Judul: Kajian Empirik Kinerja Algoritma Khusus Partial Distance Strategy (PDS) dan Imputasi.
Abstrak: Analisis gerombol merupakan metode eksplorasi untuk mengelompokkan objek-objek yang memiliki karakteristik yang mirip ke dalam satu gerombol. Seperti analisis statistika standar lainnya, analisis gerombol hanya bisa dilakukan pada data set lengkap. Permasalahan yang sering dihadapi dalam kenyataan sehari-hari sering ditemukan data tidak lengkap pada suatu data set. Penggerombolan data tidak lengkap bisa dilakukan dengan melakukan preprocessing terlebih dulu atau menggunakan algoritma khusus. Preprocessing dilakukan dengan imputasi atau marginalisasi. Imputasi merupakan penambahan nilai tertentu terhadap data tidak lengkap sedangkan marginalisasi merupakan penghapusan terhadap data tidak lengkap. Penelitian ini bertujuan untuk mengkaji kinerja penggerombolan data tidak lengkap menggunakan imputasi dan algortima khusus partial distance strategy (PDS) pada data iris dan menerapkannya terhadap data nilai S-1 Pascasarjana Statistika. Hasil penggerombolan data tidak lengkap dari data iris menunjukkan bahwa algoritma khusus PDS lebih baik dibanding imputasi. Penggerombolan terhadap data nilai S-1 Pascasarjana Statistika menghasilkan dua gerombol. Banyaknya gerombol dipilih berdasarkan metode Connectivity, Dunn, dan Silhouette.
Keyword: analisis gerombol, data tidak lengkap, imputasi, partial distance strategy

Judul: Kajian Penggerombolan Data Tidak Lengkap dengan Algoritma Khusus Tanpa Imputasi
Abstrak: Cluster analysis is a statistical method that aims to classify the n unit objects into k groups, so that the characteristic of objects in a group is more homogeneous than in other groups (Mattjik & Sumertajaya 2002). The main purpose of this technique is to clustering the objects based on specific criteria so that these objects have relatively small variations in the cluster compared to the variation among clusters. The common clustering method can only be used for the complete data set. However, sometimes problems occur when data is incomplete, due to the data not available. Handling of incomplete data clustering can be done with two approaches, namely preprocessing and application of a special algorithm. Preprocessing is a process to solve the problem of incomplete data by applying the results on the complete data (Grzymała & Hu 2001). Two techniques that can be done in preprocessing, namely: engineering marginalization (deletion) of incomplete data and imputation techniques. Wagstaff and Laidler (2005) explain that preprocessing approach is often used, either by the method of marginalization or the imputation method which are easy and simple. Marginalization method is the simplest technique to be used as a solution of incomplete data clustering. There are two possibilities to do with marginalization method; remove objects from the data collection or delete the incomplete, but it should be noted that marginalization can lead to data information loss. Imputation methods performed to estimate the value of incomplete data in clustering with various techniques, such as imputation with constant values, zeros, random values, the median value, average value and others. Troyanskaya et al (2001) in his research concluded that calculation of incomplete data by imputation method is reliable and yield inaccurate information. A special algorithm is done to cover the shortage of marginalization methods and imputation methods. There are some special algorithm for missing data without imputation, such as method of Partial Distance Strategy (PDS) and K-means Soft Constraints (KSC). PDS and KSC adopt the stages of K-means algorithm for complete data. Wagstaff (2004) conducted a study of clustering for incomplete data without imputation with approach Of K-Means Soft Constraints (KSC). Matyja and Simiński (2014) also conducted research of clustering for incomplete data without imputation by comparing Partial Distance Strategy (PDS) with Optimal Completion Strategy (OCS), Nearest Prototype Strategy (NPS), Fuzzy C-Mean (FCM) and Nearest Cluster Strategy (NCS). The development of research has been done before is success to make researchers interested in reviewing the incomplete data clustering without imputation. Study of incomplete data clustering without imputation is done by data simulation and application data. This study aims to assess the method of Partial Distance Strategy (PDS) and the method of K-Means Soft Constraints (KSC) for incomplete data clustering. Simulation data is the data generation with a total population of N = 1200 with numeric data types in terms of several aspects of the simulation, they are the amount of sample (n), the center between cluster (μ) and the correlations between variables (ρ). Data simulation performed by generating three population of normal multivariate ~ N (μ, Σ), which is consists of
Keyword: cluster, accuracy, partial distance strategy, k-mean soft constraints

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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