Peningkatan Performa Model Klasifikasi Tingkat Depresi Pengguna Media Sosial X Menggunakan Algoritma Random Forest Dan Hyperparameter Tuning

Authors

  • Fince Tinus Waruwu Universitas Budi Darma

Keywords:

Kelayakan Kredit, Naive Bayes, Sistem Pendukung Keputusan, Klasifikasi, Data Simulasi

Abstract

Depresi adalah masalah kesehatan mental serius yang mempengaruhi berbagai kalangan. Media sosial X (sebelumnya Twitter) dengan 139 juta pengguna aktif di Indonesia sering digunakan untuk mengungkapkan perasaan dan opini. Diskusi tentang kesehatan mental di X meningkat 17% antara 2018. Penelitian ini bertujuan meningkatkan klasifikasi tingkat depresi pengguna X dengan mengganti algoritma Multinomial Naive Bayes dengan Random Forest, melakukan tuning hyperparameter, dan mengeksplorasi penggunaan n-gram (unigram, bigram, trigram). Dataset dari penelitian terdahulu (Armaini dkk, 2024) mengandung 2662 data gambar terkait kesehatan mental pengguna X, dikelompokkan dalam tiga tingkat depresi: ringan, sedang, dan berat. Model Random Forest diterapkan dengan tiga skenario N-Gram dan pembagian dataset 90:10, 80:20, dan 70:30. Hasil menunjukkan model Random Forest dengan N-Gram Trigram dan parameter optimal (n_estimators = 200, max_depth = None, min_samples_split = 2, min_samples_leaf = 10) pada rasio 90:10 memberikan akurasi terbaik 98,50%.

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Published

2025-01-28

How to Cite

Waruwu, F. T. (2025). Peningkatan Performa Model Klasifikasi Tingkat Depresi Pengguna Media Sosial X Menggunakan Algoritma Random Forest Dan Hyperparameter Tuning. JURNAL MEDIA INFORMATIKA BUDIDARMA, 9(1), 9–14. Retrieved from https://ejurnal.stmik-budidarma.ac.id/index.php/mib/article/view/8128

Issue

Vol. 9 No. 1 (2025): Januari 2025

Section

Articles

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