PERBANDINGAN KERNEL LINEAR, RADIAL BASIS FUNCTION, DAN POLINOMIAL PADA ALGORITMA SUPPORT VECTOR MACHINE DALAM ANALISIS SENTIMEN TERHADAP ULASAN APLIKASI SHOPEE

CHAIRUNNISA, NADIA and Utami, Alvi Syahrini and Satria, Hadipurnawan (2023) PERBANDINGAN KERNEL LINEAR, RADIAL BASIS FUNCTION, DAN POLINOMIAL PADA ALGORITMA SUPPORT VECTOR MACHINE DALAM ANALISIS SENTIMEN TERHADAP ULASAN APLIKASI SHOPEE. Undergraduate thesis, Sriwijaya University.

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Abstract

Sentiment analysis can help detect sentiment from a review or assessment of a topic, product, service, and so on. These reviews can be classified into reviews with positive or negative sentiments. Support Vector Machine (SVM) method is a method that can be used in the classification process in sentiment analysis systems. However, often there is data that is not separated Linearly so that the kernel function is needed in the classification process. In this research, the kernel functions to be used are Linear, RBF, and Polynomial kernels with each parameter to be determined by the hyperparameter tuning method using GridSearchCV. Then a comparative analysis will be carried out on each model based on the 3 kernel functions to get the best kernel function. The results showed that the RBF kernel with parameter values C = 10 and ɣ = 0.001 give the best performance with the same accuracy, precision, recall, and f1-score values of 0.87.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, Support Vector Machine, Kernel Linear, Kernel RBF, Kernel Polinomial
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ1-1570 Mechanical engineering and machinery
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Nadia Chairunnisa
Date Deposited:	28 Jul 2023 07:41
Last Modified:	28 Jul 2023 07:41
URI:	http://repository.unsri.ac.id/id/eprint/123398

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