Judul: Modeling Spatial Dynamic of Coral Reef on The Small Island, Spermonde Archipelago (Case Study : Barrang Lompo Island, Makassar District, Indonesia)
Abstrak: The coral reef in the Coral Triangle Asia has been directly threatened by human activities and natural threats. The coral reef destructions have also happened in Spermonde Archipelago especially Barrang Lompo Island (COREMAP II 2010). Barrang Lompo Island contributes highly to the society, the most livelihoods of which depends on its shallow water. Therefore, spatial dynamic mapping and spatial prediction model for future trend of coral reef are needed to create good spatial planning for coastal area using remote sensing data and M-CA Model. The objective of this research was to analyze dynamics of coral reef by using remote sensing data and also to predict the future trend of coral reef change. The research was conducted from November 2012 to December 2013 at the shallow water of Barrang Lompo Island. There are several data used in this study. Landsat TM / ETM+ of 1993, 1997, 2002, 2007, and 2012 were processed using image classification for coral reef map. Probability maps were derived from multiplication maps of salinity, sea surface temperature (SST), Dissolve Oxygen (DO), pH, total suspended sediment (TSS), water clearness, bathymetry, and the constraint map used were land area and deep water in Barrang lompo with the assumption that the area will not change into coral reef for 10 years. This research has been conducted in several steps: a) ground truth, b) image processing, c) Markov chain analysis, d) Multi criteria evaluation (MCE) analysis, e) Cellular Automata (CA) analysis, f) validation and, g) coral reef change prediction. The main measurement data was derived from ground truth. Ground truth data from the field were used for image processing. They were applied on the required habitat types of the whole study area by using Global Position System (GPS). Image processing was used to classify coral reef at different times. The classified images were used as input for Markov chain analysis in order to get transition area matrix. MCE analysis was used to create a probabilities map using several parameters. The results of image processing, Markov chain analysis and MCE were used as inputs to perform the CA model. The final result of this model was the coral reef change prediction. The result of simulation from CA process was validated using the actual coral reef map obtained from Landsat image to evaluate the prediction result. If the result validation standard agreement is >75%, then it can be used to predict the future trend coral reef for the next 10 years. Based on the confusion matrix, the result classification in 2012 showed the overall accuracy was about 80.24%. The result of analysis dynamic that occurred during 1993 to 2012 showed live coral and seagrass area decreased approximately by 11.21 ha (12.45%) and 3.57 ha (8.02%) while dead coral increased up to 5.05 ha (86.18%), rubble increased 5.14 ha (68.72%), and sand increased 4.59 ha (35.23%). Live coral and seagrass areas decreased from 1993 to 2012, and the greatest decrease occurred from 2002 to 2007 covering the area of 4.51 ha and 1.47 ha, respectively. The second greatest decreases for coral and seagrass was 4.15 ha
Keyword: Coral Reef, Markov Cellular Automata, Remote Sensing, Spatial Dynamic

Judul: Modeling Spatial Dynamic of Coral Reef on the Small Island, Spermonde Archipelago (Case Study: Barrang Lompo Island, Makassar District Indonesia).
Abstrak: The coral reef in the Coral Triangle Asia has been directly threatened by human activities and natural threats. The coral reef destructions have also happened in Spermonde Archipelago especially Barrang Lompo Island (COREMAP II 2010). Barrang Lompo Island contributes highly to the society, the most livelihoods of which depends on its shallow water. Therefore, spatial dynamic mapping and spatial prediction model for future trend of coral reef are needed to create good spatial planning for coastal area using remote sensing data and M-CA Model. The objective of this research was to analyze dynamics of coral reef by using remote sensing data and also to predict the future trend of coral reef change. The research was conducted from November 2012 to December 2013 at the shallow water of Barrang Lompo Island. There are several data used in this study. Landsat TM / ETM+ of 1993, 1997, 2002, 2007, and 2012 were processed using image classification for coral reef map. Probability maps were derived from multiplication maps of salinity, sea surface temperature (SST), Dissolve Oxygen (DO), pH, total suspended sediment (TSS), water clearness, bathymetry, and the constraint map used were land area and deep water in Barrang lompo with the assumption that the area will not change into coral reef for 10 years. This research has been conducted in several steps: a) ground truth, b) image processing, c) Markov chain analysis, d) Multi criteria evaluation (MCE) analysis, e) Cellular Automata (CA) analysis, f) validation and, g) coral reef change prediction. The main measurement data was derived from ground truth. Ground truth data from the field were used for image processing. They were applied on the required habitat types of the whole study area by using Global Position System (GPS). Image processing was used to classify coral reef at different times. The classified images were used as input for Markov chain analysis in order to get transition area matrix. MCE analysis was used to create a probabilities map using several parameters. The results of image processing, Markov chain analysis and MCE were used as inputs to perform the CA model. The final result of this model was the coral reef change prediction. The result of simulation from CA process was validated using the actual coral reef map obtained from Landsat image to evaluate the prediction result. If the result validation standard agreement is >75%, then it can be used to predict the future trend coral reef for the next 10 years. Based on the confusion matrix, the result classification in 2012 showed the overall accuracy was about 80.24%. The result of analysis dynamic that occurred during 1993 to 2012 showed live coral and seagrass area decreased approximately by 11.21 ha (12.45%) and 3.57 ha (8.02%) while dead coral increased up to 5.05 ha (86.18%), rubble increased 5.14 ha (68.72%), and sand increased 4.59 ha (35.23%). Live coral and seagrass areas decreased from 1993 to 2012, and the greatest decrease occurred from 2002 to 2007 covering the area of 4.51 ha and 1.47 ha, respectively. The second greatest decreases for coral and seagrass was 4.15 ha
Keyword: Coral Reef, Markov Cellular Automata, Remote Sensing, Spatial Dynamic

Judul: Definisi zat besi ( Fe ) pada anak babi
Abstrak: Dalam bidang sub sektor peternakan, ternak babi mempunyai prospek yang cukup baik untuk dikembangkan dalam rangka meningkatkan eksport non migas. Namun anak-anak babi yang sedang menyusu sering mengalami defisiensi zat besi (Fe). Bila masalah ini tidak segera ditanggulangi, peternak akan mengalami kerugian. Zat besi dapat diperoleh dari makanan yang berasal dari hewan (tepung tulang, tepung ikan, tepung daging, "skimmed milk"); tumbuh-tumbuhan (leguminosae, rumput-rumputan, biji-bijian) dan mineral alam (Girindra, 1979). Dalam tubuh hewan, zat besi berperanan sebagai pembawa 02 ke seluruh jaringan, juga sebagai pembawa oksigen pernafasan di dalam sel urat daging (Underwood, 1981). Dalam duodenum dan yeyenum bagian atas, besi dalam bentuk ferro akan diserap. Penyerapan ini akan diatur berdasarkan teori "mucosal block" dari Granick. Kemudian dalam plasma darah, besi akan berikatan dengan B₁ globulin dan membentuk transferrin, yang akan segera dibawa ke seluruh jaringan. Kelebihan besi akan diikat oleh apoferritin dan membentuk ferritin yang ditimbun sebagai besi cadangan, terutama di hati. Bila kapasitas besi yang dapat disimpan sebagai ferritin telah jenuh, maka besi akan ditimbun sebagai hemosiderin. Zat besi dalam jumlah yang sedikit akan diekskresikan melalui pengelupas- an epitil, tinja, air kemih, cairan empedu, keringat. ...
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