Judul: Analisis kinerja algoritme self-organizing MAP satu dimensi dan dua dimensi pada data kebakaran hutan dan data wine
Abstrak: Self-organizing map (SOM) merupakan suatu jaringan saraf tiruan yang mampu melakukan pemetaan topologi dari unit-unit pada lapisan masukan input ke lapisan masukan kompetitif. Berdasarkan penelitian Ralina (2008) yaitu clustering menggunakan data citra, SOM memberikan hasil dengan nilai cluster precision (CP) mencapai 85%. Penelitian ini menggunakan SOM dengan data lain yaitu data kebakaran hutan dan data wine. Topologi SOM menggunakan SOM 1 dimensi dan 2 dimensi. Kinerja SOM 1 dimensi diukur berdasarkan nilai cluster precision (CP) dan nilai cluster recall (CR), sedangkan kinerja algoritme SOM 2 dimensi diukur berdasarkan berhasil atau tidak memisahkan kelompok-kelompok yang ada pada data kebakaran hutan dan data wine. Hasil kinerja dari SOM 1 dimensi pada data kebakaran hutan diperoleh nilai CP 54% dan nilai CR 53%, sedangkan pada data wine diperoleh nilai CP 70% dan nilai CR 69%. Kinerja SOM 1 dimensi pada data wine lebih baik dibandingkan data kebakaran hutan. Hasil dari Implementasi SOM 2 dimensi pada data kebakaran hutan, SOM tidak berhasil memisahkan kelompok-kelompok yang ada. Berbeda dengan hasil kebakaran hutan, SOM 2 dimensi pada data wine cukup baik memisahkan kelompok yang ada.
Keyword: Kebakaran Hutan, Wine, Algoritme Self-Organizing Map, Cluster Precision, Cluster Recall

Judul: Kajian Self-Organizing Maps (SOM) dalam pengelompokan objek (studi kasus: pengelompokan desa/kelurahan di Kabupaten Wajo Sulawesi Selatan)
Abstrak: Clustering is a process of classifying objects 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 cluster method and non-hierarchical cluster method. Determination of the number of clusters which formed by them is done subjectively. The cluster other methods also developed by using artificial intelligence. Artificial neural network is an information processing paradigm that inspired by the biology systems, it is neuron. Like brain which process information. Self-organizing maps (SOM) is one of the topology of Unsupervised Artificial Neural Network (Unsupervised ANN) which process does not require monitoring in his training. Application clustering using SOM algorithm is expected to be used as a tool to analyze the data in order to obtain the characteristics of the data that will be grouped. Clustering is used to group the data naturally without based on the specific class target. In this study, SOM compared with clustering method with large data sizes, it was two-step cluster. According to Bacher (2004), two-step cluster method (TSC) was a cluster method which can resolve the problem clustering measurement scale, especially for large data with variables which have categorical and numerical data types. Performance clustering SOM and two-step cluster method compared by the simulation data, afterwards, applying the method of SOM on clustering villages/urbans in Wajo regency, South Sulawesi. The data in this study consisted of two sources i.e. simulated data and secondary data. Simulated data was generated data multivariate distribution (μ,Ʃ) which useful to measure the performance of two-step cluster method and SOM in classifying an object. Secondary data, which used in this study, BPS’s data in Wajo regency, South Sulawesi, was Village Potential Data (VPD) in 2011. Simulation data was the generated data numeric type (M) which consisted of three forms of the population i.e. a). a population consisted of three clusters were clearly separated, b). a population which consisted of three clusters of overlapping (overlap) each other in small numbers, and c). a population that consisted of three clusters of overlapping (overlap) each other in large numbers. The results of methods SOM and TSC showed that simulation data has the good ability to classify data, however, TSC provides better clustering results for large data sizes than SOM. In addition, it is also showed that the larger the number of data, the misclassification of SOM would become larger, nevertheless, the changes were relatively smaller. In the other hand, the larger the number of data the misclassification of TSC method was become smaller. Secondary data, which used in this study, written documentation and identification of used variables about areas/villages in Wajo regency, South Sulawesi, was Village Potential Data (VPD) in 2011 i.e. : X1 (total population), X2 (family farm), X3 (family farm laborer), X4 (family power user), X5 (fuel for daily cooking), X6 (educational facilities), X7 (health personnel), X8 (population mortality). Objects in this research were applied around the village/urbans in Wajo district. The results of clustering with SOM method, village/urbans in the Wajo regency produced
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Judul: The Leukocyte Profile of Pregnant Friesian Holstein Cows vaccinated with Escherichia coli K99 polivalen vaccine.
Abstrak: The objective of this experiment was to study the leukocyte profile of pregnant cows vaccinated with E.coli K99 polivalen vaccine. Three pregnant Friesian Holsteins cows were used in this experiment. Before the administration of vaccine, the cows were given a series of immunomodulator injection for three days. The vaccinations were administered three times at 8, 6, and 4 weeks before calving. Blood were drawn through jugular vein, first before the administration of vaccines, then weekly until calving. Based on the observation, it can be concluded that the administration of E.coli K99 polivalen vaccine in pregnant cows not affected the leukocyte profile. Keywords : Escherichia coli vaccine, leukocyte profile, differential leukocyte
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