Open AccessClassification of Thesis Topics Based on Informatics Science Using SVM
Author(s) -
Eka Mala Sari Rochman,
Ika Oktavia Suzanti,
Imamah Imamah,
Muhammad Ali Syakur,
Devie Rosa Anamisa,
Ach. Khozaimi,
Aeri Rachmad
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1125/1/012033
Subject(s) - support vector machine , hyperplane , process (computing) , computer science , informatics , margin (machine learning) , subject (documents) , artificial intelligence , machine learning , variable (mathematics) , test (biology) , data mining , information retrieval , data science , mathematics , engineering , world wide web , electrical engineering , mathematical analysis , paleontology , geometry , biology , operating system
Thesis topic is an inseparable part in the world of tertiary education. Determining the thesis topic becomes a problem for students. The determination of the thesis topic leads to the trend of the topic in the development of computer science. The determination of the topic of thesis for students often ignores their ability to process. Ideally in determining the topic of the thesis, the record of student grades can be an important variable in deciding topics for students, where the student’s grade record is contained in the transcript. Therefore, this study uses the Support Vector Machine (SVM) method in recommending thesis topics by classifying selected subject groups that have been taken by students. The Support Vector Machine method is a classification method of supervision because it requires testing data and training data as a training process at the time of prediction. Support Vector Machine provides an optimal model, which provides a solution with a maximum margin to determine the distance of data to the hyperplane. The test results show an accuracy of 80%.