No abstract

Use of alum as a coagulant in drinking water treatment process generates an alum sludge as a waste product. Since the amount of this sludge is huge, it is crucial for a water work management to properly handle and dispose of this sludge. Reuse of this alum sludge as a solid adsorbent is one of the proposed applications for this material but modification and characterization are needed to alter and identify its properties so that optimum benefits are obtained. This paper reports characterization of raw and thermally treated alum sludge. The raw alum sludge was collected from a local water treatment plant and heated at 300 °C and 800 °C for 7 hours using a furnace before characterization using scanning electron microscopy energy (SEM), thermogravimetric (TGA), X-ray diffraction (XRD) and Brunauer-Emmett-Teller (BET). The results showed that surface morphology, thermal properties, microstructure, surface area and porosity of the sludge were affected by heating temperature whereby increase the heating temperature resulted in improved thermal stability of the sludge. The results also revealed that both raw and thermally treated alum sludge were mesoporous materials and mainly compose of quartz and kaolinite. It can be said that the sludge could be a good candidate as low cost adsorbent.

ARCH and GARCH models are widely used in financial data to describe its volatility pattern. The models assume the positive and negative return residual gives the same or symmetric influence on its volatility. However, in reality, this assumption is frequently violated, which is called heteroscedasticity. Therefore, to deal with heteroscedasticity and asymmetric data, the asymmetric GARCH models, which are EGARCH and GJR-GARCH models are used. This research aims to compare the models between symmetric and asymmetric GARCH to make financial data modeling. It uses daily data on three foreign exchange rates for IDR including IDR/CNY, IDR/JPY, and IDR/USD. The data series to be used here are from January 4, 2016, to January 20, 2020. This research method is started by selecting the best mean model for each data. Based on the best mean model, then modeling the mean and variance function are simultaneously conducted using the GARCH model. To test whether there was an asymmetric effect on the data, a Lagrange multiplier test was applied on the residuals of the GARCH model. The results show that the asymmetric effect was found in the IDR/CNY and IDR/JPY exchange rates. To overcome this asymmetric effect, EGARCH and GJR-GARCH model were applied to the two exchange rates. Then the two models are compared to find out which volatility model is better. Using AIC and BIC we find EGARCH as the best model for IDR/CNY exchange rates daily return and GJR-GARCH as the best model for IDR/JPY exchange rates daily return.

The study aimed to compare a few robust approaches in linear regression in the presence of outlier and high leverage points. Ordinary least square (OLS) estimation of parameters is the most basic approach practiced widely in regression analysis. However, some fundamental assumptions must be fulfilled to provide good parameter estimates for the OLS estimation. The error term in the regression model must be identically and independently comes from a Normal distribution. The failure to fulfill the assumptions will result in a poor estimation of parameters. The violation of assumptions may occur due to the presence of unusual observations (which is known as outliers or high leverage points. Even in the case of only one single extreme value appearing in the set of data, the result of the OLS estimation will be affected. The parameter estimates may become bias and unreliable if the data contains outlier or high leverage point. In order to solve the consequences due to unusual observations, robust regression is suggested to help in reducing the effect of unusual observation to the result of estimation. There are four types of robust regression estimations practiced in this paper: M estimation, LTS estimation, S estimation, and MM estimation, respectively. Comparisons of the result among different types of robust estimator and the classical least square estimator have been carried out. M estimation works well when the data is only contaminated in response variable. But in the case of presence of high leverage point, M estimation cannot perform well.