The comments contained on e-commerce users generally contain opinions about positive or negative experiences at several online shops. Sentences that can be written indirectly both a little or a lot, will affect other potential customers. So as a result of these comments cause a product sold at an online store has a rating of two things namely "recommended" or "non-recommended". However, detection of positive and negative opinions manually will require more time because of the large amount of data. For this reason opinion mining using technology in data mining can be used to automate positive and negative detection of comments. However, one of the main problems in opinion mining is limited data but has a large number of attributes. In this study, we propose the application of Pearson correlation (PC) based feature selection for opinion mining optimization. The results of the experiment show that the application of PC increases the performance of opinion mining systems in 3 types of classification, namely Logistic Regression, Naïve Bayes and Support Vector Machine, resulting in more optimal accuracy, namely 98.80%, 87.87% and 98.12%.
The era of disruption has caused many changes in people's lives, both in behavior and in system settings, where it is impossible to return to the previous era. Efforts are needed to face this era with revitalization, one of which is the urgency of early childhood education so that it remains based on Islamic values with technological developments that accompany the growth of the next generation of the Indonesian nation. The application of Islamic values from an early age is very necessary because it is the initial foundation for the generation of a religious nation by adapting itself according to the times that cannot be separated from technology. By realizing the goal that people are able to adapt to existing conditions, of course it is not easy because there are pros and cons that can be seen from social media which discusses several issues regarding the influence of Islamic values that are taught from an early age to the presence of technology. In this study using opinion mining found on social media to determine the positive or negative conditions that occur so that it is known how big the relationship between variables is. The discussion of data on this problem uses four machine learning algorithms including: decision tree, naïve Bayes, k-nn and svm. The results of data processing show that the decision tree algorithm gets the highest accuracy value of 78.20% among the other algorithms. The results of the study are used to determine strategies for improving human resources or the nation's next generation, starting with early improvements.
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