Share price as one kind of financial data is the time series data that indicates the level of fluctuations and heterogeneous variances called heteroscedasticity. The method that can be used to overcome the effect of autoregressive conditional heteroscedasticity effect is the generalised form of ARCH (GARCH) model. This study aims to design the best model that can estimate the parameters, predict share price based on the best model and show its volatility. In addition, this paper discusses the prediction-based investment decision model. The findings indicate that the best model corresponding to the data is AR(4)-GARCH(1,1). The model is implemented to forecast the stock prices of Indika Energy Tbk, Indonesia, for 40 days and significantly presented good findings with an error percentage below the mean absolute.

The main enemy in rice production is the attack of stinky bugs, brown planthoppers, grasshoppers, ladybugs, aphids, and others. This attack inhibits the growth of rice plants, thereby reducing production or even thwarting the harvest. Chemical pesticide application can reduce pests and diseases. However, the long-term use of chemical pesticides can disrupt the ecosystem. This study aims to study the application of plant-based pesticides to the presence of pests and predatory insects for rice plants. The research was begun with the preparation of citronella-based pesticide from citronella extract obtained by mixing citronella with water at a weight ratio of 2:1. The extract was mixed with water at a ratio of 1 liter for 50 ml of citronella extract. The application of the prepared pesticide was carried out by spraying 21-DAP (day after planting) rice plants at two plots sizing 400 m2 each. The types and numbers of pests and predatory insects were observed before every pesticide application. Spraying was repeated weekly for the following 4 weeks. Results showed a decrease of insects in experimental plots A and B after the application of pesticides. After the fourth application, only one type of insect (green grasshopper) was found in plot A, and no insect was found in plot B. However, four types of insects were found in the control plot. The application of citronella-based pesticides is also related to the decrease of predatory insects’ population.

Future natural gas (FNG) price is a collected data over the years and is a volatile movement in the market. In other words, FNG price is categorised as a time series data with volatility in both variance and mean, as well as most likely in some cases having heteroscedasticity problem. To come up with an estimated prediction model, some analysis tools, such as autoregressive integrated moving average (ARIMA) and generalised autoregressive conditional heteroscedasticity (GARCH), are introduced to find the best-fitted model having the smallest error value with high significance of probability value. This study aims to examine the best-fitted model that allows us to forecast FNG prices more accurately in the near future. There are 2842 observed data of daily FNG prices from 2009 to 2019 as the input of study objects. The finding suggests that the first measurement model of ARIMA (1,1,1) does not fit the model as having a non-significant probability value. Thus, it is required to check its heteroscedasticity by conducting an ARCH effect test. It is concluded that a data set has an effect of ARCH, so AR (p)-GARCH (p,q) model is then tested. AR (1)-GARCH (1,1) model is believed to be a best-fitted model having a significant P < 0.0001 with significantly small mean squared error and root mean squared error values, indicating that it has a very accurate prediction model. The forecasting model is to adjust the offered recommendation of policy for the government regarding the issue of high volatility of daily FNG prices in the future. We then offer a best-suited policy for some certain governments like Indonesia to give subsidy for targeted users in order to keep increasing their use of FNG that will expectedly affect their marketable product innovation and expansion, so economic growth in Indonesia is maintained.

This study aims at determining the effect of the torrefaction process on the fuel quality of biomass pellets made from oil palm empty fruit bunches (EFB). The torrefaction process was carried out using a rotary reactor, which has a cylinder with a diameter of 15 cm and a length of 15 cm made from an iron plate. The cylinder was heated externally using a horizontal heater fueled with LPG. The reactor cylinder was filled with 1.5 kg of clean sand to homogenize the heat transfer and prevent pellets from colliding during the process. The torrefaction process was conducted with a load of 300 grams of EFB pellets at temperatures around 240-310 °C at variations of reaction time (20, 30, and 45 min.) and the reactor cylinder rotation speed (16, 31, and 37 RPM). The results showed that the torrefaction process improved the quality of the EFB pellet fuel. This was reflected from the very low moisture content (0.32-0.52 %) of torrefied pellets and its calorific value, which increased from 15.82 MJ/kg (without torrefaction) to 17.59 MJ/kg (with torrefaction for 45 minutes). Torrefied pellets showed good hydrophobicity where the pellet was not broken when immersed in water for 24 hours. Pellet without torrefaction was destroyed in water just in one minute.

Ethanol is commonly used as a solvent in extracting glucomannan from Porang. However, the extraction process often leaves ethanol. The remaining ethanol can be re-distilled to save the use of it. The remaining ethanol is used in the re-distillation process with input volumes of 50L and 100L with variations in heating temperatures of 80°C, 85°C, and 90°C. This study aimed to analyze the effect of the ethanol input volume and temperature on the output volume of re-distilled ethanol and determine the constant change in volume of re-distilled ethanol using kinetics and Arrhenius equations. The results showed that the input volume and heating temperature variation differed significantly from the ethanol output volume. The k value changes in the ethanol output volume from re-distillation with an input volume of 50L and a temperature variation of 80°C, 85°C, and 90°C respectively were 0.0016, 0.0023, and 0.0027 L/min, while the input volume of 100L was 0.0009, 0.001, and 0.0014 L/min. The results of the k value as a function of temperature using the Arrhenius equation showed that the re-distillation process with an input volume of 50L and 100L produces activation energy (Ea) of 55.83 kJ/mol and 46.94 kJ/mol, while the collision frequency value (A) of 3.03x105/min and 7.7x103/min.Keywords: Distillation, ethanol, glucomannan, arrhenius model, re-distillation