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<http://eprints.uny.ac.id/id/eprint/27534> <http://purl.org/dc/terms/title> "Model Radial Basis Function Neural Netwok (RBFNN) \r\nUntuk Klasifikasi Stadium Kanker Kolorektal"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://eprints.uny.ac.id/id/eprint/27534> <http://purl.org/ontology/bibo/abstract> "Artificial neural network atau yang lebih dikenal dengan neural network (NN) yang dalam bahasa indonesia berarti jaringan saraf tiruan merupakan sistem pengolahan informasi yang memiliki karakteristik menyerupai jaringan saraf biologis. Salah satu metode NN adalah Radial Basis Function Neural Network (RBFNN). RBFNN adalah model dari NN yang pada lapisan tersembunyinya menggunakan fungsi aktivasi basis. Pada penelitian ini dideskripsikan mengenai prosedur dan hasil ketepatan metode RBFNN untuk klasifikasi stadium kanker kolorektal. \r\nProsedur pemodelan RBFNN untuk klasifikasi stadium kanker kolorektal adalahekstraksicitra, pendefinisianvariabelinput dan target, pembagian data menjadi data training dantesting, normalisasi data, pembelajaran RBFNN menggunakank-means clustering, fungsiaktivasi Gaussian, danglobalridge regressionuntukmencaribobot optimal, hinggapadaakhirnyadidapatkanhasilklasifikasi. Ekstraksi citra dengan metodeGray Level Co-occurrence Matrix (GLCM)untukmendapatkan parameter yang digunakansebagaiinput.Input berasal dari 14 parameter hasil ekstraksi,sedangkan target berasaldaridiagnosamasing-masingfotocolonoscopykolorektal. Pembagian data menggunakan75% data training dan 25% data testing. Normalisasi data dilakukan karena data inputmempunyaibesaranpengukuran yang berbeda – beda. K-means clustering digunakan untuk menentukan pusat dan jarak yang akan digunakan pada lapisan tersembunyi. Banyak neuron tersembunyiditentukan dengan metode trial and error. Untuk mencari bobot optimal, digunakan metode kuadrat terkecil dengan ridge regression guna meminimalkan eror sehingga didapat akurasi yang lebih baik. \r\nBerdasarkan prosedur pemodelan RBFNN, diperoleh model RBFNN terbaik dengan 14 variabel input, 8 neuron tersembunyi, dan 4 variabel target. Model RBFNN untuk klasifikasi stadium kanker kolorektal menghasilkan nilai sensitivitas, spesifisitas, dan akurasi masing masing 88,23%, 100%, dan 86,96% untuk data training serta 88,23%, 100%, dan 81,82% untuk  data testing.\r\n\r\n\r\nKata Kunci : neural network, radial basis function (RBFNN), klasifikasi, kanker kolorektal"^^<http://www.w3.org/2001/XMLSchema#string> .
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