Judul: Penerapan synthetic minority oversampling technique (SMOTE) pada identifikasi ikan berbasis DNA Barcoding
Abstrak: Data balancing is one of the methods used to overcome imbalance amount of data in the classes to be classified. Balanced data can improve the performance of classification model. The purpose of this research was to analyze the influences of SMOTE application on the model’s accuracy in classification. Feature extraction was done using k-mers. The classification process began by building the model using Support Vector Machine (SVM) with training data obtained from the Barcode of Life Database (BOLD). The model was tested using testing data from Department of Aquatic Products Technology, Faculty of Fisheries and Marine Science, Bogor Agricultural University. The average percentage of accuracy without using SMOTE was 82.67%, while the average percentage of accuracy using SMOTE was 90.63%. These results show that the application of SMOTE on bulding the model can improve the classification accuracy of fish, both at genus and species level.
Keyword: Data balancing, k-mers, SMOTE -SVM

Judul: Analisis Teknik Resampling Menggunakan Synthetic Minority Oversampling Technique (SMOTE) untuk Melatih Support Vector Machine (SVM)
Abstrak: Dataset yang memiliki distribusi kelas tidak seimbang dapat mengganggu proses pelatihan model untuk klasifikasi. Secara umum, permasalahan imbalanced data bisa diselesaikan dengan dua pendekatan, yaitu: pendekatan pada level algoritme dan level data. Penelitian ini menggunakan pendekatan pada level data untuk menganalisis data DNA kedelai kromosom 11 (Gm11) dan 16 (Gm16) menggunakan Synthetic Minority Oversampling Technique (SMOTE). Algoritme klasifikasi yang digunakan adalah Support Vector Machine (SVM). Proses penyeimbangan data difokuskan pada data di sekitar hyperplane SVM. Selanjutnya, data ini disebut sebagai data kritis. Evaluasi pada data Gm11 menunjukkan bahwa nilai f-measure mengalami penurunan. Model yang dilatih menggunakan data latih yang tidak seimbang memiliki f-measure 56.68%, sedangkan model yang dilatih menggunakan data kritis memiliki nilai f-measure 49.61%. Sebaliknya, pada Gm16 terdapat peningkatan antara model yang dilatih menggunakan data latih tidak seimbang dan data kritis. Peningkatannya adalah 60.34% menjadi 62.22%. Berdasarkan evaluasi tersebut, dapat disimpulkan bahwa teknik resampling pada data kritis dapat meningkatkan kinerja model SVM.
Keyword: data kritis, klasifikasi, imbalanced class, SMOTE, SVM

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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