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: Methane Consumption (CH4) on Upland Rice Cultivation Under Micro Climate Cobdition With N Fertilizer Levels
Abstrak: Methane (CH4) is one of radiatively active trace gases or greenhouse gas, having strong absorbsivity to the infrared outgoing radiation emitted by the earth's surface, hence causing the warming of the atmosphere. Methane concentration in the atmosphere increases with a rate of about 12+1 ppbv/yr, or 36 Tg/yr. The increase of methane was predicted to have a significant impact on the earth's climate. The presence of 1.3 ppmv of methane in the atmosphere causes the globally average surface temperature 1.30C higher than it would be with no methane.
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