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

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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.

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Inventory Code Barcode Call Number Location Status
2307002983T123398T1233982023Central Library (Referens)Available
Detail Information
Series Title
-
Call Number
T1233982023
Publisher
Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xix, 124 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
005.107
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Prodi Teknik Informatika
Identifikasi Sistem
Specific Detail Info
-
Statement of Responsibility
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No other version available

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