ANALISIS SENTIMEN MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER DENGAN MODEL DOKUMEN BERNOULLI DAN SUPPORT VECTOR MACHINE; (SENTIMENT ANALYSIS USING NAÏVE BAYES CLASSIFIER WITH BERNOULLI DOCUMENT MODEL AND SUPPORT VECTOR MACHINE)

Gumilang Ramadhani, Tiara, Rosadi, Dedi

2015 | Skripsi | FMIPA UGM

The rise of information system base on website has given a lot of simplicity to get the information in a large number and for free. An information with textual document is usually given as an unstructured form. To dealing these unstructured document form, text mining is being required. One of analysis in text mining which is often used is sentimen classification or text classification. From the result of the classification about one topic we can specify the classes for the opinions by seeing the proportion on each class from the entire documents provided. Two methods that will be discussed are naïve bayes classifier and support vector machine to determine a binary classes for each document. Naïve bayes classifier is a classification method with Bayesian rule by using the prior probabilities and conditional probabilities in each class from data training. The probabilities is being used to determine the maximum a posteriori each classes for the data testing and specify the document classes. The accuracy for the classified data is being calculated by using support vector machine method with the Kernel function and data proportion determined from the data training. By comparing these two methods, the best accurate rate for the sentimen classification is by using naïve bayes classifier (NBC) which is reached a percentage 62,6295%. By classifying 5.412 data training and 2701 data testing using naïve bayes classifier, the result shows from the entire documents the proportion for the positive class is 44,46501% and for the negative class is 55,53499%.

Kata Kunci : sentimen classification; opinion; text mining; Bayesian rule; Naïve bayes classifier; support vector machine