Prediksi Tingkat Stres Mahasiswa Menggunakan Algoritma Decision Tree
DOI:
https://doi.org/10.26905/jisad.v4i1.16616Keywords:
Decision Tree; , Stres Mahasiswa; , Machine Learning; , Klasifikasi; , Prediksi;Abstract
Penelitian ini bertujuan menerapkan algoritma Decision Tree untuk memprediksi tingkat stres pada mahasiswa menggunakan variabel non-psikologis. Stres akademik merupakan permasalahan umum yang dapat memengaruhi performa dan kesejahteraan mahasiswa sehingga deteksi dini menjadi penting. Model menggunakan fitur jenis kelamin, usia, IPK, tahun studi, status pernikahan, dan perilaku pencarian bantuan guna menghindari kebocoran data dan memastikan prediksi berbasis faktor tidak langsung. Pengolahan data dilakukan menggunakan Python dan pustaka scikit-learn. Model Decision Tree memperoleh akurasi sebesar 55% dan menunjukkan kinerja lebih baik dalam mengidentifikasi mahasiswa dengan stres rendah. Namun, model masih kesulitan mengenali kasus stres tinggi karena keterbatasan relevansi fitur. Secara keseluruhan, pendekatan ini memberikan model dasar yang mudah dipahami dan dapat membantu institusi pendidikan dalam mengidentifikasi mahasiswa berisiko serta mendukung langkah pencegahan dan intervensi awal.
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