Optimasi Model Semi-Supervised Learning dengan SVM dan Naïve Bayes

Muhammad Siddik Hasibuan; Yusuf Ramadhan Nasution

doi:10.30998/string.v9i2.24556

Optimasi Model Semi-Supervised Learning dengan SVM dan Naïve Bayes

Muhammad Siddik Hasibuan^(1*), Yusuf Ramadhan Nasution⁽²⁾

(1) Universitas Islam Negeri Sumatera Utara
(2) Universitas Islam Negeri Sumatera Utara
(*) Corresponding Author

Abstract

Determining labels in text mining is the most important thing, apart from using lexicon based, you can also use human understanding to interpret sentences into positive, negative and neutral categories. Sentiment analysis is to measure an opinion taken from tweets to be able to analyze the text. Researchers carry out a learning process and data testing. The focus of this research is to be able to determine the polarization of text into positive, negative and neutral forms using a semi-supervised machine learning model. In the previous data training process, labeling was carried out using human understanding to obtain positive, negative and neutral labels. Next, in the testing process, the data is not labeled, the role of the learning machine is so that the test data gets a label. The semi-supervised learning (SSL) technique is used to label unlabeled data with the algorithm used to process the training data using SVM and NB. The statistical evaluation used is cross validation and to measure the level of accuracy of the two algorithms using a confusion matrix. SVM received a high accuracy score in this study compared to NB, SVM got a high accuracy score in this study compared to NB, SVM got 88.97% accuracy and NB 83.02%

Keywords

semi-supervised learning; SVM; NB

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DOI: http://dx.doi.org/10.30998/string.v9i2.24556