Deteksi Stres dan Depresi Unggahan Media Sosial dengan Machine Learning
DOI:
https://doi.org/10.37859/jf.v15i1.9067
Abstract
Penelitian ini menyajikan sebuah model pembelajaran mesin yang mengintegrasikan Support Vector Machine (SVM), Natural Language Processing (NLP), dan SHapley Additive exPlanations (SHAP) untuk mendeteksi stres dan depresi dari unggahan di Twitter. Dataset ini terdiri dari postingan yang dikumpulkan selama satu tahun terakhir, yang dilabeli ke dalam kategori stres, depresi, dan netral. Teknik NLP-tokenization, penghilangan stopword, stemming, dan Term Frequency-Inverse Document Frequency (TF-IDF)-diterapkan untuk membersihkan teks dan mengekstrak fitur-fitur yang relevan. Langkah-langkah ini memastikan hanya kata-kata yang bermakna yang memengaruhi klasifikasi, sehingga meningkatkan kinerja prediktif. SVM berfungsi sebagai pengklasifikasi utama karena keefektifannya dalam data teks berdimensi tinggi. SHAP meningkatkan kemampuan interpretasi dengan menyoroti fitur-fitur berpengaruh yang mendorong prediksi, sehingga meningkatkan transparansi. Hasil penelitian menunjukkan bahwa kata-kata seperti “stres”, “lelah”, dan “cemas” sangat mengindikasikan stres, sedangkan “depresi”, “kecewa”, dan “menyerah” menandakan kecenderungan depresi. Frasa seperti “lelah secara mental” semakin mendukung proses identifikasi. Model ini mencapai akurasi 96,44%, dengan presisi, recall, dan skor F1 yang tinggi (rata-rata 96%). Matriks kebingungan mengkonfirmasi keefektifan model dalam mengklasifikasikan tiga kategori dengan kesalahan minimal. Integrasi NLP, SVM, dan SHAP tidak hanya meningkatkan akurasi klasifikasi, tetapi juga meningkatkan penjelasan, menjadikan model ini alat yang menjanjikan untuk deteksi dini dan pemahaman kondisi kesehatan mental melalui analisis media sosial.
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