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: Prediksi Nilai Crop Variable Tanaman Padi pada Data Hyperspectral Menggunakan Metode Support Vector Machine.
Abstrak: Hyperspectral is a new technology in remote sensing which exploits hundred of bands. Pusat Teknologi Inventarisasi Sumber Daya Alam Badan Pengkajian dan Penerapan Teknologi (PTISDA BPPT) applies hyperspectral in agriculture for yearly yield prediction. In this research, Leaf Area Index (LAI), number of chlorophyl (SPAD), and yearly yield has been predicted with hyperspectral data using support vector machine (SVM) method. SVM currently aroused many attentions due to its robustness in dealing with data with high dimensionality, including hyperspectral data. In principle, SVM works as a binary classifier to find the best separating hyperplane in the feature space. By modifying its loss function, SVM can be applied to regression problem in hyperspectral and Hymap hyperspectral remotely sensed data Region used are Indramayu and Subang; the growth period’s of paddy are vegetative, reproductive, and ripening, while the height’s of the spectral acquisition are 10 cm, 50 cm, and Hymap (2000 m). The data is owned PTISDA BPPT in cooperation with ERSDAC Japan. This research got prediction R2 maximum for LAI 0.96, SPAD 0.55, and yield 0.485. By using SVM, LAI values can be predicted quite well, but not for the SPAD values.This could be due to measurement error in the instrument of SPAD, while error’s of yield are probably due to measurement which using the example of a small area to represent a large area.
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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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