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All Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Utomo, Mhd. Rizki Fajar
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Computer Science & IT Control & Systems Engineering

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Prediksi Tingkat Depresi Remaja Menggunakan Metode Naïve Bayes Classifier: Analisis Faktor Psikologis Dan Lingkungan Putri, Dini Ridha Dwiki; Fahlevi, Muhammad Reza; Sadikin, Muhammad; Utami, Rida; Utomo, Mhd. Rizki Fajar
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.545

Abstract

This study developed a predictive method using the Naïve Bayes Classifier to assess depression levels in adolescents, focusing on psychological and environmental factors. The method measures the probability of various symptom categories: emotional, physical, and cognitive symptoms. Analysis results indicate that adolescents with a combination of these symptoms have a high risk of severe depression, with the highest probability value v=0.0056. The most common symptoms in the sample include decreased energy with fatigue and reduced activity, anxiety, changes in appetite (slight decrease or increase), and unexplained aches or pains, underscoring the strong influence of psychological factors. This predictive model aids in early identification of depression levels, and the Naïve Bayes Classifier is proven effective for analyzing relationships between internal and external factors. This research can enhance mental health awareness among adolescents and parents, and educate on the negative impact of environmental factors to support early detection and prevention of mental disorders.
Co-Authors MUHAMMAD REZA FAHLEVI Muhammad Sadikin Putri, Dini Ridha Dwiki Utami, Rida