Judul: Multidimensional association rules mining untuk data kebakaran hutan menggunakan algoritme fp-growth dan eclat
Abstrak: Bencana kebakaran hutan dan lahan merupakan permasalahan serius yang harus dihadapi bangsa indonesia khususnya provinsi riau. Salah satu upaya pencegahan kebakaran hutan yaitu dengan mempelajari pola keterkaitan antar kejadian titik panas (hotspot) dengan karakteristik objek geografis terdekat titik panas. Tujuan penelitian ini adalah menerapkan metode aturan asosiasi multidimensi dengan algoritme frequent pattern growth (fp-growth) dan algoritme equivalence class transformation (eclat) untuk menentukan pola keterkaitan antara kemunculan titik panas dan faktor-faktor pendukungnya. Sebagai bahan penentuan faktor kemunculan titik panas penelitian ini menggunakan minimum support 30% dan minimum confidence 80% dengan variabel target yaitu terjadinya titik panas. Hasil penelitian menunjukkan bahwa hubungan yang kuat antara terjadinya titik panas dan pengaruh faktor yang ditemukan dengan support terbesar 44.49%, confidence 100%, dan lift 1.02. Titik panas terjadi umumnya pada daerah yang memiliki curah hujan lebih besar atau sama dengan 3 mm/hari.
Keyword: eclat, fp-growth, hotspot, multidimensional association rules

Judul: Fuzzy Association Rules Mining pada Data Klimatologi dan Jumlah Hotspot di Kalimantan Tengah dan Kalimantan Selatan
Abstrak: The occurrence of forest fires was influenced by many factors of weather and climate such as temperature, rainfall, solar radiation, humidity, air stability, speed and direction of wind. Rainfall, relative humidity and temperature were the main factors on the moisture of fuel control. Wind and solar radiation were important factors on drying fuels. It was influenced on change in fuel temperature also temperature and relative humidity in the air. This research was aimed to extract or “mine” knowledge from climatology and number of hotspots data in Central Kalimantan and South Kalimantan by 2001 until 2004. It is using Association Rules Mining method with apriori algorithm to see the relation between the number of hotspots and climatology data. Also fuzzy logic was used to solve an uncertainty and the numerical data. The result of this research was presented in the rules. The rules show relation between climatology and the number hotspots data. In rainy season (November-June) few hotspots were shown. This was influenced by mild temperature, mild solar radiation, mild air pressure, high relative humidity and low wind speed. However in dry season the occurence of forest fires was high in each year. The peak of forest fires occurred in August and September. It was influenced by mild temperatures, low rainfall, low solar radiation, mild air pressure, mild relative humidity, low and mild wind speed
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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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