Prediksi Tingkat Kelulusan Mahasiswa Menggunakan Algoritma Decision Tree: Predicting Student Graduation Rates Using the Decision Tree Algorithm

Riska Septiani; Listina Nadhia Ningsih; Angga Pramadjaya

doi:10.57152/malcom.v6i3.2650

Authors

Riska Septiani Universitas Pamulang
Listina Nadhia Ningsih Universitas Pamulang
Angga Pramadjaya Universitas Pamulang

DOI:

https://doi.org/10.57152/malcom.v6i3.2650

Keywords:

Decision Tree, Kelulusan Mahasiswa, Machine Learning, Tingkat Prediksi

Abstract

Tingkat kelulusan mahasiswa merupakan salah satu indikator penting dalam menilai kualitas proses pendidikan di perguruan tinggi. Institusi pendidikan perlu melakukan analisis terhadap data akademik mahasiswa untuk memprediksi kemungkinan kelulusan sehingga dapat mengambil langkah strategis dalam meningkatkan keberhasilan studi mahasiswa. Penelitian ini bertujuan untuk melakukan prediksi tingkat kelulusan mahasiswa menggunakan metode Decision Tree. Metode ini dipilih karena mampu mengklasifikasikan data serta menghasilkan model yang mudah dipahami dalam bentuk aturan keputusan. Data yang digunakan dalam penelitian ini meliputi beberapa atribut akademik mahasiswa, seperti indeks prestasi, lama masa studi, jumlah SKS yang telah ditempuh, serta faktor pendukung lainnya yang berkaitan dengan proses akademik. Tahapan penelitian meliputi proses pengumpulan data, pra-pemrosesan data (data preprocessing), pembangunan model menggunakan algoritma Decision Tree, serta evaluasi terhadap hasil prediksi yang dihasilkan. Hasil analisis menunjukkan bahwa persentase mahasiswa yang memperoleh predikat “Dengan Pujian” pada tahun 2025 sebesar 6,01% dan meningkat menjadi 6,67% pada tahun 2026, dengan nilai rata-rata (mean) sebesar 6,34%. Namun demikian, berdasarkan hasil prediksi menggunakan metode Decision Tree, persentase mahasiswa yang diperkirakan memperoleh predikat Dengan Pujian menurun menjadi 3,00%, atau mengalami penurunan sekitar 3,34% dari rata-rata sebelumnya. Hal ini mengindikasikan bahwa dalam pola data yang dianalisis terdapat kecenderungan penurunan jumlah mahasiswa yang mampu mencapai prestasi akademik tertinggi.

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