Judul: Ekstraksi Senyawa Aktif Antioksidan dari Lintah Laut (Discodoris sp.) asal Perairan Kepulauan Belitung.
Abstrak: Radikal bebas terbentuk secara terus-menerus dalam tubuh manusia, baik melalui pengaruh eksogen maupun endogen. Radikal bebas adalah senyawa kimia yang memiliki satu atau lebih elektron tidak berpasangan di kulit terluar sehingga sangat reaktif dan mampu bereaksi dengan protein, lipid, atau DNA. Reaktivitas radikal bebas ini dapat diredam oleh senyawa antioksidan. Antioksidan sintetik yang berkembang saat ini dikhawatirkan memberi efek samping yang berbahaya bagi kesehatan manusia. Potensi antioksidan alami harus dikembangkan untuk memperoleh antioksidan yang lebih aman dikonsumsi. Salah satu sumber daya perairan yang berpotensi sebagai penghasil antioksidan alami adalah lintah laut (Discodoris sp.).
Keyword: 

Judul: Ekstraksi dan identifikasi senyawa bioaktif lintah laut (Discodoris sp.) sebagai antioksidan
Abstrak: One of the commodities produced from the ocean that have a bioactive compounds is a sea slug (Discodoris sp.). That is very interesting to study mainly deals with the nature of both chemical and biochemical characteristics and their use for the field of food and health. The purpose of this study is to determine the nutrient content of sea slug, determine antioxidant activity, and determine the bioactive compounds of sea slug. The experiment was conducted with several stages: sample preparation, extraction bioactive compound, fractionation by TLC and identification of compounds by GC-MS. Sea slug from the island of Madura Pamekasan potential as a source of protein, fat, and minerals. Sea slug has esensial amino acids amount to 5,57% was dominated by leucin of 1,42%. Nonesensial amino acids amount to 6,54% wich was dominated by glutamic acid 2,19%. Saturated fatty acids amounted to 27.53% of sea slug was dominated by palmitic (C16:0) which was 13.36%. Unsaturated fatty acids amounted to 34.66%, which was dominated by the essential fatty acids linolenic (C18:3, n-3) 20.91%. The highest yield of the crude extract was ethanol of 6,97% and contained alkaloid, steroids, saponin, phenols, carbohydrates and reducing sugar compound. The bioactive compounds in the meat of sea slug with ethanol solvent was galoxolide, dibuthyl phthalate, di-n-octhyl phthalate, oleic acid amide, erucylamide, squalene and has an IC50 best antioxidant activity in fraction 5 at 150.92 ppm.
Keyword: 

Judul: Modelling National Area Harvested of Paddy Using GARCH Methods (Generalized Autoregressive Conditional Heteroscedastic) Model.
Abstrak: Time series data of national area harvested of paddy has high volatility and non homogeneous variance. A time series data with non homogeneous variance at time is called time series data with conditional heteroscedasticity. Time series analysis methods that can be used to overcome heteroskedasticity are GARCH models. However, the data of area harvested of paddy contained in the possibility of asymmetric volatility. To overcome the influence of asymmetry, some asymmetry GARCH models can be used, such as: exponential asymmetric GARCH model (EGARCH), quadratic asymmetric GARCH model (QGARCH), T-GARCH model, and non-linear asymmetry GARCH model (NAGARCH). This study aims to model the national area harvested of paddy by incorporating elements of varians heterogeneity and the influence of asymmetry on its data using five types of symmetry, asymmetry, and non-linear GARCH models, and find the best models of those five types of GARCH models. Model for national area harvested of paddy are ARIMA(2,0,0)(1,1,0)12 ‒ GARCH(1,2) and ARIMA(2,0,0)(1,1,0)12 ‒ QGARCH(1,2). Based on the mean absolute percentage error (MAPE) value to twenty two periods ahead, ARIMA(2,0,0)(1,1,0)12 ‒ QGARCH(1,2) better than ARIMA(2,0,0)(1,1,0)12 ‒ GARCH(1,2) but MAPE values for both models is quite high because there is some predicted value deviates quite far from the actual value. However, the value of MAPE to twelve periods ahead is low, 16.88% for ARIMA(2,0,0)(1,1,0)12 ‒ QGARCH(1,2). Furthermore, based on the value of mean absolute deviation (MAD) and mean square error (MSE), ARIMA(2,0,0)(1,1,0)12 ‒ QGARCH(1,2) also seems to be the better model than ARIMA(2,0,0)(1,1,0)12 ‒ GARCH(1,2). Thus, it can be concluded that the quadratic GARCH model is a fit model of national area harvested of paddy with a fairly good prediction results.
Keyword: time series data, area harvested, GARCH, asymmetric