BibTex Citation Data :
@article{Medstat11720, author = {Dwi Ispriyanti and Abdul Hoyyi}, title = {ANALISIS KLASIFIKASI MASA STUDI MAHASISWA PRODI STATISTIKA UNDIP dengan METODE SUPPORT VECTOR MACHINE (SVM) dan ID3 (ITERATIVE DICHOTOMISER 3)}, journal = {MEDIA STATISTIKA}, volume = {9}, number = {1}, year = {2016}, keywords = {}, abstract = { Graduation is the final stage of learning process activities in college. Undergraduate study period in UNDIP’s academic regulations is scheduled in 8 semesters (4 years) or less and maximum of 14 semesters (7 years). Department of Statistics is one of six departments in the Faculty of Science and Mathematics UNDIP. Study period in this department can be influenced by many factors. Those factor are Grade Point Average (GPA) or IPK, gender, scholarship, parttime, organizations, and university entrance pathways. The aim of this paper is to determine the accuracy factors classification. We use SVM (Support Vector Machine method) and ID3 (Iterative Dichotomiser 3). The comparison of SVM and ID3 method, both for training and testing the data generate good accuracy, namely 90%. Especially ID3 training data gives better result than SVM. Keywords: SVM , ID3 }, issn = {2477-0647}, pages = {15--29} doi = {10.14710/medstat.9.1.15-29}, url = {https://ejournal.undip.ac.id/index.php/media_statistika/article/view/11720} }
Refworks Citation Data :
Graduation is the final stage of learning process activities in college. Undergraduate study period in UNDIP’s academic regulations is scheduled in 8 semesters (4 years) or less and maximum of 14 semesters (7 years). Department of Statistics is one of six departments in the Faculty of Science and Mathematics UNDIP. Study period in this department can be influenced by many factors. Those factor are Grade Point Average (GPA) or IPK, gender, scholarship, parttime, organizations, and university entrance pathways. The aim of this paper is to determine the accuracy factors classification. We use SVM (Support Vector Machine method) and ID3 (Iterative Dichotomiser 3). The comparison of SVM and ID3 method, both for training and testing the data generate good accuracy, namely 90%. Especially ID3 training data gives better result than SVM.
Keywords: SVM, ID3
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BREAST CANCER CLASSIFICATION USING SUPPORT VECTOR MACHINE (SVM) AND LIGHT GRADIENT BOOSTING MACHINE (LIGHTGBM) MODELS
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