METODE DATA MINING UNTUK SELEKSI CALON MAHASISWA PADA PENERIMAAN MAHASISWA BARU DI UNIVERSITAS PAMULANG

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Published: Jan 29, 2018
DOI: https://doi.org/10.24853/jurtek.10.1.25-36
Keywords:
Data Mining Klasifikasi Penerimaan Mahasiswa

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Aries Saifudin
Universitas Pamulang
http://orcid.org/0000-0002-3882-633X

Abstract

Universitas Pamulang berusaha memberikan pendidikan tinggi dengan biaya yang terjangkau oleh kalangan bawah. Tetapi mahasiswanya banyak yang keluar di tiap semester, sehingga menyebabkan rasio jumlah mahasiswa baru dengan jumlah yang lulus tidak seimbang. Selain itu banyak mahasiswa yang tidak lulus tepat waktu, hal ini mengakibatkan rasio dosen dan mahasiswa tidak seimbang. Kedua hal ini akan mengurangi penilaian pada saat akreditasi. Penyebab keluarnya mahasiswa tanpa menyelesaikan pendidikannya, atau tidak dapat menyelesaikan pendidikannya tepat waktu belum dapat dideteksi dengan sistem seleksi saat ini. Pada penelitian ini diusulkan penggunaan teknik data mining untuk memprediksi ketepatan waktu lulus calon mahasiswa. Teknik data mining dan machine learning dapat digunakan untuk memprediksi berdasarkan data-data masa lalu. Metode data mining yang digunakan untuk memprediksi adalah klasifikasi, yaitu Naïve Bayes (NB), k-Nearest Neighbor (k-NN), Random Forest (RF), Decision Stump (DS), Decision Tree (DT), Rule Induction (RI), Linear Regression (LR), Linear Discriminant Analysis (LDA), Neural Network (NN), dan Support Vector Machine (SVM). Berdasarkan hasil implementasi dan pengukuran algoritma/model yang diusulkan diperoleh algoritma/model terbaik, yaitu Support Vector Machine (SVM) dengan akurasi 65.00%. Tetapi akurasi ini masih jauh dari nilai excellent (sangat baik).

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How to Cite
Saifudin, A. (2018). METODE DATA MINING UNTUK SELEKSI CALON MAHASISWA PADA PENERIMAAN MAHASISWA BARU DI UNIVERSITAS PAMULANG. Jurnal Teknologi, 10(1), 25–36. https://doi.org/10.24853/jurtek.10.1.25-36
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Vol. 10 No. 1 (2018): Jurnal Teknologi
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Articles

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