ANALISIS EMOSI TWITTER MENGGUNAKAN METODE SELEKSI FITUR MUTUAL INFORMATION DAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)

LUTHFI, AHMAD MARZUQI YASYKUR and Samsuryadi, Samsuryadi and Kurniati, Rizki (2023) ANALISIS EMOSI TWITTER MENGGUNAKAN METODE SELEKSI FITUR MUTUAL INFORMATION DAN ALGORITMA SUPPORT VECTOR MACHINE (SVM). Undergraduate thesis, Sriwijaya University.

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Abstract

Most texts have a large number of features. However, the features contained in the text mostly have a low level of relevance and even caontain noise which can later reduce the accuracy of the results. Feature selection is used to reduce the dimensions of feature space by weighting all features then features with lower weights than treshold will be eliminated. It aims to improve the accuracy and efficiency of computational time in the text classification process. In this research, selection method Mutual Information were used in the text classification process in the form of Twitter emotion sentiment using the Support Vector Machine (SVM) algorithm. Then, a comparative analysis will be carried out on each classification model based on the evaluation results obtained. The results showed that the use of the feature selection method was able to increase accuracy and reduce computation time.The use of the Mutual Information feature selection method on the SVM algorithm with a linear kernel give the best performance with average of accuracy 0.71, precision 0.72, recall 0.71, f-measure 0.71 and computation time 1.17 seconds.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi Teks, Seleksi Fitur, Support Vector Machine (SVM)
Subjects:	B Philosophy. Psychology. Religion > BF Psychology > BF511-593 Affection. Feeling. Emotion Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Ahmad Marzuqi Yasykur Luthfi
Date Deposited:	27 Jul 2023 06:34
Last Modified:	27 Jul 2023 06:34
URI:	http://repository.unsri.ac.id/id/eprint/122462

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