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All Journal JURNAL PENDIDIKAN TAMBUSAI Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Sonjaya, Rebina Putri
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Computer Science & IT Education Social Sciences Other

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Predicting Student Depression Using the Naive Bayes Model on the Student Depression Dataset from Kaggle Sonjaya, Rebina Putri; Gintara, Andre Rangga; Riza, Lala Septem; Nursalman, Muhammad; Nugraha, Eki; Wahyudin, Didin
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 1 (2025): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v4i1.448

Abstract

Background of Study: The increasing prevalence of depression among college students highlights the urgent need for effective early detection strategies to promote mental well-being within higher education environments. Aims and Scope of Paper: This study aims to develop a predictive model for student depression using the Naive Bayes classification algorithm, with a focus on identifying key contributing factors from student-related data. Methods: The research utilizes the Student Depression dataset from Kaggle, containing structured survey data on academic stress, sleep duration, financial stress, GPA, and family mental health history. Data preprocessing included feature selection, handling of missing values, and normalization. The dataset was split into training and testing sets at a 75:25 ratio. Model training was conducted using the R programming language with the application of Laplace smoothing. Result: The Naive Bayes model achieved an accuracy of 77.66%, a specificity of 84.21%, and a sensitivity of 68.42%, indicating strong predictive performance, particularly in identifying depressive cases. Financial and academic stress were identified as the most influential factors. Conclusion: Despite its simplicity, the Naive Bayes algorithm proves to be an effective tool for initial screening of students at risk of depression, offering valuable support for educational institutions in delivering timely mental health interventions.
Pengujian Prasyarat Analisis Data Nilai Kelas: Uji Normalitas dan Uji Homogenitas Sonjaya, Rebina Putri; Aliyya, Farrel Rahma; Naufal, Syahandika; Nursalman, Muhammad
Jurnal Pendidikan Tambusai Vol. 9 No. 1 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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Abstract

Pengujian prasyarat analisis data merupakan tahapan penting dalam analisis statistik untuk memastikan validitas hasil yang diperoleh, khususnya dalam pengolahan data nilai kelas. Penelitian ini membahas dua pengujian utama, yaitu uji normalitas dan uji homogenitas. Uji normalitas digunakan untuk memeriksa apakah data berdistribusi normal, dengan metode seperti Chi-Square, Liliefors, Shapiro-Wilk, dan Kolmogorov-Smirnov. Uji homogenitas variansi bertujuan memastikan keseragaman variansi antar kelompok data menggunakan teknik seperti Levene's test, Bartlett's test, Brown-Forsythe test, dan Fligner-Killeen test. Dalam konteks data nilai kelas, pengujian ini diterapkan untuk menentukan kesesuaian data dengan asumsi yang diperlukan dalam analisis variansi (ANOVA). Hasil analisis ini tidak hanya mendukung validitas statistik tetapi juga memberikan wawasan yang mendalam tentang efektivitas pembelajaran dan pencapaian siswa. Penelitian ini menggunakan pendekatan kualitatif berbasis studi pustaka untuk menggali pemahaman filosofis dan teoritis tentang pengujian prasyarat analisis data. Temuan ini menegaskan pentingnya pengujian prasyarat untuk mendukung keputusan berbasis data yang berkualitas di bidang pendidikan.
Co-Authors Aliyya, Farrel Rahma Didin Wahyudin, Didin Eki Nugraha Gintara, Andre Rangga Lala Septem Riza Naufal, Syahandika Nursalman, Muhammad