Klasifikasi Mahasiswa HER Berbasis Algoritma SVM dan Decision Tree

Penulis

  • Jajang Jaya Purnama Universitas Bina Sarana Informatika
  • Hendri Mahmud Nawawi Universitas Bina Sarana Informatika
  • Susy Rosyida STMIK Nusa Mandiri
  • Ridwansyah Ridwansyah STMIK Nusa Mandiri
  • Risnandar Risnandar 5Pusat Penelitian Informatika-LIPI

DOI:

https://doi.org/10.25126/jtiik.0813080

Abstrak

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.

 

Abstract


Students in each tertiary institution are required to obtain knowledge and skills that meet the requirements with academic achievement. The results of student learning are obtained from the theory and practice exams, each student is required to complete grades according to the minimum graduation criteria of each teaching lecturer, if below the minimum limit then students take remedial. Remedial is one way to complete the minimum passing criteria. Students who take remedial every semester almost reach a relatively high number of the total number of students. To reduce the number of students who take remedial, a method that can reduce this is needed, with the Support Vector Machine (SVM) and Decision Tree (DT) methods. SVM and DT are one of the supervised learning classification methods. Therefore, in this study using SVM and DT. SVM can eliminate barriers to data, predict, classify with small sampling and can improve accuracy and reduce errors. Data classification of students who do remedial/improvements by improving the kernel model for visualization including bars, histograms, and distributions as well as the Decision Tree has its own advantages. From the results of this study it has been obtained that the accuracy and precision of DT models is greater than that of SVM, but for recall DT is smaller than SVM.


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Diterbitkan

02-12-2020

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Ilmu Komputer

Cara Mengutip

Klasifikasi Mahasiswa HER Berbasis Algoritma SVM dan Decision Tree. (2020). Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(6), 1253-1260. https://doi.org/10.25126/jtiik.0813080