This study aims to determine differences in oleoresin production and the type and content of turpentine P. oocarpa and P. merkusii. The first stage of the research activity was to collect oleoresin obtained from 15 plants of each type of pine aged 14 years which were determined randomly. Determination of the type and content of phytochemicals, especially turpentine using Gas Chromatography-Mass Spectrometry by injecting gas-phase chemical isolates. The oleoresin tapping data were analyzed by paired t-test with a test level of 5% to determine the difference in the amount of oleoresin. The results showed that there was no difference in production capacity (p<0.05) between the two stands with an average oleoresin production per tree of 0.0127±0.002 g.d-1 (P. oocarpa) and 0.0183±0.003 g.d-1 (P. merkusii). The most important species in P. oocarpa consisted of: α-pinene (5.2%), ß-pinene (5.8%), and delta 3 carena (13.8%); while in P. merkusii are α-pinene (8.2%), ß-pinene (11.5%), limonene (5.2%), α-terpinolene (32.7%), benzenemethanol (4.3%), and trans-pinocarveol (3.5%). All turpentine compounds produced by the two types of pine can be used for various pharmaceutical, cosmetic, and pesticide industries.
The purpose of this study was to analyze the effect of e-lifestyle and e-hedonism on the intensity to buy of the millennial generation. This type of research is a survey research. This research was conducted in Malang, East Java, with 150 respondents. The data analysis tool used in this research is SEM (Structural Equation Modeling), with Smart PLS as software. The results show that e-lifestyle has an effect on e-hedonism, e-lifestyle has no effect on intense to buy, e-hedonism has an effect on intense to buy, e-life style has an indirect effect on intense to buy through e-hedonism.
Quantile regression is an extension of the median regression that analyzes various quantile values. This method is used to predict the relationship between the response variable (Y) and the predictor variable (X) on the conditional quantile function. Quantile regression can be used to detect extreme conditions, either extreme dry (quantile 5) or extreme wet (quantile 95). Extreme precipitation often occurs in Indonesian territory because the area is surrounded by oceans. High frequency of extreme precipitation may trigger disasters, one of which is flooding. In 2017, there were floods in Sidoarjo area with a loss of up to 2 billion. In an effort to anticipate the adverse effects of extreme precipitation, forecast information abaut extreme precipitation is needed, one of which is using quantile regression. The objectives of this research was to determine the best quantile regression model for predict extreme precitipation. The data of this study were secondary data obtained from BMKG with a data length of 30 years. Modeling of extreme precipitation in Sidoarjo area involved variables of humidity, temperature and air pressure for model 1. Whereas model 2 involved 3 variables in model 1 plus the month and month square variables. The addition of the month variable and the month quadratic variable in model 2 was based on the precipitation data plot which formed a quadratic trend. Based on the Pseudo R2 value, it can be concluded that the best model to predict extreme precipitation in Sidoarjo is Model 2.
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