Judul: An Application of Genetic Algorithm for Clustering Observations with Incomplete Data
Abstrak: Cluster analysis is a method to classify observations into several clusters. A common strategy for clustering the observations uses distance as a similarity index. However distance approach cannot be applied when data is not complete. Genetic Algorithm is applied by involving variance (GACV) in order to solve this problem. This study employed GACV on Iris data that was introduced by Sir Ronald Fisher. Clustering the incomplete data was implemented on data which was produced by deleting some values of Iris data. The algorithm was developed under R 3.0.2 software and got satisfying result for clustering complete data with 95.99% sensitivity and 98% consistency. GACV could be applied to cluster observations with missing value without any pre-processing step to incomplete observations. Performance on clustering observations with incomplete data is also satisfying but tends to decrease as the proportion of incomplete values increases. The proportion of incomplete values should be less than or equal to 40% to get sensitivity and consistency not less than 90%.
Keyword: Incomplete Data, Genetic Algorithm, Cluster Analysis

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: Sintesis dan Karakterisasi Membran Penukar Proton Polistirena Tersulfonasi sebagai Bahan Perangkat Direct Methanol Fuel Cell
Abstrak: Sulfonated polystyrene can be used as proton exchange membrane (PEM). Characterization and performance of PEM has been performed on sulfonated polystyrene (PSS) using fourier transform infrared spectrophotometer, scanning electrone microscope, and degree of sulfonation (DS) parameter. The success of the process indicated by the DS of the membrane PSS 3%, 5%, and 10% were 35.09%; 42,26%; and 48.15%, respectively. The infrared spectrum shows that sulfonation has been succesful in 10% PSS membranes as indicated by the presence of sulfonate groups (-SO3) at wavenumber 1029.39 and 1178.03 cm-1. PSS membrane surface morphology is categorized non-porouse. Methanol permeability with qualitative test showed that the membrane PSS 3%, 5%, and 10% have no infiltration. The results of measurements of the conductivity (σ) obtained by PS, PSS 3%, PSS 5%, and PSS 10% were (0.0114; 1.5511; 3.0919; and 3.8949) × 10-6 S/cm, respectively. So that the membrane PSS 3%, 5%, and 10% can be used as a device in direct methanol fuel cell.
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