Perbandingan Algoritma Decision Tree dan Random Forest dalam Klasifikasi Kepuasan Pengguna Sistem Informasi Akademik
Indonesia
DOI:
https://doi.org/10.29103/techsi.v16i2.25799Abstract
Kepuasan pengguna merupakan salah satu indikator penting dalam menilai keberhasilan Sistem Informasi Akademik di perguruan tinggi. Berbagai penelitian telah menerapkan algoritma machine learning untuk mengklasifikasikan tingkat kepuasan pengguna, di antaranya Decision Tree dan Random Forest. Penelitian ini bertujuan untuk menganalisis dan membandingkan performa kedua algoritma tersebut berdasarkan hasil penelitian terdahulu. Metode yang digunakan adalah studi literatur terhadap artikel nasional dan internasional yang dipublikasikan pada periode 2020–2025. Analisis dilakukan secara deskriptif dan komparatif dengan meninjau metrik performa seperti akurasi, presisi, dan recall. Hasil kajian menunjukkan bahwa Random Forest secara umum memiliki performa yang lebih unggul dan stabil dibandingkan Decision Tree, terutama dalam aspek akurasi. Namun, Decision Tree tetap memiliki keunggulan dalam hal interpretabilitas model. Oleh karena itu, pemilihan algoritma klasifikasi sebaiknya disesuaikan dengan tujuan analisis dan kebutuhan institusi.
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Copyright (c) 2026 Rafi Septiawan Putra, Hasanal Fachri Satia Simbolon, Ade Linhar P, Fahmi Izhari
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