Please use this identifier to cite or link to this item: http://repository.ipb.ac.id/handle/123456789/68532
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dc.contributor.advisorHerdiyeni, Yeni
dc.contributor.advisorRauf, Aunu
dc.contributor.authorTrio, Alrasyid
dc.date.accessioned2014-04-11T03:21:03Z
dc.date.available2014-04-11T03:21:03Z
dc.date.issued2014
dc.identifier.urihttp://repository.ipb.ac.id/handle/123456789/68532
dc.description.abstractDisease in cabbage leaves can lead to a decrease in the quality and quantity of crop yields or crop failure. The purpose of this research is to develop a mobile application by applying the Fast Fourier Transform feature extraction technique and Probabilistic Neural Network classification technique to identify the cabbage leaf disease. This mobile application runs on android platform. It is found that the accuracy of FFT feature extraction and PNN classification with bias 0.03 is 58.33%. The obtained accuracy is still not satisfactory since there are some errors in identifying the cabbage leaf diseaseen
dc.language.isoid
dc.titleAplikasi Mobile Identifikasi Penyakit Daun Kubis dengan Fast Fourier Transform dan Probabilistic Neural Networken
dc.subject.keywordProbabilistic Neural Networken
dc.subject.keywordFast Fourier Transformen
dc.subject.keywordCabbage leaf diseaseen
Appears in Collections:UT - Computer Science

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