Skripsi

KLASIFIKASI GANGGUAN KECEMASAN PADA PEMAIN GAME ONLINE DENGAN ALGORITMA SUPPORT VECTOR MACHINES

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Penilaian

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The Significant increase in prevalencegame onlineraising attention to its potential impact on mental health, particularly anxiety disorders. Early and accurate detection of anxiety symptoms among gamersgame onlineessential for timely implementation of interventions. This study used an algorithmSupport Vector MachineSVM, one of the supervised machine learning methods, to classify anxiety levels based on behavioral parameters obtained through player surveys and in-game activity log analysis. Factors analyzed included daily play duration, in-game social interactions, self-reported stress levels, and the manifestation of specific anxiety symptoms. The results showed that SVM was effective in classifying anxiety levels in players.game online.The use of Linear Kernel with parameter C = 1 provides the best performance with an accuracy of 62%, precision 66%, recall 62%, andF1-score 63%, as well as efficient computing time (1.24 seconds).

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507005579	T183703	T1837032025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1837032025

Publisher

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

Collation

xvi, 68 hlm.; ilus,; tab, 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.507

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Prodi Teknik Informatika
Program Aplikasi dengan Kegunaan Khusus

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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