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: Perbandingan Hasil Penggerombolan Metode K-Means, Fuzzy K-Means, dan Two Step Cluster
Abstrak: The main principle of cluster analysis is to classify objects into clusters based on similarity measures. K-means and fuzzy k-means can be classified as popular clustering methods, which are suitable for large data with continuous variables. However, a new method has been developed to be used for large data, that is the two step cluster method. This method allows processing data with different types of variables, which in this case are continuous and categorical. The aim of this research is to compare the clustering results of k-means, fuzzy kmeans, and two step cluster method, in order to determine the ideal number of clusters for each method. This research uses hypotetical data taken from SPSS software, which fit the purpose to compare several methods. The results of this study show that in the case of two clusters, k-means and fuzzy k-means methods have more similarities with respect to the number objects in clusters, whereas the two step method gives unequal number of objects in clusters. All methods show that 2 clusters is an ideal number. It is influenced by the ratio between mean squares within clusters, which is smaller than the ratio in the case of 3 and 4 clusters.
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Judul: Extracellular Expression of thermostable Geobacillus stearothermophilus T1.2RQ Lipase for Biodiesel Production
Abstrak: The extracellular expression of enzymes in an excellent secretion host such as Bacillus subtilis is a useful strategy for making enzymes of industrial significance more affordable by lowering the cost of its downstream processing. Lipases are a category of hydrolases that catalyze the hydrolysis of triglycerides to glycerol and free fatty acids. Also, lipases catalyze the hydrolysis and transesterification of other esters as well as the synthesis of esters. They can perform very specific biotransformation reactions and are widely used in food, organic synthesis, detergents, cosmetic, and pharmaceutical industries. Geobacillus stearothermophilus T1.2RQ lipase is an industrially-desired thermostable lipolytic enzyme with an excellent hydrolytic and transesterification activity. This study aimed to express T1.2RQ lipase, in B. subtilis WB800 and ensure its secretion into the extracellular medium. Three signal peptides (AmyQ, Epr, LipA) and two promoters (Pgrac01 and Pgrac100) were used in the secretory-expression of T1.2RQ lipase, based on five constructed expression vectors constructed by restriction cloning and PCR mutagenesis. Sangers sequencing was used to verify constructs to ensure they are in the correct reading frame. Recombinants vectors, pHT43 (plasmid control), pHT43-T1.2RQ, pHT43- T1.2RQ_AmyQ, pHT43-T1.2RQ_Epr, and pHT43-T1.2RQ_LipA, were transformed by electroporation into Bacillus subtilis WB800. The electroporation was successful with the optimum condition of 2500V, 200 ohms, 25µF, and 5milliseconds. Recombinant B. subtilis pHT43 (negative control) and pHT43-T1.2RQlip + signal peptides were streaked on the lipidic agar, LA+TBN. All four recombinants, including the negative control (B. subtilis WB800-pHT43), produced clear zones on the TBN agar plate after overnight incubation (fig 2), indicating the presence of a lipolytic enzyme in all of the recombinant B. subtilis cells. pHT43-T1.2RQlip + signal peptides (AmyQ, Epr, and LipA) have clear zones with significantly higher diameters than B. subtilis WB800-pHT43 (negative control). This phenomenon suggests that the recombinant thermostable lipase T1.2RQ introduced into these cells was successfully expressed, thereby increasing the lipolytic capability of its host cells T1.2RQ lipase was expressed in 50ml terrific broth medium (TB) and the expression induced with 1mM IPTG. Lipase activity assay using p-nitrophenyl esters showed that all three signal peptides directed the secretion of T1.2RQ lipase into the extracellular medium. Signal peptide, LipA, resulted in the highest extracellular activity of 5.6 U/mL, which corresponds to a 6-fold increase over the parent B. subtilis WB800 strain. Meanwhile, signal peptides, AmyQ and Epr produced 4.2 U/mL and 3.4 U/mL activity respectively. SDS-Zymogram analysis confirmed that T1.2RQ lipase was correctly processed and secreted in its original size of 44 kDa. An attempt to further increase lipase expression level by promoter optimization showed that, contrary to expectation, promoter Pgrac100 exhibited similar expression levels as the original Pgrac01. Nevertheless, the demonstration of the successful secretion of T1.2RQ lipase in this work confirms the latter as a promising approach for the industrial production of the enzyme
Keyword: Bacillus subtilis, Expression, Extracellular, Heterologous, Lipase, Thermostable