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: 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