Judul: Marine Basic Spatial Unit Grid Optimization for Ecosystem Extent in Ocean Accounts
Abstrak: Luasan ekosistem merupakan salah satu struktur dalam Neraca Sumberdaya Laut yang diukur dalam satuan luasan spasial dengan grid MBSU. Penelitian ini bertujuan menentukan ukuran grid dan teknik gridding MBSU optimal untuk perhitungan luasan ekosistem perairan Indonesia (Teluk Saleh). Analisis spasial dengan coarse-graining digunakan untuk membandingkan 4 (empat) skema pemrosesan grid. Persen error dari luasan ekosistem hasil teknik hierarki berada di kisaran 7,54-44,06%, sedangkan teknik dominansi 1,46-2,20%. Skema dengan teknik dominansi memperoleh perbedaan hasil antara ukuran grid 10x10 meter dan 25x25 meter tidak berbeda nyata, dibuktikan dengan p-value 0,479, 0,239, dan 0,329 untuk ekosistem mangrove, lamun, dan terumbu karang. Uji akurasi memperoleh overall accuracy di atas 85% untuk teknik dominansi baik ukuran 10x10 meter maupun 25x25 meter, sehingga hasil keduanya dianggap baik. Dominansi merupakan teknik pemrosesan MBSU yang lebih optimal, baik menggunakan ukuran grid 10x10 meter maupun 25x25 meter, namun jika mempertimbangkan lama waktu pemrosesan, grid 25x25 meter lebih efisien., Ecosystem extent is one of the structures in Ocean Accounts, which is measured in spatial units using the MBSU grid. This study aimed to determine the optimal MBSU grid size and gridding technique for ecosystem extent of Indonesian waters (Saleh Bay). Coarse-graining spatial analysis was used to compare 4 (four) grid processing schemes. The percent error of the ecosystem area resulting from the hierarchy technique was in the range of 7.54-44.06%, while the dominance technique was 1.46-2.20%. In dominance technique, the difference between grid sizes of 10x10 meters and 25x25 meters was insignificant, as evidenced by the p- values of 0.479, 0.239, and 0.329 for mangrove, seagrass, and coral reef ecosystems respectively. The accuracy test obtained overall accuracy of above 85% for both 10x10 meter and 25x25 meter dominance techniques, so the results were considered good. Dominance was a more optimal processing MBSU technique, both using 10x10 meter and 25x25 meter grid sizes, but considering the processing time, the 25x25 meter grid was more efficient.
Keyword: Ocean Accounts, ecosystem extent, MBSU grid, coarse-graining

Judul: Geomorphological Zone and  Benthic Habitat Mapping Using SPOT-7 Imagery in Kelapa and Harapan Island
Abstrak: Perairan dangkal mempunyai ekosistem yang berkaitan yakni lamun, mangrove, dan karang. Geomorfologi merupakan ilmu yang membahas bentuk muka bumi. Bentuk muka bumi yang terbentuk pada laut mempunyai kedalaman tertentu. Adanya satelit SPOT-7 yang memproduksi citra resolusi tinggi menghasilkan metode Object Based Image Analysis (OBIA). Penggunaan algoritma Support Vector Machine (SVM) pada metode OBIA menghasilkan akurasi yang tinggi. Penelitian ini dilakukan pada perairan Pulau Kelapa dan Harapan, Kepulauan Seribu, DKI Jakarta. Penggunaan metode OBIA dan klasifikasi algoritma SVM dengan 3 level segmentasi. Level 1 menghasilkan 3 kelas yaitu, perairan dalam, perairan dangkal, dan darat. Hasil klasifikasi level 2 menghasilkan 4 kelas zona geomorfologi dengan berbagai kedalaman yaitu, crest, slope, flat, dan lagoon. Zona flat mendominasi lokasi penelitian. Keempat zona tersebut dapat dipetakan kedalamannya dengan baik. Klasifikasi level 3 menghasilkan 6 kelas yaitu DCA, karang, lamun, rubble, makroalga, dan pasir. Kelas lamun mendominasi area penelitian. Akurasi secara keseluruhan yang didapatkan mencapai 76.47%., Shallow water has some ecosystem that links each other: seagrass, mangrove, and coral. Geomorphology is a science about the shape of the earth's surface. The surface of the planet has a different depth. The appearance of the SPOT-7 imagery satellite that produced high-resolution images developed the Object-Based Image Analysis (OBIA) method. Using the Support Vector Machine (SVM) algorithm in the OBIA method has high accuracy results. This research took place in Kelapa and Harapan Island, Kepulauan Seribu, Jakarta Province. OBIA method and classification using SVM algorithm for 3-level segmentation. Level 1 obtained three classes: deep water, shallow water, and land. Classification level 2 received four categories for geomorphology zone, with the difference in depth: crest, slope, flat, and lagoon. The flat area dominated the research location. The bathymetry of all the four-zone got mapped well. Classification level 3 generated six classes: DCA, coral, seagrass, rubble, macroalgae, and sand. Seagrass class dominated the research area. The classification results produced an overall accuracy value of 76.47%.
Keyword: Geomorphology Zone, Benthic Habitat, SVM, SPOT-7, OBIA

Judul: Prenilasi Senyawa β-Diketon sebagai Prekursor 3-Prenilflavon
Abstrak: One group of flavonoids that has not been commonly synthesized is 3- prenylflavone. The main objective of this experiment was to synthesize precursor of 3-prenylflavone. Firstly, β-diketone was synthesized via 4 steps from phenol as starting compound with total yield of 24%. Prenylation of β-diketone with prenyl bromide and K2CO3 base produced 4 fractions which were separated by using preparative thin layer chromatography (PTLC) with n-hexane:EtOAc (8:2) as the eluent. Fraction with Rf = 0.80 was further separated by using preparative PTLC with n-hexane:EtOAc (99:1) as the eluent. Two fractions were obtained with Rf = 0.38 and 0.42. The first fraction has been characterized as diprenylated β-diketone with 49% yield. Application of protecting group on the phenolic OH group in β- diketone are needed to obtain monoprenylated β-diketone which can be further cyclized into 3-prenylflavone
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