Judul: Penelusuran Artikel Kasus Tindak Pidana Korupsi (Tipikor) Menggunakan Metode Self Organizing Maps (SOM)
Abstrak: Nowadays, there are so many corruption articles in the internet with so many topics. Pusat Teknologi Informasi dan Komunikasi Badan Pengkajian dan Penerapan Teknologi (PTIK BPPT) conducts a research to make a system to cluster the corruption articles with related topic based on the keywords without changing the topology of the articles and can be visualized to the two dimension maps with information of related article and share rate. Self Organizing Maps (SOM) is the clustering method that is being used in the research. SOM can cluster massive article with high dimension to lower dimension without changing its topology. In this research, tools for measuring the clusters are Vector Quantization Error (VQE), cohesion, and separation. Method of computation cohesion and separation that used in this research is prototype-based cohesion and separation that focus in computing on the centroid. Finally, the conclusion of this research is that SOM clusters the articles based on the similarity of the topic. Neighbouring clusters mean that they have resemblance topics. Clusters that far-set from the other clusters means that they have less or do not have resemblance each other.
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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: Pengaruh pemberian pupuk kandang ayam dan pupuk daun gandasil D terhadap pertumbuhan dan perkembangan tanaman Calathea lancifolia
Abstrak: Calathea lancifolia merupakan tanaman hias daun yang memiliki keelokan daun yang mampu menambah kesejukan rumah. Daunnya memiliki warna dengan kombinasi warna yang menarik , serta bentuk daun yang unik yaitu bergelombang, tanaman ini disebut dengan Rattlesnake plant. Suhu dingin minimum untuk Calathea lancifolia yaitu 16 derajad Celsius(60 derajad F), fluktuasi suhu yang tiba-tiba perlu dihindari, kelembapan yang dibutuhkan tinggi. Tanaman ini juga merupakan tanaman yang senang naungan, tidak suka kena cahaya matahari langsung.
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