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Dwi Hurriyati
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Jurnal Ilmiah Psyche
Published by Universitas Bina Darma
ISSN : 02163985     EISSN : 26558505     DOI : https://doi.org/10.33557/jpsyche.v12i1
Core Subject : Humanities, Education, Art, Social,
Arts Humanities Education Social Sciences Other
Jurnal Ilmiah Psyche adalah studi ilmiah yang berisi hasil penelitian, pemikiran dan diseminasi yang bertujuan untuk meningkatkan penelitian,ulasan dan aplikasi di bidang psikologi. Dengan Bidang Psikologi : - Psikologi Industri dan Organisasi - Psikologi Sosial - Psikologi Pendidikan - Psikologi Perkembangan - Psikologi Teknologi dan Internet - Psikologi Eksperimen - Psikologi Faal - Psikologi Olah Raga
Arjuna Subject : Psikologi - Psikologi (Lain-Lain)
Articles 121 Documents
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Implementasi Algoritma Support Vector Machine untuk Prediksi Kesejahteraan Psikologis Remaja Berdasarkan Analisis Sentimen Media Sosial Mutia Mawardah; Fatmasari Fatmasari; Susan Dian Purnamasari
Jurnal Ilmiah Psyche Vol. 20 No. 1 (2026): Jurnal Ilmiah Psyche
Publisher : Direktorat Riset dan Pengabdian Masyarakat Universitas Bina Darma Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/dzgmhj18

Abstract

The increasing use of social media among adolescents may influence their psychological well-being. This study aimed to implement the Support Vector Machine (SVM) algorithm to predict adolescent psychological well-being based on social media sentiment analysis. A quantitative approach was employed involving 157 social media users aged 13–18 years. Data were collected using the Social Media Sentiment Analysis Revised (ASMS-R) and Adolescent Psychological Well-Being Revised (KPD-R) instruments. The prediction model was developed using SVM with a Radial Basis Function (RBF) kernel and evaluated through a confusion matrix. The results revealed a positive relationship between social media sentiment and psychological well-being (r = 0.565). The SVM model achieved an accuracy of 65.63%. These findings indicate that social media sentiment analysis can be utilized as an indicator for predicting adolescent psychological well-being and may support the development of artificial intelligence-based early detection systems.

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