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: Modeling Arabica Coffee Commodity Price Using Asymmetric ARIMA-GARCH Model Approach
Abstrak: Komoditi pertanian pada saat ini merupakan aset yang menarik untuk dijadikan underlying asset dari suatu produk derivatif. Salah satu komoditi pertanian yang sedang populer saat ini adalah komoditi kopi arabika. Komoditi ini memiliki volatilitas yang tinggi. Karena tingginya volatilitas, penelitian ini bertujuan memodelkan harga komoditi kopi untuk meminimalisir kerugian atau memaksimumkan keuntungan. Volatilitas yang tinggi membuat pemodelan dengan Autoregressive Integrated Moving Average (ARIMA) saja tidak cukup. Model Generalized Autoregressive Conditional Heteroscedasticity (GARCH) bisa menyelesaikan permasalahan heteroskedastisitas namun masih memiliki kelemahan dalam memahami fenomena good news dan bad news. Oleh karena itu model GARCH asimetris dibutuhkan. Model GARCH asimetris yang digunakan dalam penelitian ini adalah model Exponential GARCH (EGARCH). Model yang diperoleh dalam penelitian ini adalah ARIMA(0,1,0)-EGARCH(2,3) dengan MAPE sebesar 2.54%., Agricultural commodities are currently attractive assets to be used as the underlying asset of a derivative product. One of the agricultural commodities that are currently popular is the arabica coffee commodity. This commodity has high volatility. Due to the high volatility, this study aims to model the price of the coffee commodity to minimize losses or maximize profits. High volatility makes modeling with the Autoregressive Integrated Moving Average (ARIMA) is not enough. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model can solve heteroscedasticity problems but still has weaknesses in understanding the phenomena of good and bad news. Therefore an asymmetric GARCH model is required. The asymmetric GARCH model used in this study is the Exponential GARCH model. The model obtained in this study is ARIMA(0,1,0)-EGARCH(2,3) with a MAPE of 2.54%.
Keyword: ARIMA, EGARCH, modeling

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.