algoritma visual basic 6 sistem tabungan

Before consumers decide to buy cosmetics, consumers should know in detail the products to be purchased, this can be learned from the testimonials or the results of reviews from consumers who have bought and used the previous product. Reading a review before buying a product is very important to know the advantages and disadvantages of the products we will use, besides reading a cosmetic review can find out the quality of the cosmetic brand is feasible or not be used. Sentiment analysis is an area of approach that solves problems by using reviews from various relevant scientific perspectives. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc.

News popularity is the maximum growth of attention given for particular news article.

Algoritma visual basic 6 sistem tabungan