Judul: Analisis komponen utama kernel: suatu studi eksplorasi pembakuan peubah
Abstrak: Analisis komponen utama kernel (akuk) merupakan perluasan dari analisis komponen utama (aku) yang berguna untuk menyelesaikan masalah data yang takterpisah atau taklinear. Akuk adalah aku yang diterapkan di ruang fitur, yang merupakan ruang hasil pemetaan objek-objek dari ruang asal. Pada akuk, data asal dipetakan ke ruang fitur. Namun tidak semua hasil pemetaannya diketahui, sehingga nilai eigen dan vektor eigen hanya dapat diperoleh dari matriks dual hasil pemetaan tersebut di ruang fitur, tidak dari matriks primalnya. Hasil kali dalam dari pemetaan di ruang fitur disebut dengan fungsi kernel. Fungsi kernel polinom memiliki hasil pemetaan yang jelas sehingga pembakuan peubah di ruang fitur dapat dieksplorasi. Pada karya tulis ini, eksplorasi pembakuan peubah diterapkan pada data pengenalan anggur (forina 1991). Eksplorasi pembakuan peubah tersebut memberikan gambaran bahwa ada perbedaan pada waktu pembakuan dilakukan terhadap konfigurasi yang terbentuk dan terhadap nilai salah klasifikasinya. Perbedaan hasil pembakuan peubah tercermin dari ukuran kemiripan dan nilai salah klasifikasinya
Keyword: analisis komponen utama, analisis komponen utama kernel, fungsi kernel polinom, pembakuan peubah

Judul: Data classification using kernel principal component analysis with isotropic function
Abstrak: Analisis komponen utama (AKU) merupakan bentuk khusus dari AKU kernel dengan fungsi kernel linear. Tujuan dari studi ini ialah untuk menyelesaikan permasalahan data yang tak terpisahkan secara linear dan mengklasifikasikan suatu objek ke dalam kelompok menggunakan AKU kernel sehingga diperoleh salah klasifikasi terkecil. Pengklasifikasian kelompok menggunakan AKU kernel diselesaikan dengan fungsi kernel linear dan fungsi isotropik (Gauss dan gelombang). Hasil untuk data pengenalan anggur menunjukkan bahwa fungsi kernel linear memberikan salah klasifikasi 6.74%, sedangkan fungsi isotropik yaitu Gauss dan gelombang masing-masing memberikan salah klasifikasi 2.25% dan 7.30%. Kata kunci: analisis komponen utama, kernel, isotropik., Principal component analysis (PCA) is a special case of the kernel PCA with linear kernel function. The aim of this study is to resolve the data problem that is not linearly separable and to classify objects into a group by using kernel PCA to obtain the smallest classification error. Group classification using kernel PCA is performed by the linear kernel function and isotropic function, i.e., Gauss and wave functions. The result of the study shows for wine recognition data with the linear function produces 6.74% classification error, whereas Gauss and wave isotropic functions produce 2.25 % and 7.30% classification error, respectively. Keywords: principal component analysis, kernel, isotropic.
Keyword: analisis komponen utama, kernel, isotropik

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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