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: 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: Trichomonas Gallinae Penyebab Trichomonosis Pada Unggas
Abstrak: Trichomonas gallinae adalah protozoa yang bergerak aktof dengan keempat flagella yang mempunyai layar dan pada salah satu sisinya terdapat membran seperti sirip ikan yang berombak. Spesies dari genus Trichomonas ini mempunyai arti penting pada unggas, karena dapat menyebabkan infeksi pada alat pencernaan bagian atas.
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