Judul: Perbandingan Metode Cluster Validity pada Jenis Data Numerik dan Kategorik
Abstrak: Clustering is one of the important methods to determine the similarity of the objects set. Based on the similarity of these characteristics, it will form classes and get a pattern from a collection of unlabeled data. The resulting clusters cannot be ascertained if the accuracy has not been analyzed and tested using the method of cluster validity. This study implements the k-means clustering algorithm to classify three types of data: numerical, categorical, and a combination of numerical and categorical. The three data will be validated using the cluster validity methods: Dunn index, Hubert's statistic, and silhouette coefficient. Results of the three methods will be compared based on the three types of data used. Based on the result of the study, the data type used influences the cluster validation algorithm. The test result showed that Hubert's statistic can be used in the three types of data. Research involving bigger sized data is still needed to be able to conclude the most appropriate algorithm for the validation of numerical data, categorical, or a combination of both.
Keyword: clustering, cluster validity, index Dunn, Hubert’s statistic, silhouette coefficient

Judul: Kajian Metode Penggerombolan Dua Tahap untuk Data yang Mengandung Pencilan
Abstrak: Cluster analysis is often encountered in various studies. Analysis of classical clusters, such as hierarchical clustering method and k-means clustering cannot handle categorical variables or a mixture of numerical and categorical. In addition, the determination of the optimal number of clusters are still dependent on the subjectivity of the researcher and cannot handle very large datasets, which is larger than 500. One approach to addressing this problem is to use a two-step clustering method. The accuracy of the two-step clustering method of predicting the number of clusters generated as well as the classification of cluster membership, especially in the data containing outliers is important to be studied. Outliers in the data containing a small (1%), this method provides more accurate compared with the results of data containing a large outliers (5% or 15%). Scale use of outliers handling in the data containing outliers must be greater than the amount of outliers itself. Two-step clustering method is very accurate in producing a number of clusters associated with the actual number of population clusters that do not contain data outliers, especially in the most variable of type numeric and categorical rest. Clustering villages in Indonesia by a factor of progress and backwardness villages using a two-step clustering method generates optimal cluster 7.
Keyword: two-step clustering method, data outliers, cluster analysis

Judul: Hydrothermal Synthesis of Biphasic Calcium Posphate
Abstrak: Biphasio calsium phosphate (BCP), which was a mixture of hydroxypatite (HA) and tricalcium phosphate (TCP), was synthesized by hydrothermal method followed by sintering process. Hydroxyapatite was produced by using natural calcium source CaO from hen's eggshell and (NH4)2HPO4 as phosphate souce, other hydroxyapatite sample was produce by using synthetic calcium source Ca(OH) and H3PO4 as phosphate source.
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