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Full metadata record
DC Field | Value | Language |
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dc.contributor.advisor | Herdiyeni, Yeni | |
dc.contributor.advisor | Rauf, Aunu | |
dc.contributor.author | Trio, Alrasyid | |
dc.date.accessioned | 2014-04-11T03:21:03Z | |
dc.date.available | 2014-04-11T03:21:03Z | |
dc.date.issued | 2014 | |
dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/68532 | |
dc.description.abstract | Disease 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 disease | en |
dc.language.iso | id | |
dc.title | Aplikasi Mobile Identifikasi Penyakit Daun Kubis dengan Fast Fourier Transform dan Probabilistic Neural Network | en |
dc.subject.keyword | Probabilistic Neural Network | en |
dc.subject.keyword | Fast Fourier Transform | en |
dc.subject.keyword | Cabbage leaf disease | en |
Appears in Collections: | UT - Computer Science |
Files in This Item:
File | Description | Size | Format | |
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G14atr.pdf Restricted Access | full text | 1.18 MB | Adobe PDF | View/Open |
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