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dc.contributor.advisorKustiyo, Aziz
dc.contributor.authorDini, Alita Wulan
dc.date.accessioned2013-06-17T02:26:19Z
dc.date.available2013-06-17T02:26:19Z
dc.date.issued2013
dc.identifier.urihttp://repository.ipb.ac.id/handle/123456789/64109
dc.description.abstractShorea is a timber plant from the genus of meranti. Shorea belongs to Dipterocarpaceae family which has 194 species growing in tropical area. The species of Shorea is difficult to be identified because of it is similarity to each other. This research utilized Probabilistic Neural Network (PNN) to classify Shorea species. The parameters used for classification were area, perimeter, diameter, smooth factor, form factor, perimeter ratio of diameter, perimeter ratio of physiological length and physiological width with 0.1 bias value. It was found that the accuracy of the proposed method was 100% (without normalization) and 91% (with normalization). It can be concluded that the morphological features of area, perimeter, diameter, and ratio of leaf length and width significantly affect the accuracy.en
dc.subjectBogor Agricultural University (IPB)en
dc.subjectProbabilistic Neural Networken
dc.subjectMorphologyen
dc.subjectShoreaen
dc.titleIdentifikasi Daun Shorea Menggunakan Probabilistic Neural Network dengan Normalisasi Fitur Morfologi Daunen


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