Judul: Identifikasi penyakit tanaman menggunakan fast fourier transform dan local binary pattern dengan probabilistic neural network (studi kasus tanaman Anthurium dan padi)
Abstrak: Plant diseases can result in death and decreased quality and quantity of agricultural products that are economically significant to cause losses for farmers. This research proposes a new system to identify plant disease automatically based on plant leaf image. Fast Fourier Transform (FFT) and Local Binary Patterns (LBP) are used to extract the features. There are nine features had been resulted by FFT i.e. mean, variance, different value of maximum and minimum levels, different value of maximum and mean levels, standard deviation, skewness, kurtosis, entropy, and the highest pixel value. Then these features are combined and classified using Probabilistic Neural Network. The result shows us that these features can be used to identify plants disease, best accuracy to indentify plants disease is 85.56%. The proposed system is promising since it is capable to identify disease plants species efficiently and accurately.
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Judul: pengukuran kinerja k-nearest neighbors dan self organizing maps menggunakan fast fourier transform untuk identifikasi penyakit tanaman (studi kasus : tanaman padi dan anthurium)
Abstrak: Plant diseases can reduce the plant sale price, resulting in losses on farmers especially in the economic field. Eradication of the diseases has been done, but determining the type of disease is still difficult. This research proposed a new study on measuring for automatically identification of plant disease based on leaf image with the input of digital image. Detection process started with feature extraction using Fast Fourier Transform and the identification process using the k-Nearest Neighbor (kNN) and Self Organizing Maps (SOM). This study, used the image which consists of six hundred pieces of images, that divided into six classes consisting of 70% for train data and 30% for test data. The experimental result showed that identification of plant disease using k-NN is better than that of SOM. The accuracy achieved 76% and 62% for k-NN and SOM respectively.
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Judul: Sintesis Superkonduktor YBCO dengan Metode Evaporasi dan Karakterisasinya
Abstrak: Telah berhasil dibuat pelet superkonduktor YBCO-123 dengan metode evaporasi dengan menghasilkan Tc = 100 K. Sintesis dilakukan dengan mencampur garam-garam nitrat Y(NO3)2, Ba(NO3), Cu(NO3)2 di dalam pelarut H2O pada suhu 150 oC. Setelah melalui proses pirolisis pada 350 oC selama 1 jam, prekursor dikalsinasi pada 900 oC selama 4 jam kemudian serbuk dikompaksi dengan tekanan hidrolik 5 ton untuk membentuk pelet dengan diameter 1,5 cm dan tebal 0,24 mm. Sintering dilakukan pada suhu 940 oC selama 20 jam. Efek meissner menunjukkan fenomena levitasi pada temperatur sekitar 80 K. Hal ini menunjukkan fasa superkonduktor telah terbentuk. Data XRD memberi mengkonfirmasi bahwa fasa kristal 123 telah terbentuk. Nilai konduktivitas didapat dari hasil pengukuran dengan LCR pada temperatur 300 K sebesar 4 x 10-5 S/cm. Pada penurunan temperatur dari 100 K ke 80 K terjadi kenaikan nilai konduktansi yang tajam dari 4.2 x 10-4 S/cm ke nilai 1.309 x 10-1 S/cm.
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