Judul: Klasifikasi Fragmen Metagenom menggunakan PNN dengan Ekstraksi Fitur GLCM pada setiap Fragmen
Abstrak: Analisis metagenom adalah salah satu bidang kajian bioinformatika yang sedang berkembang di Indonesia. Bidang ini terkait dengan analisis sequencing genom yang diperoleh langsung dari lingkungan, namun sequencing secara langsung tersebut dapat menyebabkan kesalahan rekayasa atau perakitan fragmen dalam suatu kelompok mikroorganisme. Oleh karena itu, diperlukan suatu algoritme yang tepat dalam pengelompokan dari organisme yang memiliki ciri sesuai dengan kelompok tersebut. Tujuan dari penelitian ini adalah menerapkan algoritme klasifikasi probabilistic neural network (PNN) dengan ekstraksi fitur dari gray level co-occurrence matrix (GLCM). Pada proses ekstraksi fiturnya dilakukan dua variasi perlakuan GLCM yaitu ekstraksi untuk seluruh fragmen dan ekstraksi untuk tiap fragmen pada masing-masing organisme, dan didapatkan akurasi maksimum 86% pada perlakuan untuk tiap fragmen dan 73% untuk perlakuan seluruh fragmen. Nilai spesifisitas pada kedua perlakuan ekstraksi mencapai 98.33%, sedangkan sensitivitas diperoleh pada nilai 70%–85% .
Keyword: fragmen, GLCM, klasifikasi, 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: Hamiltonian formulation for linear waves motion in two layers fluid
Abstrak: The surface wave could be considered as a wave that separates two fluids, namely water and air. Based on this assumption, it is introduced the interfacial wave, a wave between two layers of fluid with different density. The formulation of interfacial waves motion begins with deriving the base equation of irrotational ideal fluid. Furthermore, according to irrotational fluid assumption, the base equation can be stated in velocity potential. In this derivation, the fluids domain is assumed to be restricted by rigid lid boundary conditions, both at the upper and lower limit. Therefore, the interfacial waves motion can be explained in a hamiltonian formulation. In the hamiltonian formulation, total energy is defined as the sum of kinetic and potential energy. The hamiltonian system is obtained from reduction of kinetic energy by using the Dirichlet Neumann Operator. The resulted kinetic energy equation is nonlinear. Therefore, this form is linearized by first part of the Taylor expansion. This linearization gives a dispersion relation of linear wave. Based on this dispersion relation, the phase speed of the linear wave depends on the density ratio of the two layers fluid.
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