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Analisis Kinerja Support Vector Machine (SVM) Dan Probabilistic Neural Network (PNN) Pada Sistem Identifikasi Tumbuhan Obat Dan Tanaman Hias Berbasis Citra

dc.contributor.advisorHerdiyeni, Yeni
dc.contributor.advisorAnnisa
dc.contributor.authorWidyawati, Dewi Kania
dc.date.accessioned2012-09-24T03:06:35Z
dc.date.available2012-09-24T03:06:35Z
dc.date.issued2012
dc.identifier.urihttp://repository.ipb.ac.id/handle/123456789/57499
dc.description.abstractThis research analyzed perfomance of clasification for plant identification using Support Vector Machine (SVM) and Probabilistic Neural Network (PNN). In this research we used kernel linear, polynomial and RBF for clasifier SVM. In thus research, we use 1.440 medicinal plant images and 300 house plant images belong to 30 are extracted using Fuzzy Local Binary Patern based on texture feature.The expermental result shows that SVM kernel polynomial is superior compare to PNN with accuracy 73.57% to medicinal plant identification and SVM kernel RBF is superior compare to PNN with accuracy 81.11% to house plant identification. The proposed system is promising to improve identification medicinal plant and house plant.en
dc.subjectsupport vector machineen
dc.subjectprobabilistic neural networken
dc.subjectfuzzy local binary paternen
dc.subjectmedicinal plant identificationen
dc.subjecthouse plant identificationen
dc.titlePerfomance Analysis of Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) on Identification System of Medicinal Plant and House Plant Based on Imageen
dc.titleAnalisis Kinerja Support Vector Machine (SVM) Dan Probabilistic Neural Network (PNN) Pada Sistem Identifikasi Tumbuhan Obat Dan Tanaman Hias Berbasis Citra


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