Judul: Remote Sensing Application and Spatial Database Design  For Sustainable Management of Coral Reefs in Banda  Island, South Maluku, Indonesia
Abstrak: Two LANDSAT-7 ETM satellite images of the study area were processed to apply the Bottom Feature Index and perform ISOclass unsupe~ised classification. The images were then mosaicked to create a classified image showing the entire archipelago of Banda Island. The result was a raster image with classified features deviating relatively less from actual measurements of area in Banda Island.
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Judul: Klasifikasi Ekosistem Terumbu Karang Berbasis Objek dan Piksel di Pulau Morotai
Abstrak: Most researches on the classification ofcoral reef benthic habitat mapping were conducted on pixel base analyses. This technique may lead to some extent of misclassification of coral reef ecosystem due to a high diversity and high spatial heterogenity. An alternative technique to reduce misclassification, an object-based image classification (OBIA) algorithm was proposed. This techniques first decomposed an image into relatively homogeneous objects or areas and then classified these objects (areas) instead of pixels. OBIA has been well developed and applied in terrestrial studies in past decades, but the method has not been used adequately on benthic habitat mapping. In addition to an image classification technique as one of the component in the analysis of remote sensing, the selection of a classification algorithm was also an important factor because it can largely affect the final result. Benthic habitat mapping studies of coral reefs are generally applied with traditional classification algorithms such as Maximum Likelihood (ML), which requires the spectral response of each class following the pattern of normal distribution (Gaussian distribution). In contrast to a contemporary algoritm, the machine learning algorithm has received more attention in the benthic habitat mapping for producing higher accuracy thanML classification algorithms. Currently, the machine learning algorithm has been widely used in the commercial remote sensing data processing both on pixel-based and object-based. The algorithm is also potential to be developed for coral reef ecosystem mapping. Application of an object-based classification technique with machine learning algorithm is still limited in Indonesia, especially for mapping of coral reefs, therefore, it requires an intensive study to develop this algorithm as an alternative for traditional algorithm. The general objective of this research was to identify the effectiveness of the application of satellite image classification techniques in generating detail mapping information of coral reef ecosystems. To achieve this objective, the following special objectives were developed: (1) to develop a systematic approach in the coral reef ecosystem classification to define the classification scheme, (2) to mapping coral reef ecosystems using object-based classification techniques with advanced classification algorithms (machine learning) on satellite image data, and (3) to assess status of coral reef ecosystems within the last few decades. Results of this study were expected to be used for consideration in the sustainable management of coral reefs ecosystem in the District of Morotai Island, especially in North Maluku, and can be used as a baseline to support the development of the field of mapping and remote sensing of coral reef ecosystems. This research was conducted in the coral reef ecosystem located in the western part of the Morotai island in North Maluku province. The study stepsconsisted of equipment preparation, data collection, data processing, and preparation of dissertation begins in September 2012. Field data collection was held in October 2012, while processing and data analyses were conducted from January v to September 2013. The method used in the study consisted of field data collection using the technique of line transects photo quadrat, pre-processing of Landsat 5TM, 7EMT + and 8OLI, the development of a classification scheme with ecological approach quantitative analysis based grouping in a hierarchical
Keyword: change detection, classification scheme, Morotai Island, object and pixel based classification, support vector machine

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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