Judul: Klasifikasi Protein Family Menggunakan Algoritme Probabilistic Neural Network (PNN)
Abstrak: Proteins are composed of series and combination of 20 - amino acid. Each Protein has the composition of the amino - acid sequence which is different one from another. The classification of Protein is contributed as a guide to determine the structure, activity and role in the metabolism of organism. Since necessary to classifying proteins into appropriate classes, new sequences needed. Since the number of sequences that appear, new method of classifying proteins into appropriate classes. The purpose of this study is to apply the algorithm Probabilistic Neural Network ( PNN ) with four bias to classify the protein family. There are three classes of protein family data, namely class 1 - cysPrx_C , 4HBT and ABC_Tran. Each class is taken as 100 Data.
Keyword: Bogor Agricultural University (IPB), Protein Family., Feature Extraction Markov Chain, Probabilistic Neural Network (PNN), classification

Judul: Klasifikasi Fragmen Metagenom Menggunakan Metode Probabilistic Neural Network (PNN)
Abstrak: Teknologi pada bidang bioinformatika terus berkembang, khususnya untuk analisis metagenom. Metagenom merupakan komunitas mikrob genom yang sampelnya diambil langsung dari lingkungan. Fragmen metagenom yang dihasilkan dari pengambilan sampel masih bercampur antar-organisme. Tujuan penelitian ini adalah melakukan klasifikasi fragmen metagenom ke dalam takson genus. Penelitian ini menggunakan metode Probabilistic Neural Network (PNN) dengan ekstraksi fitur Gray Level Co-occurence Matrix (GLCM). Penelitian dilakukan pada panjang fragmen 200 bp, 400 bp, 800 bp, dan 1 Kbp. Data yang digunakan sebanyak 100 organisme dalam variasi 10 genus dengan 5-fold cross validation. Untuk mendapatkan nilai fitur fragmen dibutuhkan formulasi 8 fitur persamaan haralick. Setelah hasil fitur didapat, kemudian diklasifikasi dengan metode PNN. Dari klasifikasi PNN, diperoleh akurasi pada rentang 92.8%-93.6% berdasarkan panjang fragmen. Nilai spesifisitas pada keempat panjang fragmen mencapai 96%. Berbeda dengan spesifisitas, nilai sensitivitas yang diperoleh sekitar 64%-68%. Selisih akurasi, sensitivitas, dan spesifisitas pada panjang 200 bp dan 1 Kbp tidak jauh berbeda. Dari hasil tersebut, terdapat kecenderungan bahwa semakin panjang fragmen yang digunakan maka akurasi semakin meningkat.
Keyword: GLCM, metagenome, PNN

Judul: Elektrodeposisi Film Tipis Semikonduktor Cu2ZnSnS4 (CZTS) pada Substrat Kaca ITO
Abstrak: Semiconductor Cu2ZnSnS4 (CZTS) thin film that can be used as absorber layers on solar cell module was made via electrodeposition method. CZTS thin film was electrodeposited on the surface of a silicate glass coated with a thin layer of indium tin oxide (ITO). Deposition process was done using potentiostatic mode at room temperature with a potential of 1.05 V for 30, 45, and 60 minutes, followed with annealing the film at 2 different temperatures, 500 ºC using furnace and 180 ºC using hot plate, both treatments for 45 minutes in N2/H2S atmosphere. The X-ray difractograms were analyzed using Match 2 program and showed that the CZTS thin films had kesterit crystalline structure, with a low percentage of CZTS phase. The thin film also contained secondary phases such as Cu2S, ZnS, and SnS. The obtained CZTS thin films had a thickness between 1.039 and 1.676 μm. Absorption study showed that the band gap energy of CZTS thin films which was annealed using furnace ranged from 1.50 to 1.52 eV, whereas that annealed using the hot plate was not observed. The band gap energy value showed that CZTS thin films can be used as sunlight absorber layers on solar cell. The formation of CZTS crystal film on this investigation was effectively performed using furnace annealing process at a temperature of 500 ºC with deposition time of 45 minutes.
Keyword: Thin film, Electrodeposition, Cu2ZnSnS4