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: Debrominasi Difenil Eter Terpolibrominasi serta Isolasi dan Elusidasi Theonellapeptolide Id dari Spons Laut Theonella swinhoei.
Abstrak: Pencarian senyawa aktif dapat difokuskan pada eksplorasi reaksi kimia dan isolasi dari sumber hayati laut untuk bahan obat baru. Penelitian ini bertujuan memodifikasi senyawa mayor 2,3,4,5-tetrabromo-6-(3,5-dibromo-2-hidroksifenoksi)fenol 1 melalui reaksi debrominasi, serta mengisolasi dan mengelusidasi struktur molekul aktif spons laut Theonella swinhoei asal Sulawesi Selatan. Berdasarkan elusidasi resonansi magnetik inti (NMR) proton (1H), produk reaksi debrominasi didapatkan sebagai senyawa 4 dan senyawa campuran 5−6 (nisbah 1:1) dengan rendemen masing-masing 1.95%. Lapisan etil asetat (EtOAc) T. swinhoei (5.8924 g) memiliki sitotoksik ampuh terhadap sel kanker MCF-7. Fraksionasi lapisan EtOAc menghasilkan 16 fraksi A−P. Rekristalisasi fraksi G menghasilkan senyawa 7 (36.9 mg) yang dielusidasi sebagai theonellapeptolide Id (C70H125O16N13) berdasarkan data gabungan spektroskopi massa resolusi tinggi dan NMR multidimensi. Berdasarkan pustaka, senyawa 7 dianggap sebagai senyawa sitotoksik yang baik terhadap berbagai sel kanker.
Keyword: elusidasi struktur, reaksi debrominasi, Theonella swinhoei, theonellapeptolide Id