@thesis{thesis,
		author={MUHAMAD  SATRIA ADHI},
		title ={OPTIMASI NILAI AKURASI PADA ANALISIS SENTIMEN MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER DAN SEMANTIC EXPANSION},
		year={2019},
		url={https://repository.ittelkom-pwt.ac.id/5677/},
		abstract={ABSTRACT Sentiment analysis using the Naïve Bayes Classifier method produces low accuracy values. the low value of accuracy is caused by the Preprocessing process which is not optimal in processing text. To overcome these problems, optimization of the Word Normalization, Negation, Stemming, and addition of the Semantic Expansion method is carried out. Improvements to the process and the addition of the Semantic Expansion Method are used to increase the accuracy of the Sentiment Analysis process. Based on the results of the sentiment analysis test using the Naïve Bayes Classifier method, proving that the addition of the Semantic Expansion method in the text classification process can improve accuracy. It is known from the results of testing carried out by researchers getting an accuracy value of 72%. While the accuracy value obtained without using the Semantic Expansion method is 70%. An increase in accuracy value of 2% indicates that programs created by researchers can classify data sets better when using Semantic Expansion processes. Keywords: naïve bayes classifier, negation, semantic expansion, sentiment analysis, stemming.}
		}