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

Judul: Time Series Data Analysis with Heterogen and Asymmetry Error Variance : Jakarta Composite Index Study for 1999-2008.
Abstrak: Time series data at financial area has value experiencing fluctuate from time to time. This fluctuation results its conditional variance is becoming not constant. So modelling with model ARIMA cannot be applied by assumption of homogeneity of variance is not fufilled. One of way of overcome it is with simultaneously modeling mean function and variance function. The model is recognized as Generalized Autoregressive Conditional Heteroscedasticity (GARCH). Some data financial shows existence of negative relationship between value changes return wit h movement of its volatility. Existence of impairment return has influence larger ones to movement of its volatility. Exponential-GARCH (EGARCH) model can overcome the asymmetric problem with modeling conditional variance as function of log-linear. So conditional variance value predicted negativity will never. Jakarta Composite Index (JCI or IHSG) be one of indicators applied by government in taking policy in the field of chartered investment counsel. Besides government assumes the importance of stock market alternatively defrayal besides banking. A real big fluctuation happened in stock exchange, because every transaction is noted with small time scale so that value change happened so quickly. At this case assumption of homogeneity of variance is not fufilled. At stock exchange also shows existence of asymmetric influence. Hence applied model EGARCH in modeling daily value of JCI (IHSG). Chosen EGARCH model is MA(1)-EGARCH(1,1). EGARCH Model very good in modeling daily value of JCI (IHSG), but have not yet enough good to forecasting value JCI (IHSG ) which will come. Besides forecast to daily value of JCI (IHSG), modelling of variance function also yields forecasting to its conditional variance. Forecast of conditional variance hardly good for asset holder in seeing behavior of movement of JCI (IHSG ) and calculate level of risk holds a leg asset in the future.
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Judul: Sintesis dan uji aktivitas katalitik Fe3O4 dari campuran limbah wire plating sludge dan FeSO4 pada efisiensi pembakaran batu bara
Abstrak: Combustion catalyst is an additive added to the fuel to increase the heat energy released. Fe3O4 was the combustion catalyst used in this study, synthesized from PT Sumiden Cibinong wire plating sludge waste by coprecipitation between FeCl3 from wire plating sludge and FeSO4. Synthetic Fe3O4 was characterized by using Faurier transform infrared spectrophotometer, X-ray diffractometer, and atomic absorption spectrophotometer. The Fe2O3, Fe2O3+Fe3O4, and Fe3O4 catalysts were dispersed separately with oil, surfactant, and water. The catalyst mixed into coal having 4596 cal/g calorific value, with overall concentration of catalyst mixture in the coal was 0.01%(w/w). The combustion activity resulted by differential thermal analyzer showed an increase of combustion fficiency of 91.0% with Fe2O3 catalist, whereas addition of Fe3O4 catalyst decreased the efficiency of 55.6%.
Keyword: catalyst, coal, Fe3O4, wire plating sludge.