Judul: Seleksi Hyperspectral Band Menggunakan Recursive Feature Elimination untuk Prediksi Produksi Padi dengan Support Vector Regression
Abstrak: Hyperspectral is a new technology in remote sensing that exploits hundreds of bands. Pusat Teknologi Inventarisasi Sumber Daya Alam Badan Pengkajian dan Penerapan Teknologi (PTISDA BPPT) applies hyperspectral in agriculture for yearly yield prediction. Hyperspectral images consist of large number of bands that require analysis to select features. One approach to reduce computational cost is to eliminate bands that do not add value to the regression and analysis method to be applied. In this research, we use a Recursive Feature Elimination (RFE) algorithm that is tailored to operate with Support Vector Regression (SVR) to perform band selection and regression simultaneously. Annual yield of paddy has been predicted with hyperspectral data using Support Vector Regression (SVR) algorithm. Regions used are Indramayu and Subang, and the altitude of the spectral acquisition is 2000 m (Hymap). This data is owned by PTISDA BPPT. RFE-SVR used in hyperspectral data was able to reduce about 30% of the bands, resulting in 70 bands out of 109 original bands with an RMSE value of 0.0901 and R2 value of 0.9874. Radial Basis Function (RBF) is the best kernel used in RFE-SVR having an RMSE value less than those of other kernels tested.
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Judul: Feature Selection Data Hiperspektral untuk Prediksi Produktivitas Padi dengan Algoritme Genetika dan Support Vector Regression
Abstrak: that of multispectral. However, the high-dimensional feature of hyperspectral data often cause the risk of “over fitting” when analyzed. Therefore, it is necessary to reduce the dimension of hyperspectral data, which can be done by feature selection. In this study, Genetic Algorithm (GA) and Support Vector Regression (SVR) are used to select the best band as the best feature of hyperspectral data for predicting productivity of paddy. GA selects the best band, parameter C and parameter γ for using in SVR. Parameter C is used as error penalty in SVR and parameter γ is used for kernel function in SVR. SVR uses the best band and the best parameter, which both of them are selected by GA to predict yield of paddy, then give fitness value for GA. Hyperspectral data that used in this research has been taken on June 2008 in Indramayu and Subang, while the heights of the spectral acquisition is 2000 m which is called hymap data. GA-SVR method was implemented using IDL and LIBSVM in C languange. The result shows that the GA-SVR method could select the best band and decrease the error of the prediction. The best result was shown by Hymap data using RBF kernel. The number of band usage for prediction was reduced from 109 to 48, the RMSE was reduced from 0.301 to 0.017 and the R2 = 0.99.
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Judul: Trichomonas Gallinae Penyebab Trichomonosis Pada Unggas
Abstrak: Trichomonas gallinae adalah protozoa yang bergerak aktof dengan keempat flagella yang mempunyai layar dan pada salah satu sisinya terdapat membran seperti sirip ikan yang berombak. Spesies dari genus Trichomonas ini mempunyai arti penting pada unggas, karena dapat menyebabkan infeksi pada alat pencernaan bagian atas.
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