KLASIFIKASI MAHASISWA HER BERBASIS ALGORTIMA SVM DAN DECISION TREE

KLASIFIKASI MAHASISWA HER BERBASIS ALGORTIMA SVM DAN DECISION TREE

• By Purnama, Jajang Jaya; Nawawi, Hendri Mahmud; Rosyida, Susy; Ridwansyah, Ridwansyah; Risnandar, Risnandar;
• Jurnal Nasional Terakreditasi
• 483
• 1253-1260

Mahasiswa di setiap perguruan tinggi dituntut untuk memperoleh pengetahuan dan keterampilan yang memenuhi syarat dengan prestasi akademik. Hasil dari pembelajaran mahasiswa didapat dari ujian teori dan praktek, setiap mahasiswa wajib menuntaskan nilai sesuai kriteria kelulusan minimum dari masing-masing dosen pengajar, jika dibawah batas minimum maka mahasiswa mengikuti her. Her adalah salah satu cara untuk menuntaskan kriteria kelulusan minimum. Mahasiswa yang mengikuti her setiap semesternya hampir mencapai angka yang relatif tinggi dari jumlah seluruh mahasiswa. Untuk mengurangi jumlah mahasiswa yang mengikuti her maka dibutuhkan sebuah metode yang dapat mengurangi hal tersebut, dengan metode Support Vector Machine (SVM) dan Decision Tree (DT). SVM dan DT adalah salah satu metode klasifikasi supervised learning. Oleh karena itu, dalam penelitian ini menggunakan SVM dan DT. SVM dapat menghilangkan hambatan pada data, memprediksi, mengklasifikasikan dengan sampling kecil dan dapat meningkatkan akurasi dan mengurangi kesalahan. Klasifikasi data siswa yang melakukan her/peningkatan dengan mengimprovisasi model kernel untuk visualisasi termasuk bar, histogram, dan sebaran begitu juga Decision Tree mempunyai kelebihan tersendiri. Dari hasil penelitian ini telah didapatkan akruasi dan presisi model DT lebih besar dibandingkan dengan SVM, akan tetapi untuk recall DT lebih kecil dibandingkan SVM

• Katakunci Data Mahasiswa, Her, SVM, Decision Tree
• URL https://jtiik.ub.ac.id/index.php/jtiik/article/view/3080
• Penerbit Semnas Inovasi dan Tren 2018 Kampus BSI Kaliabang, Bekasi Jawa Barat

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