Judul: Spatial Modeling of Forest and Land Fires Vulnerability in Rawa Aopa Watumohai National Park
Abstrak: Kebakaran hutan dan lahan selalu menjadi agenda tahunan di Taman Nasional Rawa Aopa Watumohai, hal ini menimbulkan banyak dampak baik aspek sosial, ekonomi, ekologi, serta kesehatan manusia. Penelitian ini bertujuan untuk menganalisis faktor dan variabel terjadinya kebakaran hutan di TNRAW, membangun model dan peta kerawanan kebakaran hutan dan lahan di TNRAW, serta pengaruh sosial dan ekonomi masyarakat dengan kejadian kebakaran hutan dan lahan di TNRAW. Data yang digunakan untuk membangun model kerawanan kebakaran adalah data titik panas dari satelit Moderate Resolution Imaging Spectroradiometer (MODIS) dan Balai TNRAW, data variabel yang digunakan adalah data iklim, proxy from human activities, serta biofisik alam. Data variabel yang digunakan dilakukan uji multikolinearitas dan dilakukan model kebakaran hutan dan lahan menggunakan machine learning Maximum Entropy (maxent). Pengaruh ekonomi dan sosial masyarakat dilakukan dengan metode wawancara dengan pemilihan responden menggunakan snowball, wawancara dilakukan terhadap lima desa yang terdapat di dalam kawasan TNRAW dan dianalisis secara deskriptif. Berdasarkan kontribusi variabel dengan mengggunakan maxent, variabel yang memiliki pengaruh terbesar dalam kejadian kebakaran hutan di TNRAW adalah curah hujan, tutupan lahan, dan biomasa. Curah hujan yang relatif rendah di TNRAW didukung dengan tutupan lahan savana di TNRAW meningkatkan bahan bakar untuk terjadinya kebakaran hutan dan lahan. Validasi model mencapai 0,918 yang menunjukkan tingkat validitas model yang cukup baik. Berdasarkan hasil wawancara, masyarakat memiliki kebiasaan untuk memperluas lahan kebun dan masuk kedalam kawasan guna meningkatkan ekonomi masyarakat karena rendahnya peningkatan lapangan pekerjaan yang tidak sebanding dengan jumlah pertumbuhan penduduk. Pembukaan lahan dilakukan dengan cara pembakaran hutan, hal ini disebabkan karena pembakaran lahan dinilai salah satu cara yang efektif, mudah, cepat, dan relatif murah dibandingkan dengan metode lain., Forest and land fires are always an annual agenda in the Rawa Aopa Watumohai National Park, this has many impacts on social, economic, ecological and human health aspects. This research aims to analyze the factors and variables in the occurrence of forest fires in Rawa Aopa Watumohai National Park (RAWNP), build a model and map of forest and land fire vulnerability in RAWNP, and society's social and economic influence on the occurrence of forest and land fires in RAWNP. The data used to build the fire vulnerability model is hotspot data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and the RAWNP. The variable data used is climate data, proxies from human activities, and biophysics. The variable data used was tested for multicollinearity, and a forest and land fire model was carried out using maximum Entropy (maxent) machine learning. The economic and social influence of the community was carried out using the interview method by selecting respondents using snowball. Interviews were conducted in five villages within the TNRAW area and will be analyzed descriptively. Based on the contribution of maxent variables, the variables that have the most significant influence on forest fires in TNRAW are rainfall, land cover, and biomass. Relatively low rainfall in TNRAW, supported by savanna land cover, increases fuel for forest and land fires. Model validation reached 0.918, showing a good model validity level. Based on the results of interviews, the community has a habit of expanding their plantation area to improve
Keyword: curah hujan, kebakaran hutan dan lahan, kerawanan kebakaran, Maxent

Judul: Strategi Pengendalian Kebakaran Hutan dan Lahan di Taman Nasional Rawa Aopa Watumohai dengan Pemanfaatan Pemodelan Spasial.
Abstrak: Rawa Aopa Watumohai National Park (TNRAW) is one of the most important conservation areas in Indonesia which has unique species in Wallacea region, and an international status as a RAMSAR Site. Unfortunately, TNRAW has been disturbed by wildfire that could potentially degrade functions. The objectives of this study were (1) to analyze the appropriate factors that influenced the vulnerability of forest fires in TNRAW and the surrounding areas, (2) to optimize forest fires management in TNRAW through early warning and spatial modeling, (3) to formulate strategic direction to control forest fires in TNRAW. This research used the CMA (Composite Mapping Analysis) method. Based on the analysis of the 13 variables associated fuels and ignitions, it was formed 9 variables that built the hotspots density model in the study area, they were the average monthly temperature, elevation, slope, level of income, population density, land cover, the buffers of rivers, the buffers of mangrove / marine and the buffers of district centers. The factors influenced spatially vulnerability of the forest fires in the study area were socio-economic (regional income) and land cover. On the other hand, the rainfall intensity influenced temporally frequency of wildfires. Best composite model had a determination coefficient of 25.8 % and formed a polynomial model. Three classes of fires vulnerability grouped by Arc GIS 9.3 based on summary mean and standard deviation, ie low vulnerability 815.32 km² (77.51 %), middle class 92.74 km² (8.8 %) and high vulnerability 137.14 km² (13.04 %). The widest class of high vulnerability was the buffer zone of 104.86 km² (9.97 %), and the core zone just only 2.1 km² (0.2 %). According to the spatial model, the effective forest fires management needed to consider socio-economic and land cover. The priority of the forest fires management needed to be focused at the core zone and the buffer zone from the foot of Watumohai Mountains to savanna near mangrove which high vulnerability for wildfires. The chosen strategy based on Quantitative Strategic Planning Matrix (QSPM) method by score of 5.87 was the threats analysis and diversification of forest fire control method, devided into preventif and repressive methods, such as socialization, promotion, infrastructure development, law enforcement and fire suppression.
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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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