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: Prediksi Crop Variabel Tanaman Padi Pada Data Hyperspectral Menggunakan Algoritme Backpropagation Neural Network
Abstrak: Hyperspectral is a new technology in remote sensing which 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. In this research, Leaf Area Index (LAI), number of chlorophyll (SPAD), and yearly paddy yield has been predicted with hyperspectral data using backpropagation neural network (BPNN) algorithm. The regions 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 was obtained in cooperation between PTISDA BPPT and ERSDAC Japan. In this study, three test procedures were conducted using backpropagation neural network algorithm, where the first and second procedures used Weka to predict the R2 and RMSE of LAI, SPAD, and yield. Prediction results show high accuracy for LAI but not for SPAD and yield. The third procedure used IDL for prediction yield RMSE, with a satisfactory result : an error value of 0.10153786.
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Judul: Desain Gerbang Logika Kuantum Fredkin Multi- Kontrol Berbasis Gerbang Toffoli Melalui Superposisi Gerbang Elementer
Abstrak: Gerbang Fredkin dapat dimanfaatkan sebagai sel pembangun rangkaian fulladder untuk proses komputasi. Karena sifatnya yang reversibel, gerbang tersebut dapat dibuat dari sususan gerbang-gerbang elementer dengan basis gerbang Toffoli. Pada penelitian ini, penulis mencari desain dari gerbang Fredkin multi-kontrol berbasis gerbang Toffoli dengan mengkombinasikan tiga jenis gerbang elementer, yaitu CNOT, CV, dan CV†. Penelitian ini juga memperhitungkan jumlah gerbang elementer yang digunakan (quantum cost) dan jalur tambahan (ancillae input) dari desain-desain gerbang Fredkin yang dihasilkan. Dari hasil penelitian ditunjukkan bahwa gerbang Fredkin multi-kontrol dapat dibentuk oleh susunan tiga gerbang Toffoli multi-kontrol. Kemudian, gerbang-gerbang tersebut dapat direkonstruksi menjadi susunan gerbang-gerbang elementernya. Jumlah quantum cost dan ancillae input akan semakin besar seiring bertambahnya jumlah kontrol yang digunakan.
Keyword: Gerbang Fredkin multi-kontrol, gerbang Toffoli multi-kontrol, gerbang elementer