Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307002765	T122462	T1224622023	Central Library (REFERENS)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1224622023

Publisher

Indralaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xv, 119 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

003.5 07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Teori komunikasi dan kontrol
Jurusan Teknik Informatika

Specific Detail Info

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Statement of Responsibility

ANUG

Other version/related

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