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: Modified Method of Selection Initial Centroid in K-Means Clustering
Abstrak: Cluster analysis is one of multivariate technique, clustering is a process of classifying object into groups which have similarity. The result of clustering will show that objects in one cluster will be more homogeneous than others. There are two methods in classic clustering analysis i.e. hierarchical clusters method and non-hierarchical cluster method. Determination of the number of clusters which formed by them is done subjectively or based on literature. One of the non-hierarchical cluster method is k-means clustering. Characteristics of k-means clustering is a good computational performance, but k-means clustering is very sensitive to select initial centroid because k-means method select initial centroid random from data so that the results of the k-means clustering are not unique. In this research, k-means method was compared with modified method of selection initial centroid in k-means. According to Sona and Sujatha (2013), the method of centroid selection is focused on improving performance of k-means clustering algorithm. Performance of modification method in selecting initial centroid will be compared in simulation data, and then apply the modified method in secondary data. The data in this research consist of two sources i.e. simulated data and secondary data. Simulated data were generated data multivariate normal distribution (μ,Ʃ) which useful to measure the performance of modified method of selection initial centroid in k-means and k-means method. Secondary data which used in this research, BPS’s data in Bengkulu province was village potential data in 2011. Simulation data were the generated data numeric type which consisted of three clusters, and each cluster consist of three variables. Simulation data is divided into three conditions i.e a). distance between each centroid near, b). distance between each centroid medium, c). distance between each centroid far. Every data condition applied in small (n=300), medium (n=900), and lagre number (n=1500). Modification method of selection initial centroid in k-means has better performance than k-means clustering method. It is based on number iterations is formed, there is no member of each clusters that switch positions (convergent). The number of iterations on the modified method of selection initial centroid in k-means method will increase if the variance from data is enhanced. The results of modified method of seletion initial centroid in k-means method is determined into three clusters. It is based on purpose of clustering by village potential data for view infrastructure and facilities in Bengkulu province. This clustering is villages with adequate infrastructure, villages with inadequate infrastructure, and villages with lack adequate infrastructure.
Keyword: Bengkulu province, clustering, k-means, modified selection initial centroid

Judul: Manajemen Pemeliharaan Kuda (Equus caballus) Untuk Upacara Kenegaraan Dan Sarana Kesenjataan Di Detasemen Kavaleri Berkuda (DenKavkud) TNI-AD Parongpong
Abstrak: Cavalry Horse Detachement (Denkavkud) Indonesian National Army-Cavalary Parongpong is one of the Indonesian military units that utilize the horse and train it as a means of purpose of formal state ceremony and armed forces. Skills possessed by the horse cavalry was the result of care management and specialized training provided by the trainer. Aspects of care management are breeding, feeding, and management. Horses that are kept in the Denkavkud, Parongpong are stallions, mares, foals, and military horses. Total populations of horses at Denkavkud are 183 horses. Besides commited horse training (remonte), Denkavkud also did breeding program to produce horses that can be trained and used as a military horse. There are some issues contained in Denkavkud, such as some of the nutrients needed by the horses not being met from feed given. One of these is crude protein and lysine. Lactation mares was crude protein and lysine deficiency. The number of death in 2010 is higher than previous years. Based on the number of horses that died from January until June 2010 is 11 horses. The average mortality was caused by colic. Colic is caused by excessive eating, excessive drinking during hot, moldy food, and even by investment roundworms. Animal welfare in Denkavkud can be enhanced by improvement of feed given and improving the health of livestock through improved sanitation.
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