Prediksi Tingkat Indeks Prestasi Kumulatif Akademik Mahasiswa dengan Menggunakan Teknik Data Mining

Penulis

DOI:

https://doi.org/10.25126/jtiik.2020722493

Abstrak

Educational data mining (EDM) adalah suatu bidang aplikasi antara pendidikan dan komputer. Salah satu yang dapat dilakukan pada EDM adalah memprediksi tingkat prestasi mahasiswa. Tingkat indeks prestasi kumulatif (IPK) akademik mahasiswa sangat penting karena menentukan tingkat kelulusan dan kualiatas institusi pendidikan. Penelitian ini bertujuan untuk menganalisa atribut-atribut yang mempengaruhi tingkat indeks prestasi kumulatif (IPK) mahasiswa yang berasal dari faktor eksternal pada mahasiswa. Adapun atribut yang digunakan adalah 10 variabel atribut yaitu nilai TOEFL, pendidikan ayah, pendidikan ibu, pekerjaan ayah, pekerjaan ibu, asal daerah, tempat tinggal selama kuliah dan tingkat prestasi akademik yang dicapai. Hasil akurasi pengolahan dengan menggunakan Algoritma C4.5 adalah 75,18% dan Naive Bayes 74,47% menunjukkan bahwa model dan atribut yang digunakan baik untuk memprediksi tingkat IPK  mahasiswa. Algoritma C4.5 mampu menunjukkan atribut apa yang berpengaruh langsung pada tingkat IPK  mahasiswa yaitu Nilai TOEFL, jam belajar, pendidikan ayah, pekerjaan ayah, dan tempat tinggal mahasiswa.  Algoritma C4.5 tidak mampu  memperhitungkan peluang suatu klasifikasi jika jumlah  instan pada klasifikasi tersebut sangat sedikit pada kejadian data. Sebaliknya Naive Bayes tetap mampu memperhitungkan peluang kemunculan dan ketepatannya informasi yang dihasilkan  meski jumlah instan yang sedikit. Dalam penelitian ini data mahasiswa yang memiliki tingkat IPK cumlaude sangat sedikit, namun Naive Bayes tetap mampu mengukur Recall pada kelas ini sebesar 28,6% dan Precision sebesar 40%. 

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Diterbitkan

02-12-2020

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Prediksi Tingkat Indeks Prestasi Kumulatif Akademik Mahasiswa dengan Menggunakan Teknik Data Mining. (2020). Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(6), 1237-1244. https://doi.org/10.25126/jtiik.2020722493