Independent Campus Student Exchange Sentiment Analysis Using SVM

Putri Irhami; Eva Darnila; Fadlisyah Fadlisyah

doi:10.29103/jacka.v1i2.14902

Independent Campus Student Exchange Sentiment Analysis Using SVM

Putri Irhami, Eva Darnila, Fadlisyah Fadlisyah

Abstract

Support Vector Machine (SVM) is a machine learning method that is widely used for regression and classification problems, especially application review classification. Student exchange is one of the programs that universities must prepare. The student exchange program is intended to reduce the problem of disparities in educational facilities and infrastructure in Indonesia. The advantage of student exchange is that they can manage their time, have high awareness in communicating, are able to admit when they experience problems and need help, independent student exchange offers study options of up to 20 credits, both covering Higher Education Recipients courses and activities in the form of the Nusantara Module. Additionally, students are offered the option to register for a maximum of 6 credits of higher education online. The method used in this research is the SVM algorithm, the dataset used consists of 1000 comment reviews with a ratio of 70;30. This research was implemented in a web system using the Python programming language. Of the 300 test data implemented with 700 training data. The Support Vector Machine (SVM) algorithm in classifying review data obtained the highest accuracy in dividing training data & test data 70:30 at 85.00% then precision 28.33%, recall 33.33%

Keywords

Support Vector Machine; student exchange

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DOI: https://doi.org/10.29103/jacka.v1i2.14902