Pengembangan Model Deteksi Dini Kesehatan Mental Remaja Menggunakan Support Vector Machine Berbasis Analisis Teks: Early Detection Model Development of Adolescent Mental Health Using Support Vector Machine Based on Text Analysis

Rima Ruktiari; Hamid Wijaya; Muhammad Rizal

doi:10.57152/malcom.v6i1.2341

Authors

Rima Ruktiari Universitas Sembilanbelas November Kolaka
Hamid Wijaya Universitas Sembilanbelas November Kolaka
Muhammad Rizal Universitas Dipa Makassar

DOI:

https://doi.org/10.57152/malcom.v6i1.2341

Keywords:

Analisis Teks, Deteksi Dini, Kesehatan Mental Remaja, Support Vector Machine

Abstract

Kesehatan mental remaja merupakan aspek fundamental dalam pembangunan generasi muda. Peningkatan prevalensi depresi, kecemasan, dan gangguan kepribadian di kalangan remaja menimbulkan dampak serius terhadap kesejahteraan individu dan masyarakat. Evaluasi tradisional melalui wawancara atau kuesioner bersifat subjektif dan memerlukan waktu lama, sehingga sering terlambat dalam memberikan intervensi dini. Penelitian ini bertujuan mengembangkan model deteksi dini kesehatan mental remaja berbasis analisis teks menggunakan algoritma Support Vector Machine (SVM). Dataset terdiri dari ±4000 entri teks dari forum Reddit dengan lima kategori: stress, depresi, bipolar, gangguan kepribadian, dan anxiety. Setelah dilakukan resampling, setiap kelas memiliki sekitar 200 data. Teks diproses melalui cleaning, tokenisasi, penghapusan stopwords, dan stemming. Representasi fitur dilakukan dengan gabungan word-level dan char-level TF-IDF, menghasilkan 28.456 fitur. Dua model diuji, yaitu Linear SVC dan SVC dengan kernel RBF, melalui grid search. Hasil terbaik diperoleh SVC (RBF) dengan akurasi 63.3% dan macro-F1 0.635 pada data uji. Analisis menunjukkan kelas 0 (stress) paling stabil (F1=0.730), sedangkan kelas 1 (depresi) masih sulit dideteksi (F1=0.545). PR Curve memperlihatkan AP tertinggi pada kelas 0 (0.844) dan terendah pada kelas 4 (0.638). Hasil ini menunjukkan bahwa SVM mampu memberikan baseline yang menjanjikan untuk deteksi dini kesehatan mental berbasis teks, meskipun optimasi lanjutan tetap diperlukan.

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