Judul: Suitability Analysis for Determining Potential Seaweed and Pearl Oyster Cultivation (A Case Study in Saleh Gulf)
Abstrak: In recent years, particular geographic information systems can utilize area for spatial reasoning. Marine suitability analysis is a prerequisite for sustainable seaweed and pearl oyster cultivation production and it involves evaluation of the parameter such as depth, brightness, salinity, pH, dissolve oxygen, and temperature as infrastructure. In approach of multi criteria decision making, the objective of this research is to analyze the marine suitability using minimum threshold criteria, simple additive weighting (SAW), and fuzzy logic. The parameters in these methodologies will be interpolated and reclassified as a set rule for the coastal marine suitability for seaweed and pearl oyster cultivation. The method of minimum threshold criteria implements matching of two different classes and results the lower class. The second method is using simple additive weighting that implements multiplication of score and weight on marine criteria. The third method is using a fuzzy decision analysis. The comparative analysis of the three methods for seaweed majority indicates that classification of marine coastal suitability belongs to N class based on minimum threshold criteria. SAW method indicates produces two classes such as N and S3 class, while fuzzy method produces three classes: S1, S2, and S3. In SAW and fuzzy method indicates S3 class is higher than N class. The three methods for pearl oyster cultivation shows that the suitable marine using threshold criteria method which produce N classification. The suitable marine using simple additive weighting method produce two classifications: N and S3. S3 classification replies the biggest area than N classification. The suitable marine using fuzzy method produces three classifications: S3, S2 and S1 class, which describes that S1 class is larger than the other class in this location, and suitable for pearl oyster cultivation.
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Judul: Development of Land Suitability Evaluation System for Coastal Aquaculture Using Artificial Neural Network and Geographical Information Systems (Case Study: Mahakam Delta, East Kalimantan)
Abstrak: Land suitability is the aptitude of given type of land to support a certain use. Land suitability is one of key factors for the sustainability of aquaculture system. It can be determined by using matching methods between land suitability criteria and land characteristics, such as topography, hydrography, climate, water characteristic, risk, land cover, and spatial plan aspects. Evaluation of land for aquaculture suitability can be reached using a parametric approach, which is implemented by using the distinguish land characteristics and combination of it. Parametric approach uses the numeric value, which classifies the land based on individual characteristics. The objectives of the research are to develop a land evaluation system on aquaculture suitability by using artificial neural network (ANN) and geographical information system (GIS) and to evaluate the performance of the new developed system as compared to GIS vector spatial analysis method. Four types of suitability were defined, i.e. highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (N). The new developed GIS model uses the raster format and ANN classifier. The performance of GIS model was then evaluated, and compared to the vector map overlay that used GIS spatial analysis features, including suitability distribution, covered area, processing speed and validity. The multilayer feedforward of the ANN with backpropagation learning algorithm was chosen to do the classification. GIS overlaid layer in form of spatial database was then converted into training dataset, validation dataset and prediction dataset. Training and validation dataset contains a pair of input and output, while the prediction dataset contains input data only. To compare the two methods, the Mahakam Delta-East Kalimantan were selected as a case study. Ten thematic map layers of land characteristic were involved in both methods, i.e.: water salinity, dissolved oxygen, water pH, soil texture, soil drainage, distance to rivers, pollution risk, land cover type, land use plan and annual rainfall intensity. Each feature of thematic map layers were classified and scored using 1 to 4 ranges from worst to best, respectively. The performance of ANN together with GIS was reliable, it includes the accuracy on training dataset of 97%, and validation of 96%. Distributions of each suitability of both methods are located on the same location in the study area. Area covered in the study area was 188,666.19 ha, consisting of 21,202.20 ha (11.24%) of 3 S1, 87,190.83 ha (46.21%) of S2, 6,958.26 ha (3.69%) of S3 and 61,251.12 ha of N. Only 0.19% of total area represents the “Unclassified Area” and 6.20% is “No Data”. Unclassified areas may be caused by less representative training dataset. “No Data” class represents the area that was not included in the data processing by raster data model. The research also proved that ANN together with GIS raster do the classification faster than vector spatial analysis.
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Judul: Infeksi Virus Parainfluenza Pada Anjing
Abstrak: Virus parainfluenza anjing termasuk famili Paramyxoviridae, berbentuk pleomorf dengan ukuran bervariasi (bergaris tengah antara 100-500 nm), berpembungkus peka ether, berisi genom RNA dan secara serologis mempunyai kesamaan dengan virus SV-5 kera; dikenak sebagai salah satu kausa penting dari sindrom "kennel cough". Infeksinya dapat mengenai anjing semua umur yang belum pernah terinfeksi atau mendapatkan vaksinasi mengakibatkan gejala klinis ringan yang terbatas pada traktus respirasi atu infeksi yang secara klinis tidak memperlihatkan gejala nyata. Gejala klinis yang nampak setelah masa inkubasi (1-5 hari) terlewati berupa batuk yang tidak berat selama beberapa hari, berbagai tingkat tonsilitis dan pharyngitis dan kadang-kadang ditemukan kenaikan temperatur rektal antara 1-2 derajat C dari temperatur normal rat-rata. Anjing terinfeksi mengandung virus pada traktus respirasi bagian atas dan bawah serta limfonoduli sampai hari ke 9 setelah infeksi. Penyebaran virus antar anjing terjadi secara aerogen.
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