Judul: Penerapan Dynamic Density Based Clustering pada Data Kebakaran Hutan
Abstrak: Land and forest fire has become prominent issues in Indonesia. Possibilities of the occurrence of forest fire in Indonesia has increased from year to year. This makes early prevention very important for forest fire investigation. One of the efforts concerning the forest fire prevention is by knowing the distribution of hotspot clustering which have high potential for the occurrence of forest fire. This research has classified the hotspot data using Dynamic Density Based Clustering (DDBC) algorithm. The use of DDBC technique is capable of handling spatiotemporal aspects simultaneously by storing the position of each point. The storage of each point’s position is estimated using the strength of its relationship to other points that appear every year. The neighborhood concept of DDBC algorithm is a modified version of the neighborhood concept of the Density Based Spatial Clustering (DBSCAN) called Relationship Strength Threshold (RST). Cluster detection is performed on the points that fulfill the RST neighborhood value, so that only the point which was considered as a strong relationship will be grouped. The result of the clustering obtained through DDBC technique is the grouping of areas with high potential for forest fire occurrence. Visualization of the clustering results is presented based on a map that describe the distribution of hotspot so that the authorities can determine the prioritized areas for early forest fire prevention.
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Judul: Clustering Dataset Titik Panas dengan Algoritme RDBC Menggunakan Web Framework Shiny pada Bahasa R.
Abstrak: Indonesia has tropical forest that is quite extensive, but forest fires often occur resulting in great impact for Indonesia. Monitoring hotspots can be one of the forest fire disaster mitigation efforts. Each hotspot will be recorded on a dataset that can be processed to obtain information. This study aims to build a clustering web application on the hotspot data. This application uses the R programming language to implement Recursive Density Based Clustering (RDBC) algorithms using Shiny framework. Clustering is performed on hotspot data of the Kalimantan island and province of South Sumatra in 2002-2003 to find the pattern of spread of hotspots. The results obtained from the clustering process are evaluated using the Silhouette Coefficient. The SC value of this research is 0.2045354 for Kalimantan Island dataset and 0.232323 for South Sumatera Province dataset. The result is displayed in the form of web pages that can be accessed easily and can be referred for subsequent fire occurrence prediction.
Keyword: clustering, hotspot, RDBC, Shiny, Silhouette Coefficient

Judul: Optimization of Alginate-Chitosan Microcapsules Formation for Leydig Cells Encapsulation
Abstrak: Hypogonadism is a clinical condition characterized by low concentrations of testosterone. It is caused by malfunction of Leydig cells in producing testosterone. Encapsulation method will protect the transplanted cells from the immune system rejection. The aims of this study was to optimize the formation of microcapsules for Leydig cells encapsulation. The microcapsules were made of alginate and chitosan as the first and the second coating agents, respectively. The result showed that the minimum concentration of alginate was 1.5% (w/v) with a viscosity of 33.8 cPs, resulted spherical microcapsules with diameters of 230-270 µm. The optimum concentration of chitosan as the second coating agent was 0.5% (w/v), resulted spherical microcapsule with mechanical stability in 4 hours. Leydig cells can be trapped inside the capsule with a density that proportional with concentration of cells used in the encapsulation.
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