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.
Keyword: 

Judul: Prediksi LAI, SPAD, dan Yield Padi Menggunakan Data Hyperspectral dengan Algoritme Partial Least Square Regression (PLSR)
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 chlorophyll (SPAD), and yield paddy yearly has been predicted with data hyperspectral using partial least square regression (PLSR) algorithm. Region used are Indramayu and Subang; the growth periods of paddy are vegetative, reproductive and ripening, while the heights 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 predicted R2 maximum for LAI 0.9500, SPAD 0.5262, and yield 0.4921. By using PLSR, LAI values can be predicted quite well, but not for the SPAD values and yield. This could be due to measurement error in the instrument of SPAD, while errors of yield are probably due to using a small area to represent a large area.
Keyword: 

Judul: Katalis Zink Ferit Tersubstitusi Cu untuk Oksidasi Benzil Alkohol
Abstrak: Katalis berperan dalam menurunkan energi aktivasi. Katalis ZnFe2O4 dan ZnFe2O4 tersubstitusi Cu disintesis menggunakan metode kopresipitasi untuk digunakan dalam oksidasi benzil alkohol. Katalis dicirikan dengan spektrofotometer inframerah (FTIR) dan difraktometer sinar-X. Spektrum FTIR menunjukkan puncak serapan dari ikatan Zn-O dan Fe-O masing-masing pada rentang 559‒575 cm-1 dan 419‒447 cm-1. Difraktogram menunjukkan terbentuknya fase tunggal Zn0.6Cu0.4Fe2O4 pada katalis ZnFe2O4 tersubstitusi Cu 20%. Benzil alkohol dioksidasi dengan sistem refluks. Konversi benzil alkohol meningkat dengan meningkatnya suhu. Kadar benzaldehida yang dihasilkan dengan katalis ZnFe2O4 tersubstitusi Cu 20% lebih tinggi dibandingkan dengan katalis ZnFe2O4 yang tidak tersubstitusi dan yang tersubstitusi Cu 60 dan 80%. Hal tersebut menunjukkan bahwa katalis ZnFe2O4 tersubstitusi Cu 20% memiliki kinerja yang lebih baik dalam oksidasi benzil alkohol menjadi benzaldehida dengan kadar benzaldehida 8.763%.
Keyword: Benzil alkohol, katalis, oksidasi, ZnFe2O4