Skripsi

KLASIFIKASI KESEHATAN MENTAL BERDASARKAN UNGGAHAN MEDIA SOSIAL MENGGUNAKAN TF-IDF DAN XGBOOST

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Penilaian

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Mental health is a critical issue in global public health, especially in the digital era where individuals often express their psychological conditions through social media posts. This study aims to develop a multi-class classification model to detect mental health conditions based on social media text using TF-IDF (term frequency–inverse document frequency) for feature extraction and XGBoost (extreme gradient boosting) as the classification algorithm. The dataset used consists of 53,043 English texts categorized into seven mental health classes: Normal, Depression, Suicidal, Anxiety, Stress, Bipolar, and Personality Disorder. The best-performing model, with hyperparameters set to learning rate = 0.2, max depth = 5, and n_estimators = 1000, achieved an accuracy of 78%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507004298	T179373	T1793732025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1793732025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xiii, 97 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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