Klasifikasi Prestasi Akademik Mahasiswa Berdasar Hasil Tes Potensi Akademik Menggunakan Support Vector Machine

Penulis

  • Agustinus Handaya Ajitama Universitas Sanata Dharma
  • Paulina H. Prima Rosa Universitas Sanata Dharma

Kata Kunci:

Data Mining, Classification, Support Vector Machine, Academic Potential Test

Abstrak

This paper describes research that aims to classify student academic achievement based on the results of the Academic Potential Test (APT), using data containing APT scores and Grade Point Average (GPA) in semester 1 to semester 4. To get the best accuracy, some experiments were carried out by varying the fold, kernel, and multiclass. The folds used in the experiment are 5, 7, and 9 folds. The kernel used is a linear kernel, Gaussian Radial Basic Function (RBF), and polynomial. Meanwhile, the multiclass used in this experiment is one against one and one against all. The highest accuracy of 80% was obtained in the GPA classification in semester 4 using multiclass one against one and one against all, RBF kernel, and 9-fold cross validation

Referensi

Farida, Intan Nur & Ratih Kumalasari N., (2017), Penggunaan Algoritma Naïve Bayes Untuk Mengevaluasi Prestasi Akademik Mahasiswa Universitas Nusantara PGRI Kediri. Kediri: Universitas Nusantara PGRI Kediri, Jurnal Sains dan Informatika Volume 3, Nomor 2, November 2017, e-ISSN: 2598-5841.

Han, J., Kamber, M. & Pei, J., (2012), Data Mining: Concepts and Techniques. Waltham: Morgan Kaufmann Publishers.

Hasibuan, Chainur A. et.al., (2017), Klasifikasi Diagnosa Penyakit Demam Berdarah Dengue (DBD) Menggunakan Support Vector Machine (SVM) Berbasis GUI Matlab. Jurnal Gaussian, Volume 6, Nomor 2, Tahun 2017, Halaman 171-180, ISSN: 2339-2541.

Kurniawan, Vincentius B., (2019), Prediksi Prestasi Akademik Mahasiswa Berdasarkan Hasil Tes Potensi Akademik dengan Algoritma K-Nearest Neighbor, Skripsi. Yogyakarta: Universitas Sanata Dharma

Kurniawaty, D. et.al., (2018), Klasifikasi Gangguan Jiwa Skizofrenia Menggunakan Algoritme Support Vector Machine (SVM). Universitas Brawijaya, Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol.2, No.5, Mei 2018, halaman 1866-1873, e-ISSN: 2548- 964X.

Ocataviani, Pusphita A. et.al., (2014), Penerapan Metode Klasifikasi Support Vector Machine (SVM) Pada Data Akreditasi Sekolah Dasar (SD) Di Kabupaten Magelang. Jurnal Gaussian, Volume 3, Nomor 4, Tahun 2014, Halaman 811-820, ISSN: 2339-2541.FLEXChip Signal Processor (MC68175/D), Motorola, 1996

Suyanto, (2019), Data Mining untuk Klasifikasi dan Klasterisasi Data. Bandung: penerbit INFORMATIKA.

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Diterbitkan

2022-01-22

Cara Mengutip

Ajitama, A. H. ., & Rosa, P. H. P. . (2022). Klasifikasi Prestasi Akademik Mahasiswa Berdasar Hasil Tes Potensi Akademik Menggunakan Support Vector Machine . Prosiding, 4, 319–323. Diambil dari https://ejournal.upbatam.ac.id/index.php/prosiding/article/view/5347

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