Judul: Spatio-Temporal Clustering Hotspot di Sumatera Selatan Tahun 2002-2003 Menggunakan Algoritme ST-DBSCAN dan Bahasa Pemrograman R
Abstrak: Forest fire is a serious problem in Indonesia. One indicator of forest fire possibility can be seen through the occurence of hotspots. Hotspot dataset is large spatial data because it is recorded continuously. ST-DBSCAN an algorithm that can process spatial and temporal data. This study implement ST-DBSCAN algorithm with R language programming. R is software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software. Clustering is performed on hotspots dataset in South Sumatra in the period 2002-2003. By using the spatial distance parameter (Eps1) = 0.2, the temporal distance parameters (Eps2) = 7 and a minimum cluster members (MinPts) = 7 resulted in 41 clusters with 712 noises.
Keyword: ST-DBSCAN, spatio-temporal, hotspot, R, forest fire

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.
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Judul: In Vitro Release of Ibuprofen from Microcapsules Coated Polyblend of Poly(lactic acid) and Poly(ε-caprolactone)
Abstrak: Ibuprofen is an anti-inflammatory drug that has a short biological half-life and imposes adverse gastrointestinal reaction. Microencapsulation could be used to minimize its disadvantages. In this experiment, microencapsulation of ibuprofen was done by emulsification method. Coating material which was used were polyblend of poly(lactic acid) and poly(ε-caprolactone) which are biodegradable and biocompatible. Polyvinylalcohol was used as an emulsifier at concentrations of 1.5%. The microencapsulation was carried out with various quantities of ibuprofen. The increase in quantities of ibuprofen would improve encapsulation efficiency. Encapsulation of ibuprofen showed an efficiency of more than 71% with the highest score of 84.13% in microcapsule with ratio of polyblend-ibuprofen 5:1.5. The dissolution test results in basic medium showed that the encapsulation efficiencies affected ibuprofen release from the microcapsule. Microcapsules with the higest encapsulation efficiency released ibuprofen ranged from 26.71 to 28.78% for 6 hours. Kinetic of ibuprofen release followed first order kinetic model. The microcapsules particle sizes ranged from 38 to 250 μm.
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