<> <http://www.w3.org/2000/01/rdf-schema#comment> "The repository administrator has not yet configured an RDF license."^^<http://www.w3.org/2001/XMLSchema#string> .
<> <http://xmlns.com/foaf/0.1/primaryTopic> <http://eprints.uny.ac.id/id/eprint/41329> .
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<http://eprints.uny.ac.id/id/eprint/41329> <http://purl.org/dc/terms/title> "PERBANDINGAN K-MEANS DAN FUZZY C-MEANS CLUSTERING\r\nPADA MODEL RADIAL BASIS FUNCTION NEURAL NETWORK\r\n(RBFNN) UNTUK KLASIFIKASI STADIUM KANKER PAYUDARA"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://eprints.uny.ac.id/id/eprint/41329> <http://purl.org/ontology/bibo/abstract> "Kanker payudara adalah kanker yang paling sering terjadi pada wanita\r\nbaik di negara maju dan berkembang. Oleh karena itu, pemeriksaan deteksi dini\r\nperlu dilakukan agar kanker payudara dapat disembuhkan. Radial Basis Function\r\nNeural Network (RBFNN) merupakan salah satu model Neural Network (NN)\r\nyang dapat digunakan untuk mendiagnosa kanker payudara. Penelitian ini\r\nbertujuan untuk menjelaskan prosedur dan membandingan hasil klasifikasi\r\nstadium kanker payudara menggunakan model RBFNN dengan metode K-Means\r\ndan Fuzzy C-Means clustering.\r\nProsedur awal klasifikasi kanker payudara menggunakan RBFNN adalah\r\nmelakukan preprocessing citra berupa cropping dan penghilangan background\r\nhitam pada citra mammogram payudara. Setelah itu dilakukan ekstraksi citra\r\nmammogram untuk mendapatkan parameter statistik, yaitu energi, kontras,\r\nkorelasi, sum of squares, inverse difference moment, sum average, sum variance,\r\nsum entropy, entropi, difference variance, difference entropy, probabilitas\r\nmaksimum, homogenitas dan dissimilarity. Variabel input yang digunakan adalah\r\nparameter-parameter hasil ekstraksi citra menggunakan Gray Level Co-occurance\r\nMatrix (GLCM), sedangkan target dan variabel output berupa vektor yang\r\nmewakili masing-masing stadium kanker payudara. Pembagian data menjadi dua,\r\nyaitu 80% data training dan 20% data testing. Sebelum dilakukan pembelajaran,\r\ninput dinormalisasi. Pembentukan model terbaik dilakukan dengan menentukan\r\nbanyaknya neuron terbaik pada lapisan tersembunyi yang menghasilkan nilai\r\nakurasi tertinggi dari kedua metode clustering yang digunakan dan menentukan\r\nbobot-bobotnya menggunakan kriteria GCV (Generalised Cross-Validation) pada\r\nGlobal Ridge Regression. Fungsi aktivasi yang digunakan adalah fungsi Gaussian\r\npada lapisan tersembunyi.\r\nKlasifikasi stadium kanker payudara menggunakan model RBFNN\r\nmenghasilkan jaringan terbaik dengan 14 neuron input, lapisan tersembunyi\r\ndengan 11 neuron, dan 2 neuron output. Pengujian ketepatan model RBFNN\r\nuntuk klasifikasi diukur melalui nilai sensitivitas, spesifisitas, dan akurasi. Hasil\r\nmetode K-Means clustering dengan nilai sensitivitas, spesifisitas, dan akurasi\r\nsecara berurutan adalah 75%, 93,75%, dan 80,21% untuk data training dan\r\n81,25%, 75%, dan 79,17% untuk data testing, lebih baik daripada hasil metode\r\nFuzzy C-Means (FCM) clustering yaitu 67,19%, 90,62%, dan 75% untuk data\r\ntraining dan 75%, 75%, dan 75% untuk data testing.\r\nKata Kunci : Radial Basis Function Neural Nework (RBFNN), K-Means cluster,\r\nFuzzy C-Means cluster, klasifikasi, kanker payudara"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://eprints.uny.ac.id/id/eprint/41329> <http://purl.org/dc/terms/date> "2016-07-14" .
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<http://eprints.uny.ac.id/41329/40/preview.jpg> <http://www.w3.org/2000/01/rdf-schema#label> "preview.jpg"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://eprints.uny.ac.id/id/eprint/41329> <http://eprints.org/ontology/hasDocument> <http://eprints.uny.ac.id/id/document/333805> .
<http://eprints.uny.ac.id/id/document/333805> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://eprints.org/ontology/Document> .
<http://eprints.uny.ac.id/id/document/333805> <http://www.w3.org/2000/01/rdf-schema#label> "PERBANDINGAN K-MEANS DAN FUZZY C-MEANS CLUSTERING\r\nPADA MODEL RADIAL BASIS FUNCTION NEURAL NETWORK\r\n(RBFNN) UNTUK KLASIFIKASI STADIUM KANKER PAYUDARA (Other)"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://eprints.uny.ac.id/id/document/333805> <http://eprints.org/relation/isVersionOf> <http://eprints.uny.ac.id/id/document/333752> .
<http://eprints.uny.ac.id/id/document/333805> <http://eprints.org/relation/isVolatileVersionOf> <http://eprints.uny.ac.id/id/document/333752> .
<http://eprints.uny.ac.id/id/document/333805> <http://eprints.org/relation/ismediumThumbnailVersionOf> <http://eprints.uny.ac.id/id/document/333752> .
<http://eprints.uny.ac.id/id/document/333805> <http://eprints.org/ontology/hasFile> <http://eprints.uny.ac.id/41329/41/medium.jpg> .
<http://eprints.uny.ac.id/id/document/333805> <http://purl.org/dc/terms/hasPart> <http://eprints.uny.ac.id/41329/41/medium.jpg> .
<http://eprints.uny.ac.id/41329/41/medium.jpg> <http://www.w3.org/2000/01/rdf-schema#label> "medium.jpg"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://eprints.uny.ac.id/id/eprint/41329> <http://eprints.org/ontology/hasDocument> <http://eprints.uny.ac.id/id/document/333806> .
<http://eprints.uny.ac.id/id/document/333806> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://eprints.org/ontology/Document> .
<http://eprints.uny.ac.id/id/document/333806> <http://www.w3.org/2000/01/rdf-schema#label> "PERBANDINGAN K-MEANS DAN FUZZY C-MEANS CLUSTERING\r\nPADA MODEL RADIAL BASIS FUNCTION NEURAL NETWORK\r\n(RBFNN) UNTUK KLASIFIKASI STADIUM KANKER PAYUDARA (Other)"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://eprints.uny.ac.id/id/document/333806> <http://eprints.org/relation/isVersionOf> <http://eprints.uny.ac.id/id/document/333752> .
<http://eprints.uny.ac.id/id/document/333806> <http://eprints.org/relation/isVolatileVersionOf> <http://eprints.uny.ac.id/id/document/333752> .
<http://eprints.uny.ac.id/id/document/333806> <http://eprints.org/relation/issmallThumbnailVersionOf> <http://eprints.uny.ac.id/id/document/333752> .
<http://eprints.uny.ac.id/id/document/333806> <http://eprints.org/ontology/hasFile> <http://eprints.uny.ac.id/41329/42/small.jpg> .
<http://eprints.uny.ac.id/id/document/333806> <http://purl.org/dc/terms/hasPart> <http://eprints.uny.ac.id/41329/42/small.jpg> .
<http://eprints.uny.ac.id/41329/42/small.jpg> <http://www.w3.org/2000/01/rdf-schema#label> "small.jpg"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://eprints.uny.ac.id/id/eprint/41329> <http://www.w3.org/2000/01/rdf-schema#seeAlso> <http://eprints.uny.ac.id/41329/> .
<http://eprints.uny.ac.id/41329/> <http://purl.org/dc/elements/1.1/title> "HTML Summary of #41329 \n\nPERBANDINGAN K-MEANS DAN FUZZY C-MEANS CLUSTERING&#13;\nPADA MODEL RADIAL BASIS FUNCTION NEURAL NETWORK&#13;\n(RBFNN) UNTUK KLASIFIKASI STADIUM KANKER PAYUDARA\n\n" .
<http://eprints.uny.ac.id/41329/> <http://purl.org/dc/elements/1.1/format> "text/html" .
<http://eprints.uny.ac.id/41329/> <http://xmlns.com/foaf/0.1/primaryTopic> <http://eprints.uny.ac.id/id/eprint/41329> .
<http://eprints.uny.ac.id/id/subject/F4> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2004/02/skos/core#Concept> .
<http://eprints.uny.ac.id/id/subject/F4> <http://www.w3.org/2004/02/skos/core#prefLabel> "Matematika"@en .
<http://eprints.uny.ac.id/id/eprint/41329> <http://purl.org/dc/terms/subject> <http://eprints.uny.ac.id/id/subject/F4> .