E jurnal Matematika

92-99

Authors: Juen Ling, I Putu Eka N. Kencana, Tjokorda Bagus Oka

Sentiment analysis is the computational study of opinions, sentiments, and emotions expressed in texts. The basic task of sentiment analysis is to classify the polarity of the existing texts in documents, sentences, or opinions. Polarity has meaning if there is text in the document, sentence, or the opinion has a positive or negative aspect. In this study, classification of the polarity in sentiment analysis using machine learning techniques, that is Naïve Bayes classifier. Criteria for text classification decisions, learned automatically from learning the data. The need for manual classification is still required because training the data derived from manually labeling, the label (feature) refers to the process of adding a description of each data according to its category. In the process of labeling, feature selection is used and performed by chi-square feature selection, to reduce the disturbance (noise) in the classification. The results showed that the frequency of occurrences of the expected features in the true category and in the false category have an important role in the chi-square feature selection. Then classification by Naïve Bayes classifier obtained an accuracy of 83% and a harmonic average of 90.713%. Keywords: chi square, classification, feature selection, machine learning technique, Naïve Bayes, sentiment analysis.

107-115

Authors: Ni Made Rara Keswari, I Wayan Sumarjaya, Ni Luh Putu Suciptawati

Poisson regression is a nonlinear regression that is often used to model count response variable and categorical, interval, or count regressor. This regression assumes equidispersion, i.e., the variance equals the mean. However, in practice, this assumption is often violated. One of this violation is overdispersion in which the variance is greater than the mean. There are several methods to overcome overdispersion. Two of these methods are negative binomial regression and generalized Poisson regression. In this research, binomial negative regression and generalized Poisson regression statistically equally good in handling overdispersion. Keyword: Poisson Regression, Overdispersion, Negative Binomial Regression, Generalized Poisson Regression.

123-129

Authors: I Putu Oka Paramartha, Komang Dharmawan, Desak Putu Eka Nilakusmawati

The aim to determine of the simulation results and to calculate the stock price of Asian Option with Normal Inverse Gaussian (NIG) method and Monte Carlo method using MATLAB program. Results of both models are compared and selected a fair price. Besides to determine simulation accuracy of the stock price, speed of program execution MATLAB is calculated for both models for time efficiency. The first part, set variabels used to calculate the trajectory of stock prices at time t to simulate the stock price at the time. The second part, simulate the stock price with NIG model. The third part, simulate the stock price with Monte Carlo model. After simulating the stock price, calculated the value of the pay-off of the Asian Option, and then estimate the price of Asian Option by averaging the entire value of pay-off from each iteration. The last part, compare result of both models. The results of this research is price of Asian Option calculated using Monte Carlo simulation and NIG. The rates were calculated using the NIG produce a fair price, because of the pricing contract NIG using four parameters α, β, δ, and μ, while Monte Carlo is using only two parameters σ and μ. For execution time of the program, the Monte Carlo model is better in all iterations. Keywords: Option, Asian Option, Normal Inverse Gaussian, NIG, Monte Carlo.