Judul: Prediksi Penyakit Paru Menggunakan Algoritme Voting Feature Intervals 5 dengan Bobot Fitur Tidak Seragam
Abstrak: Penyakit paru mempunyai gejala yang sangat mirip misalnya pada Bronkitis dan Tuberkulosis Paru, gejala tersebut adalah batuk, batuk darah, sesak nafas, sakit dada, badan lemah, nafsu makan berkurang, berat badan turun, berkeringat malam walaupun tanpa kegiatan. Pada penelitian ini algoritme VFI5 melakukan klasifikasi dua jenis penyakit paru yaitu Bronkitis dan Tb Paru. Pengumpulan data dilakukan dengan proses wawancara pada pasien baru di poli paru Rumah Sakit Umum Daerah Pasar Rebo. Pertanyaan yang diberikan berupa gejala umum penyakit paru yang telah dikonsultasikan dengan dr. Tri Novianti, MARS dan dilakukan proses pelatihan dan pengujian menggunakan algoritme Voting Feature Intervals 5. Penggunaan algoritme VFI 5 dalam mengklasifikasi Bronkitis dan Tb Paru cukup memberikan hasil yang baik dan miripnya gejala pada kedua penyakit ini dapat dibuktikan setelah dilihat dari selang-selang yang dihasilkan oleh setiap gejala pada pelatihan dan pada normalisasi akhir seluruh percobaan.
Keyword: 

Judul: Optimalisasi algoritme voting feature intervals 5 menggunakan algoritme genetik pada data tuberkulosis paru
Abstrak: We proposed a genetic algorithm to optimize VFI5 classification algorithm and to get the best feature weights in lung tuberculocis data. This research used a uniform value for this features weights which equals to one. The accuracy obtained was 83%. Genetic Algorithm (GA) is used to optimize VFI5 by determining the optimal weights for each feature. GA will combine each weight and search the best combination to get an optimal solution. In this research, GA can find the optimal weight feature VFI5 classification algorithm in lung tuberculocis data. The optimal feature weights are “the blood cough with average weight 0.91, limp with average weight 0.82, lost of appetite with average weight 0.63, body weight decrease with average weight 0.62, fever and perfiration with average weight 0.62”. in this reseached, 3-fold cross validation was used to divide data into traning and testing and obtained 95% accuracy in each fold. In conclucions, this research has provided with and accurate model to predict TB and Non TB data instances.
Keyword: VF15 classification algorithm, Genetic algorithm, Tubercolosis paru, Bogor Agricultural University, Institut Pertanian Bogor, IPB

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.
Keyword: