Judul: Penapisan Bakteri Asosiatif Karang Lunak (Octocorallia; Alcyonacea) Sinularia dura dan Lobophytum strictum yang Potensial sebagai Alternatif Penanganan Microfouling di Laut.
Abstrak: Microfouling (pembentukan biofilm) merupakan syarat utama terjadinya biofouling, sehingga untuk melakukan penghambatan terjadinya biofouling pada struktur bangunan laut dapat dilakukan dengan menghambat terjadinya biofilm. Beberapa mikroorganisme laut seperti bakteri yang berasosiasi dengan karang lunak mampu menghasilkan sejumlah metabolit sekunder yang dapat menghambat pertumbuhan biofilm. Tujuan dari penelitian ini adalah mencari bakteri asosiasi potensial dari karang lunak Sinularia dura dan Lobophytum strictum yang dapat menghasilkan senyawa bioaktif sebagai sumber anti-microfouling alami. Penelitian ini dilakukan pada bulan Mei hingga Agustus 2011. Penelitian dibagi menjadi beberapa tahap, yaitu pengambilan sampel karang lunak jenis Sinularia dura dan Lobophytum strictum, penjebakan biofilm bakteri, isolasi bakteri, dan uji antagonisme. Pengambilan sampel karang lunak dan penjebakan biofilm bakteri dilakukan pada waktu dan tempat yang sama yaitu di perairan Barat pulau Pramuka, Kepulauan Seribu. Uji antagonisme dilakukan antara bakteri asosiasi karang lunak terhadap bakteri pembentuk biofilm dengan metode difusi agar. Parameter kualitas perairan (fisika-kimia) yang diukur sebagai data penunjang meliputi suhu, kadar garam, kecerahan, pH, nitrat, fosfat, TSS, dan COD. Hasil penelitian menunjukkan bahwa sebanyak 24 isolat bakteri asosiasi berhasil diisolasi dari karang lunak jenis Sinularia dura dan Lobophytum strictum. Uji antagonisme menunjukkan sebanyak 23 isolat (95,83%) bakteri asosiasi mampu menghambat pertumbuhan bakteri pembentuk biofilm. SD6 dan LS1 merupakan isolat bakteri yang memiliki tingkat aktivitas penghambatan paling tinggi terhadap bakteri pembentuk biofilm. Bakteri SD6 dan LS1 tersebut berpotensi sebagai sumber anti-microfouling alami yang ramah lingkungan.
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Judul: Eksplorasi Spasial Karang Lunak Kaitannya dengan Senyawa Bioaktif Bakteri Simbion.
Abstrak: In ecology, soft and hard corals have the same function in the waters. The distribution of soft corals is strongly influenced by the characteristics of the aquatic environment. Soft corals had been knows contain of bioactive compounds that can be used as a marine natural product, but naturally theses resource are limited and very difficult to cultivated. Therefore, the isolation of bacterial symbiont on soft coral is one of alternatives in an effort to utilize these resources in sustainability. This study had four main objectives that include the following: (1) to determine the differences of the soft corals between sheltered and exposed area in Pongok Island, Soulth of Bangka and Tegal Island in Lampung Bay, Indonesia; (2) to find of the soft corals inhibition potential for antibacterial activity; (3) to measure the antibacterial activity level and minimum inhibitory concentration of bioactive compounds in the soft coral S. flexibilis and S. polydactyla species; (4) to find the isolation of bacterial symbionts from soft coral S. flexibilis and S. polydactyla species that the potential to producted bioactive compound. The results found eigtht types of soft corals classified in four genera (Lobophytum, Nephthea, Sarcophyton and Sinularia) were found in both sites, six species were identified in the waters of South Bangka (at 9 meters depth) and two species (at 3 meters depth). In Tegal Island waters (closed waters), only 2 species from the genus Sinularia were recorded (at 2-5m depth). There were 2 stations at Pongok Island namely, PGK1 and PGK2, while in Tegal Island, there were 4 stations namely TGL1, TGL2, TGL3 dan TGL4. Principal component analyses showed that both waters can be classified based on their physical-chemical characteristic, which were depth, currents, phosphate and turbidity waters. Discriminant function were showed in phosphate variable. While the similarity analysis showed that both waters had very strong similarity of about 79%. In sampling treatment, the biomass among wet and dry samples varied. The highest shrinkage due to drying was found in Nephthea, followed by Lobophytum and Sarcophyton, while the lowest was found in the genus of Sinularia. Extracted from 12 samples of soft coral, the lowest extract weight was found in the semipolar solvent (EtOAc), while the extract weight of n-Hex and MeOH was vary. Inhibition power from all extract samples were found on soft coral extract in the EtOAc and MeOH solvents. The highest value of inhibition power was found in soft coral of Sinularia polydactyla and S. flexibilis within strong catogery. Principal component analysis showed there were corelations between the the environment characteristics with the bioactive compounds inhibition of soft corals, that the identifier parameters include; depth, currents, phosphate and turbidity waters. Discriminant function were showed in depth variable. Extracts weight of both species S. flexibilis (SFTLS4) and S. polydactyla (SPTLS2) found the same relatively, except extract in n-Hexane (n-Hex) solvent from SFTLS4 samples that higher compered to extract weigth in other solvents. vii Antibacterial activites of both spesies showed only on extract in ethyl acetate (EtOAc), within strong category. For purification were obtained eigth fractions (F1-F8) for SFTLS4 extractions and six fractions (F1-F6)
Keyword: antibacterial activity, bacterial symbiont, bioactive compound, sinularia, soft coral

Judul: Pemodelan Nilai Tukar Rupiah terhadap Dollar Amerika Menggunakan Deret Waktu Hidden Markov Empat Waktu Sebelumnya
Abstrak: The exchange rate of Rupiah to US Dollar fluctuates according to time in a long period. An existing exchange rate might happen again in the future. Assume that the causes of the fluctuation are not observed directly and form a Markov chain, then the exchange rate of Rupiah to US Dollar can be modeled by the hidden Markov time series. In this research, the model used to describe the exchange rate of Rupiah to US Dollar is the four previous time in hidden Markov time series. This model assumes that the exchange rate of Rupiah to US Dollar depends on Markov chain explained by the cause of occurence and the four previous exchange rates of Rupiah to US Dollar. The model parameter is estimated by using the maximum likelihood method, while the estimation itself is calculated using the expectation maximization algorithm, which is implemented using Mathematica. After estimating the parameter, the exchange rate of Rupiah to US Dollar are calculated. The results are as follows. The symmetric mean absolute percentage error (SMAPE) is 2,1%, the maximum absolute error is 19,16%, the minimum absolute error is 0,0036%, and only 1,95% of the whole absolute percentage error that is bigger than 10%. These results show that the four previous time in hidden Markov time series can describe the exchange rate of Rupiah to US Dollar very well.
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