikasi buah pisang dan identifikasi \r\nbuah pisang Muli, Ambon, dan Kepok menggunakan model Jaringan Saraf Tiruan \r\n(JST). Dalam penelitian ini digunakan Jaringan Syaraf Tiruan tipe\r\nbackpropagation dengan metode pelatihan terawasi (supervised learning). \r\nPerbandingan data yang digunakan dalam membangun model dan pengujian \r\nmodel yaitu 60% : 40% dan 70% : 30% dari keselurulan jumlah data sebanyak \r\n150 data set. \r\nHasil penelitian menunjukkan bahwa model Jaringan Syaraf Tiruan dapat \r\ndigunakan untuk identifikasi morfologi buah pisang Muli, Ambon dan Kepok. \r\nPada perbandingan data uji dan data latih 60% : 40% tipe model yang \r\nmemberikan hasil identifikasi terbaik yaitu purelin-tansig-logsig dengan nilai \r\nRMSE sebesar 0,0074 dan R2\r\nsebesar 1. Sedangkan model Jaringan Syaraf Tiruan \r\nuntuk identifikasi morfologi buah pisang Muli, Ambon dan Kepok pada \r\nperbandingan data uji dan data latih 70% : 30% yang memberikan hasil \r\nidentifikasi terbaik yaitu purelin-logsig-logsig dengan nilai RMSE sebesar 0,0060 \r\ndan R2\r\nsebesar 1. Akurasi hasil prediksi Jaringan Syaraf Tiruan adalah 100%. Hal \r\nini menunjukkan bahwa model prediksi yang dibangun melalui arsitektur jaringan \r\n8 input 2 hidden layer dan 1 output node tersebut akurat dalam mengidentifikasi \r\nbuah pisang Muli, Ambon, dan Kepok berdasarkan karakteristik morfologi yaitu; \r\nberat, volume, luas rata-rata irisan buah, kebundaran rata-rata irisan buah, R (red) \r\nrata-rata, G (green) rata-rata, B (blue) rata-rata dan diameter rata-rata irisan buah \r\nsebagai variable masukan untuk model Jaringan Syaraf Tiruan.\r\nKata kunci : Identifikasi, Morfologi, Buah pisang Muli, Buah pisang Ambon, \r\nBuah pisang Kepok, Jaringan Syaraf Tiruan.\r\nBanana (Musa paradisiaca) is one of the horticulture commodity from many \r\nkinds of fruits. Indonesia as a developing country is known as the center of \r\nbanana diversity. The high in diversity gives possibility for Indonesian to choose \r\nand to utilize any kind of bananas that are suitable to their needs. Not all of the \r\nspecies and banana cultivars found in Indonesia are already classified. The \r\nidentification is needed to recognize the different properties of any type of \r\nbanana.\r\nThis study aims to measure the morphological parameters of Muli, Ambon, and \r\nKepok bananas as the parameters for classification and identification of Muli, \r\nAmbon, and Kepok bananas fruit using Artificial Neural Network (ANN) models \r\nbased. In this research, the backpropagation Artificial Neural Network was \r\ndeveloped and implemented using supervised learning method. The groups of data \r\nused to develop the model and the testing are 60% : 40% and 70% : 30%, \r\nrespectively. The total of sample used is 150 data sets. \r\nThe results showed that the Artificial Neural Network model developed can be \r\nused to identify and classify the morphological of Muli, Ambon and Kepok \r\nbananas. At the data set of testing and training of 60%: 40%, respectively the type \r\nof Artificial Neural Network model that gave best identification results is purelin�tansig-logsig with RMSE value of 0.0074 and R2\r\nof 1. While at the composition \r\ndata sets of 70% for training and 30% for testing, the type of Artificial Neural \r\nNetwork model gives best result is purelin-logsig-logsig with RMSE value of \r\n0.0060 and R2\r\nequal to 1. The accuracy of prediction using the developed model is \r\n100%. The results showed that the prediction model built using 8 inputs 2 hidden \r\nlayers and 1 output node as network architecture in Artificial Neural Network is\r\naccurate to identify the type of banana (Muli, Ambon, and Kepok) based on \r\nmorphology characteristics such as weight, volume, average area of fruit slices, \r\naverage roundness of fruit slices, average R (red), average G (green), average B \r\n(blue) and average diameter of fruit slices as input variables for Artificial Neural \r\nNetwork model.\r\nKeywords : Identification, Morphology, Muli banana, Ambon banana, Kepok \r\nbanana, Artificial Neural Network\r\n"^^<http://www.w3.org/2001/XMLSchema#string> .
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