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 breaking news by Naïve Bayes classifier obtained an accuracy of 83% and a harmonic average of 90.713%.

Weaving industry is one of the creative industries based on local wisdom of Bali, need to be developed with the concept of modern entrepreneurship (orange economy). Regional economic development strategies need to take into account the dynamics of local community life or social capital in addition to the role of government and other physical capital, in order to improve the performance of weaving industry in Jembrana regency, Bali. Based on empirical theory and facts, this study aims to analyze how the direct and indirect influence of the role of government, social capital and business performance on and subjective wellbeing on the business actors of weaving industry in Jembrana, Bali. Through the modeling of the resulting structural equations is studied: (1) the direct influence of the government's role on business performance and subjective well-being; (2) the direct influence of social capital on business performance and subjective wellbeing; (3) the direct impact of business performance on subjective well-being; (4) the indirect and total influence of the government's role on subordinate welfare mediated by business performance; and (5) the indirect and total social capital influences on subjective well-being mediated by business performance. Based on surveys and structured interviews on 70 business actors of weaving industry in Jembrana District, Bali through data analysis techniques using SEM-PLS with the help of Smart PLS 3.0 software, in the business actors weaving industry found that: (1) directly the role of government have positive and significant (2) direct social capital has a positive and significant effect on business performance, but not significant to subjective wellbeing, (3) directly the business performance have a positive and significant effect on subjective wellbeing, (4) the role of the government indirectly has a positive but insignificant effect on subjective wellbeing, but through full mediation of business performance, the role of the government has a positive and significant effect on subjective wellbeing, and (5) social capital indirectly has a positive and significant effect on the subjective achievement, so totally through the full mediation of business performance, social capital has a positive and significant impact on subjective wellbeing although it directly does not have a significant effect.

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One of functions of multivariate analysis is to group data. Multivariate analysis often used in grouping  data are cluster analysis and biplot analysis. In this paper, a comparative analysis will be made between clusters analysis and biplot analysis  for grouping the data. Technique used in the cluster analysis is k-mean method  and biplot analysis used two-dimensional display.  The results ware that biplot analysis produces are better in grouping accuracy than clusters analysis. But in general, biplot analysis can not be said to be better than clusters analysis in grouping the data and vice versa.

The purpose of this research is to know the influence of the image of the destination towards internal motivation and external motivation, internal motivation and external motivation against travelers, the influence of taourist motivation towards the stratification of tourists holidaying in the village of Sanur. The population in this research is the elderly tourists on vacation to the village of Sanur with sample as much 100 sample and sample “technique used is Accidentalsampling techniques. This research method using Partial Least Square with 3 variables second order in the variable image of tourist destination, motivation, satification of tourists and 6 first orde variable the explain the motivation variable satisfaction of tourists and travelers.The results of the research show that the image of destinations likely to affect external motivation path coefficient with a value amounting to 0.939 on the tourist motivation more dominant external motivation that affect the value of the coefficient of 0.836. While the motivations of tourist proved to influence that satisfaction of travelers with coefficients of 0.402.