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All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) KOVALEN: Jurnal Riset Kimia Jurnal Sains Matematika dan Statistika JMPM: Jurnal Matematika dan Pendidikan Matematika Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya Jambura Journal of Mathematics Amalee: Indonesian Journal of Community Research & Engagement Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Journal of Computer System and Informatics (JoSYC) Madaniya Phydagogic : Jurnal Fisika dan Pembelajarannya
Hikmah Hikmah
Universitas Sulawesi Barat
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Journal : Journal of Computer System and Informatics (JoSYC)

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Oversampling, Undersampling, Smote SVM dan Random Forest pada Klasifikasi Penerima Bidikmisi Sejawa Timur Tahun 2017 Laila Qadrini; Hikmah Hikmah; Megasari Megasari
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2154

Abstract

Bidikmisi is tuition assistance from the government for high school graduates (SMA) or equivalent who have good academic potential but have economic limitations. Different from scholarships that focus on providing awards or financial support to those who excel. The achievement requirements for Bidikmisi are aimed at ensuring that Bidikmisi recipients are selected from those who truly have the potential and willingness to complete higher education. Given that the recipients of this bidikmisi must really be the right person, in this study a classification of the recipients of the 2017 bidikmisi in East Java will be carried out, in this study there is data that is not balanced the "Accepted" class is more than the "Not accepted" class. If the data is not balanced, almost all classification algorithms will produce much higher accuracy for the majority class than for the minority class. Researchers will handle class imbalances. The resampling technique used in research related to the prediction of bidikmisi recipients includes resampling techniques, namely Oversampling, Undersampling and SMOTE using two classification methods, namely SVM and Random Forest. The Oversampling technique was chosen because it does not reduce the amount of data but adds to the dataset that is lacking in the minority class. The Oversampling algorithm used is Synthetic Minority Over-sampling Technique (SMOTE), this algorithm was chosen from several resampling algorithms because SMOTE produces good accuracy and is effective in dealing with unbalanced classes because it reduces overfitting.
Co-Authors Ahmad Ansar Andi Risfan Rizaldi Apriyanto Apriyanto Asriani Hasan Asrirawan Aulya Maghfirah Darma Ekawati Darmawati Darmawati Dewi Sartika Dzulkifli Elviani Tande Fardinah Fardinah Laila Qadrini Laila Qadrini Megasari Megasari Musafira Nilawati Nilawati Nizar Nursyafitri Amin Nurul Fadhilah Shinta Nuriah Supardi Supardi