Malaria is a mosquito-borne disease caused by the existence of the Plasmodium parasite in female Anopheles mosquitoes. Tropical countries, such as Indonesia, provide a suitable place for these mosquitos to live and breed quite easily. Despite having an overall low case incidence, some regions in Java still have it worse than others. Purworejo Regency holds the highest place in this regard, with an Annual Parasite Incidence (API) value of 1.96 for every 1,000 population. This research aims to utilize physical, climate, and socio-demographic variables as a parameter for malaria incidence in Purworejo Regency, as well as to map the vulnerability of the said area towards malaria. Physical variables included vegetation density, landuse and landcover, elevation, and soil texture, while climate variables included rainfall and humidity. Settlement density was used as a socio-demographic variable in the emergence of malaria. Each of these variables weights was then calculated with Analytical Hierarchy Process (AHP) based on their ranks, resulting in elevation being the most prominent variable. The overlay was used to combine all the variables as well as to calculate the total score for existing polygons to decide their classification. The generated map from this particular scenario showed that Menoreh Mountains in the northern region and settlements close to dense vegetation in the southern region of Purworejo Regency were vulnerable to malaria. Moderately vulnerable class dominated 55.23% of Purworejo Regency, followed by 35.41% of the vulnerable class, and lastly 9.36% of the non-vulnerable class. Malaria cases from Purworejo Public Health Center year 2007-2011 were used as a comparison. Not all incidents that happened on-field fit perfectly within the generated map. As such, further research involving other variables and the latest malaria case as a comparison are needed.
Updating information on rice fields is very important to pay attention to environmental quality and food security. This is related to Indonesia's commitment to achieving Sustainable Development Goal number two in terms of agricultural data collection and analysis. Remote sensing can be used as an alternative method for identifying and mapping land cover, for instance paddy fields. Land cover in paddy fields varies greatly according to paddy growth phase, wherein these growth phase can be shown by different spectral reflectance values in remote sensing imagery. Mapping of paddy fields based on their spectral reflectance began to be widely carried out in Indonesia. Therefore, the aims of this study were to determine the spectral reflectance pattern of temporal paddy growth phase then form a map of the paddy fields based on those spectral libraries. This study used Spectral Angle Mapper (SAM) method to identify paddy fields on Landsat-8 OLI determined from spectral reflectance pattern of paddy-growth phase in some areas of Subang and Indramayu Regency in one growing season. The results succeeded in classifying paddy fields and non-paddy fields area. Classified paddy fields consisted of several land covers comprising the bare-land, inundation-land, vegetative, generative, and ripening. The accuracy test showed an overall accuracy of 70.07%. Misclassification in this study occurred due to the existence of thin cloud cover, besides there was a misclassification between built-up area and the bare land.
KEYWORDS: Landslide (networking), Data modeling, Raster graphics, Java, Roads, Data conversion, Digital imaging, Data processing, Agriculture, Analytical research
Landslide is caused by meteorological and geomorphological factors. Landslide is one of the most common disaster that occur in Indonesia. Purworejo is one of the potential area that could be experiencing landslides, because the geomorphological conditions which are included in Menoreh Hills are geographically sloping to very steep. Based on the Indonesian Disaster Information Data (DIBI) and the National Disaster Management Agency (BNPB) in the last five years from 2014 to April 2019 there have been 64 landslides in Purworejo. To reduce the impact of landslide, effective evacuation routes are needed. Determining of evacuation routes can be done in various methods, one of methods is use a spatio-cost approach. The purpose of this research is to determine the most effective evacuation routes to reduce the impact of landslide. Spatio-cost parameters obtained by certain paramaters. The parameters are physical parameters and some social parameters derived from the appearance on the surface of the earth, such as housing, number of population, land use, slope direction, roads and also the wide of the roads. These parameters are processed to look for evacuation routes using Least Cost Path (LCP) method. The expected result of this research is evacuation routes that can help people around disaster-prone areas to prepare. This on going research is important to improve disaster manajemen in Indonesia, especially for landslide in Bruno, Purworejo, Central Java.
KEYWORDS: Landslide (networking), Data modeling, Visual process modeling, Visualization, 3D modeling, 3D visualizations, Information visualization, Roads, Raster graphics, Associative arrays
Indonesia is one of the disaster-prone countries. Based on the Indonesian Disaster Information Data (DIBI) and the National Disaster Management Agency (BNPB) in the last five years from 2014 to April 2019 there have been 65 landslides in Purworejo. Landslide is one of the most common disaster that occur in Indonesia. Landslide is caused by meteorological and geomorphological factors. Purworejo is one of the potential area that could be experiencing landslides, because the geomorphological conditions which are included in Menoreh Hills are geographically sloping to very steep. Landslide susceptibility modeling in Purworejo Regency was carried out using three different methods, namely Information Value Model (IVM), Information Value Model-Analytical Hierarchy Process (IVM-AHP) and Information Value Model-Gray Clustering (IVM-GC). Each modeling is conducted using the Natural Breaks (Jenks) method to produce five classes, namely very low, low, medium, high and very high class based on the IVM value of each method. This research’s goal is to visualized 3 maps of modelling results. The visualization used is 3-dimensional mapping. This mapping is intended to make it easier to compare the map results of modeling that have been done before. The expected results of this study are accurate and reliable 3-dimensional visualization to study the advantages and disadvantages of each of the modeling methods used.
Tin mining is one of the main sectors of the national economy where the Bangka Regency is the largest tin producer in Indonesia. However, this sector cannot be separated from the pros and cons for a long time. In a way, this sector can increase both national and regional income but on the other side, the adverse effects of it can threaten the survival of humans and the environment. Open tin mining activity has converted previously vegetated land cover become the nonvegetated land cover. Furthermore, the land cover changes to the mining area have a major impact on global warming which has become an international issue in the past few decades. This research aims to map and measuring land cover changes especially from vegetated to non-vegetated land cover related to tin mining activity in Bangka Regency. This research using multitemporal Landsat imagery data acquisition in the year 2004 (Landsat 5 TM) and 2017 (Landsat 8 OLI) through digital image processing using Maximum Likelihood Classifier method. Previously, the image as a classification input through relative radiometric normalization. The result shows that tin mining activity in Bangka regency for thirteen years causes an area reduction in vegetated land cover. These results are expected to be an important input in policymaking for local governments to support the action plan which leads to mitigation of climate change.
Malaria is one of deadly infectious diseases commonly found in tropical countries, and until now its preventive efforts are still going on. From a spatial-analytical perspective, the preventive efforts can be done by developing malaria vulnerability maps, which can be used as a basis for risk management. Remotely sensed imagery is a powerful source for collecting relevant spatial data for that purpose. Among various models, there are four analysis methods for generating such maps, i.e. scoring, matching, spatial multi-criteria evaluation (SMCE) and geographically weighted regression (GWR), which have been compared according to their effectiveness and accuracies. The authors tested those methods in Purworejo Regency, Central Java, Indonesia, which has been recognized as a malaria endemic area. This study used Landsat-8 OLI imagery as a basis for deriving spatial parameters closely related to malaria vulnerability . Each vulnerability spatial model’s accuracy was then evaluated by calculating the number of cases found in the field, with respect to each vulnerability class, and then compiling all values using cross tabulation. It was found that, among other methods, the SMCE-based malaria vulnerability map statistically delivered the best result.
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