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: Klasifikasi Mangrove Berbasis Objek dan Piksel Menggunakan Citra Satelit Multispektral di Sungai Kembung, Bengkalis, Provinsi Riau
Abstrak: Mangrove that generally found in coastal regions plays important roles such as (1) coastal protection from hurricane, tsunami, wind and wave, (2) spawning ground for many fishes and other faunas, (3) a place for recreational, (4) source of nutrients for organisms, and (5) source of wood. Recently, mangrove ecosystems in Kembung River, Bengkalis Island Riau Province tend to get pressure from antrophogenic. Protection and management of mangrove ecosystems needs to be a serious concern of various parties. Such efforts require data acquisition techniques and information mangrove spatially and accurately. Field data were collected in June and December 2012. Satellite imageries were used in this study consisted of Landsat 5 TM, Landsat 7 ETM +, Landsat 8 OLI, SPOT 6 multispectral, SPOT 6 panchromatic, ALOS PALSAR and SENTINEL-1. Pre-processing in the satellite imageries were applied including atmospheric correction, radiometric calibration, geometric correction, and spectral transformation. Field data observations and measurements were conducted on mangrove vegetation and land cover. Scheme development of mangrove community classification was conducted by using clusters and similarity percentage (SIMPER) analyses. Furthermore, the scheme was used to charactirized satellite images spectral reflectance by using spectral analysis. Satellite data were classified by using object-based approach which applied random forest algorithm. Object-based classification results was then compared to pixel-based classification technique which used maximum likelihood algorithm. The succesfull object-based classification then applied on magrove change detection in the study area. Kembung River mangrove community was assembled by 69 mangroves species which consists of 22 true mangrove species and 47 species of associate mangrove. Xylocarpus granamun, Rhizophora. apiculata, Lumnitzera. racemosa and Scyphiphora hidrophyllaceae were dominant species and always found in the study area compared with other species, for all strata. Based on standard criterias and guidelines for the determination of mangrove destruction which issued by the Minister of the Environment No. 201 of 2004, Mangrove at Kembung River was categorized as good and very dense mangrove ecosystem. Based on the composition of mangrove species, one level classification scheme was succesfully developed. The scheme consisted of 12 mangrove community classes, however most of the mangrove species belong to the two main classes of Rhizophora apiculata (Ra) composed of 33 samples and Xylocarpus granatum (Xg) composed of 93 samples Other 10 classes had limited sample number, therefore the 10 other classes were groupped into one class (La) that composed of 28 samples. Both classification techniques were able to identify all land cover classes and mangrove communities. Some misclassifications were still found to produce salt and pepper effects on both classifications, however, the random forest algorithm v could reduced such errors than the maximum likelihood algorithm. The best result of land cover classification using object-based and pixel-based classifications was obtained through input image layer M6. The object-based classification approach was better than pixel-based and it can improved the classification results by 1-24.5%. Change detection analyses showed that the mangrove area in Kembung River was relatively stable. For nearly two decades, we found mangrove loss about 197.2 ha, gain of 251.1 ha, and unchanged of 2904.9 ha. Changes in mangrove covers were generally caused by anthropogenic factors
Keyword: classification, Kembung River, mangrove, object-based, pixel-based

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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