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: Klasifikasi Fragmen Metagenome Menggunakan KNN dan PNN dengan Ekstraksi Fitur Gray Level Co-occurrence Matrix (GLCM) pada Variasi Panjang Fragmen
Abstrak: Bioinformatics is a field of study which is developing rapidly in Indonesia. The main focus of this study is to classify metagenome fragment into some taxonomies using GLCM that has 13 features. The training data used in the classification process are 50 organisms with 5-fold cross validation. The DNA combination sequences inside a fragment can be seen as a 1xN image, where N is the fragment’s length in two-dimension’s matrix. The result from those features will be classified using PNN and KNN. The research shows that the accuracy percentage with all lengths variety, including 200 bp, 1 Kbp, 3 Kbp, and 10 Kbp, are 100%. It can be stated that the variety fragment’s length does not affect the accuracy. In addition, it can be concluded that GLCM feature extraction method can be prospectively implemented for classifying metagenome fragment.
Keyword: Bogor Agricultural University (IPB), PNN, KNN, GLCM, Fragment

Judul: The Effect Cassava Leaves Meal in The Diets on Body Weigbt, Percentage of Digestive Tract and Viscera of Mule Duck.
Abstrak: The mule ducks are the crossbreed between common duck females (Anas plathyrhynchos) and muscovy ducks (Cairino moschata) to improve the meat production in order to meet the animal protein requirement of the people. One of the problems in husbandry farming is highly feed cost. This problem can be solved with using highly available local sources and cheapness. Cassava leaves (Manihot esculenta Crantz) is the one local sources which can be used as an alternative feed, because it contains a rather high protein (24.1%) aud vitamin A (10,000-13,000 IU). A highly Hydrocyanide (HCN) as well as crude fiber in the cassava leaves are the limiting factors to be used as poultry diet. The ducks have more ability in digest the crude fiber than chicken. So that, using some cassava leaves in the diet does not make any problem.
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