Judul: Penggerombolan Spasial Hotspot Kebakaran Hutan dan Lahan Menggunakan DBSCAN dan ST-DBSCAN
Abstrak: Forest and land fire has become international important environmental and economic issue for the last several years. For Indonesia and some neighboring countries, it also produces huge amount of smog and air pollution causing economic, environmental and health problems. The objective of this research is to find hotspot clustering pattern using DBSCAN dan ST-DBSCAN. There is a possibility that the hotspot is not spread randomly but naturally gather in some area to form clusters based on proximity of distance and time. DBSCAN and ST-DBSCAN are density based clustering in spatial data mining. The advantage of these methods is the clusters form can be more flexible, especially when applied on a large data size. However, DBSCAN cannot differentiate two adjacent clusters with different density while ST-DBSCAN can. As the results, DBSCAN clustering detected 38 hotspot clusters with 6 noises while ST-DBSCAN detected more. Since ST-DBSCAN is an extension of DBSCAN algorithm, ST-DBSCAN can process both of spatial and non-spatial data by using Eps 1 for spatials attributes and Eps 2 for non-spatial attributes, such as time. With this algoritm, there are 147 hotspot clusters and 149 noises. The biggest cluster are located in Musi Rawas, Muara Enim, Musi Banyuasin, dan Ogan Komering Ilir. Furthermore, this research is expected to identify hotspot clustering patterns and behaviors and to produce useful information to evaluate and mitigate forest and land fires hazards and also generate it’s prototype.
Keyword: 

Judul: Spatio-temporal Clustering Hotspot di Sumatera Selatan Tahun 2002-2003 Menggunakan Algoritme ST-DBSCAN dan Bahasa Pemrograman Python
Abstrak: These last few years the issues of forest fire in Sumatera increase dramatically, attracting international attention. Hotspot data which are monitored using satellites can be used as an indicator of the occurrence of fire on the Earth's surface. One approach to analyze hotspot dataset is spatio-temporal clustering which can recognize patterns of hotspot event based on space and time. This study applies ST-DBSCAN clustering using hotspot data in South Sumatera 2002-2003. As multi-paradigm programming language, Python is chosen so that STDBSCAN algorithm can work fast. By using spatial distance (Eps1) 22 km, temporal distance (Eps2) 7 days, and density of cluster (MinPts) 7, 41 clusters were found to have many stationary patterns in Musi Banyu Asin. The average ST-DBSCAN execution runtime using Python was 4.934 seconds
Keyword: ST-DBSCAN, spatio-temporal clustering, Python, forest fire

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.
Keyword: