Judul: Performance of logistic regression model for predicting deforestation, case study: cikepuh wildlife reserve and cibanteng natural reserve
Abstrak: Cikepuh Wildlife Reserve and Cibanteng Natural Reserve, since both conservation area was established in 1973 and 1925 have been facing complex problem caused by land use changed, deforestation, illegal hunting, forest fire, and so on. Deforestation itself is a complex socio-economic, cultural, and political event. This thesis focused on what factors affect the rate of deforestation by considering some common driving forces of deforestation and using logistic regression for predicting deforestation. It is clearly important to know where deforestation is likely to occur. The objectives of the thesis are to quantify the contribution of each deforestation driving factor such as distance from center of dweller, aspect, slope, distance from shore line, distance from existing road, and elevation, and to elaborate spatial projection of future trends of deforestation based on possibility of deforestation as the result of logistic regression equation. The methodology is using Stacking Method from CI (Conservation International) CABS (Center for Biodiversity Applied Science) and developed together with WCS IP (Wildlife Conservation Society – Indonesia Program). Two image with different dates or one period was stacked and analyzed by visualization from both images. Signature area was extracted from the stacked-images by using shapefile polygon for forest to forest class, forest to non forest class, non to non forest class, water, cloud and shadow. Signature area should be represented certain spectral characteristic, so for obtaining number of class as many as possible, it could use 16 bit data type indeed 8 bits. Classification method is supervised classification that was done by CART ERDAS Imagine plug in tool and See5, a stand alone decision-tree based classification program. The result of classification is thematic raster image with forest change attribute. Analysis was done in one attribute table of polygon vector cell (PVC), that is created by using Edit Tool Vector Grid, an extension from ArcView 3.3. All attribute of independent variables fill the squared-shaped polygon as called PVC, and the result probability of logistic regression as the result of the calculation as well. Independent variable is divided to two binary category 0 and 1. 1 is a parameter that tends to occur deforestation such as less 1 km distance from road. 0 is stable condition that there is no change from forest to non forest. The result of possibility deforestation occurrence is if the road distance less than 1 km, tends to deforested occurrence 3 times compare the distance greater or equal 1 km. The smallest possibility of deforestation occurrence was contributed by predictor distance 1 km from river, and almost has no effect to deforested occurrence. Regression logistic equation in this thesis can predict deforestation significantly, although some processes of polygon vector cell could not accommodated to assign data from attribute of independent variables to polygon vector cell exactly. Regression logistic model could predict deforestation better if distribution of independent variables that are assumed to tend to deforestation occurrence distribute evenly entire the study area.
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Judul: Lintasan dan Model Spasial Deforestasi di Kalimantan Periode 2000-2013
Abstrak: Carbon emissions in Indonesia was contributed by the forestry sector. Yet, forest absorbs carbon emissions by photosynthesis processes and stores it in the forest biomass. This conditions causes the forestry sector could played a main role in the efforts to reduce global warming. Emission in forestry sector is mainly caused by deforestation for various purposes. Sustainable forest management approaches as important mechanisms to ensure balance in the use of forest resources for humans and the environment. Kalimantan island has large forest cover and has high rate deforestation. The trajectory and spatial model of deforestation was expected to provide more detailed information about the phenomenon of deforestation in Kalimantan that could be organized on sustainable forest management planning. This study used time-series land cover data from Ministry of Forestry in 2000–2013 and validated with SPOT 5/6 images in 2013. land cover data was used to analyzed the trajectory and spatial model of deforestation. The spatial model of deforestation were developed using logistic regression with data in 2000–2006 and then implemented in 2006–2013. The variables that used to predicted influence on the models were slope, altitude, peatlands, distance from forest edge, distance from plantations edge, distance from road, distance from river, farmer households, human population and Gross Regional Domestic Product (GDRP). Trajectory analysis of deforestation in Kalimantan in the period 2000-2013 suggests that a deforested has not been optimally exploited because most of land cover were still in the form of shrubs until the end of the period. The results of logistic regression analysis that obtained spatial model of deforestation in Kalimantan Pi = 1.1480714 - (0.033262*slope) - (0.002242*elevation) - (0.000413*distance from forest edge) + (0.000045*GDRP). The result of validation test of deforestation model in 2000–2006 has a value of overall accuracy about 79.64% and model of deforestation in 2006–2013 has value of overall accuracy about 77.01%.
Keyword: trajectory, spatial model, logistic regression, deforestation, Kalimantan

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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