Judul: Optimasi Parameter pada Fast Correlation Based Filter untuk Reduksi Dimensi Menggunakan Algoritme Genetika
Abstrak: Metagenome merupakan mikroorganisme yang diambil secara langsung dari alam. Proses sequencing genom dari metagenome mengakibatkan bercampurnya berbagai organisme. Hal ini menyebabkan kesulitan pada proses perakitan. Oleh karena itu, dibutuhkan proses pemilahan yang disebut binning. Proses binning dengan pendekatan komposisi dilakukan dengan supervised learning. Metode supervised learning memiliki tahapan ekstraksi fitur, penelitian ini menggunakan metode ektraksi fitur k-mers. Besarnya parameter k pada metode ekstraksi fitur mengakibatkan dimensi yang besar. Penelitian ini bertujuan untuk menerapkan algorime fast-correlation based filter untuk mereduksi dimensi fitur yang dihasilkan k-mers dan mengoptimasi parameter threshold pada fast-correlation based filter menggunakan algoritme genetika. Penelitian ini diuji menggunakan klasifikasi k-nearest neighbour sehingga menghasilkan akurasi terbaik diperoleh ketika k = 7 dengan akurasi mencapai 99.41% dengan nilai threshold 0.67788.
Keyword: binning, fast-correlation based filter, genetic algorithm, k-mers, knearest neighbour

Judul: Optimasi Parameter pada Fast Correlation Based Filter Menggunakan Metode Particle Swarm Optimization untuk Klasifikasi Metagenom
Abstrak: Bioinformatika memiliki banyak bidang kajian penting yang terus berkembang, salah satunya adalah analisis metagenom. Metagenom merupakan materi genetik yang diperoleh dari sampel yang diambil langsung dari lingkungan tanpa budidaya di laboratorium. Untuk mengklasifikasikan fragmen metagenom ke dalam tingkat taksonomi yang berbeda, proses binning perlu dilakukan. Dalam penelitian ini, proses binning dilakukan dengan pendekatan komposisi berbasis metode supervised learning. Pendekatan komposisi ini memiliki tahapan ekstraksi fitur yang dilakukan dengan metode k-mers. Untuk mereduksi dimensi fitur yang dihasilkan k-mers digunakan algoritme Fast Correlation Based Filter (FCBF). Untuk klasifikasi, kami menggunakan metode k-nearest neighbour (KNN). Penelitian ini bertujuan untuk meningkatkan hasil akurasi pengklasifikasian metagenome dengan mengoptimasi nilai parameter threshold pada algoritme FCBF dengan menggunakan Particle Swarm Optimization (PSO). Hasil penelitian menunjukkan bahwa PSO dapat digunakan untuk mengoptimasi nilai parameter threshold. Selain itu, dapat diamati bahwa nilai fitness dan akurasi pengklasifikasian berpotensi untuk ditingkatkan dengan menambah jumlah partikel PSO dan iterasi.
Keyword: fast correlation based filter, K-mers, K-nearest neighbour, metagenom particle swarm optimization

Judul: Thermal Characteristics and Dynamics of Tropical Cyclone over Western North Pacific Ocean: A Case Study of Typhoon Songda (Chedeng) 2011
Abstrak: The frequent occasion of Tropical Cyclone (TC) over Western North Pacific (WNP) Ocean lies from 10˚N to 26˚N and 121˚E to 170˚E. Sources of TC development come from warm ocean temperature where latent heat is released during condensation processes and subsequently concentrated in the boundary layer. Relative humidity at 850-700 hPa is responsible to produce the most intense storm. Meanwhile, weak vertical wind shear between 850-200 hPa is one of the dynamical factors that lead to the development of TC. Pressure level of 850-200 hPa is chosen to minimize the thermodynamic effect in the lower troposphere. Typhoon Songda (Chedeng in the Philippines) or TSC lasted from May 18th to 30th, 2011 and strengthened into powerful Super Typhoon stage between May 24th and 27th. Long curving track and the exhibition of all sequences of TC life cycle, TSC is then considered as an ideal case of TC. The objective of the study with case of TSC is to investigate the role of thermal characteristics and dynamics on TC intensity. The research is carried out by using Advanced Research of Weather Research and Forecasting model (WRF ARW model) version 3.3. Two domains are employed in the model. The first domain situate from 02˚N to 40˚N and 118˚E to 149˚E and cover spatial resolution () of 33.1 km. The second domain situate from 09˚N to 27˚N and 119˚E to 132˚E with of 11 km. Timestep of the model () is 120 seconds. The model is supported by the following physical parameterization, i.e., Yonsei University Scheme (YSU) in the boundary layer, Kain-Fritsch scheme (KF) in the cumulus, and WRF Single-Moment 3-class scheme in the microphysics. Initial meteorological condition prior to the formation of the cyclone is generated using WRF ARW standard-release model. Sensitivity of the model output is obtained from a selected single-parameter change scenarios, namely, 10 % to 20 % decrease and increase of wind shear initial data at 850-200 hPa, and by 10 % to 20 % decrease of relative humidity initial data at 850-700 hPa. The result showed that the negative correlations between wind shear and intensity are found in wind shear scenarios. Wind shears of 10 ms-1 which lead to strengthening of the wind intensity in the early period of Typhoon development are found in wind shear scenarios. The effect of wind shear changes on wind intensity is statistically significant in the 20 % wind shear scenarios. During mature stage, weak wind intensity occurred when wind shear increase as given in the WRF standard-release and the 20 % decreasing scenario. Different situation is found in the 20 % increasing scenario where wind shear tend to weak and lead to strong wind intensity at this stage. Meanwhile, the reduction of moisture supply at 850-700 hPa resulting from 20 % decreasing relative humidity play a major role in the weakening of wind intensity and reduces mechanical energy of the cycle process as much as 300 J kg-1.
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