JOURNAL INFO
Model Prediksi Gangguan Tidur berdasarkan Beberapa Faktor menggunakan Machine Learning
Abstract
Gangguan tidur merupakan masalah kesehatan yang signifikan dan dapat mempengaruhi kualitas hidup individu. Penelitian ini bertujuan untuk mengembangkan model prediksi gangguan tidur menggunakan teknik machine learning dengan mempertimbangkan beberapa faktor risiko. Dataset yang digunakan merupakan data sekunder yang diperoleh dari Kaggle dan dianalisis menggunakan beberapa algoritma machine learning, termasuk model machine learning seperti logistic regression, decision tree dan gradient boosting. Hasil penelitian menunjukkan bahwa model Gradient Boosting menghasilkan akurasi prediksi tertinggi, dengan nilai akurasi 99% berdasarkan AUC – ROC Score. Faktor-faktor seperti usia (ages), durasi tidur (sleep duration), kategori BMI dan pekerjaan ditemukan sebagai prediktor yang paling signifikan. Temuan ini menunjukkan bahwa penggunaan machine learning dapat menjadi alat yang efektif dalam mengidentifikasi individu yang berisiko mengalami gangguan tidur, sehingga memungkinkan intervensi dini dan pengelolaan kesehatan yang lebih baik. Penelitian ini memberikan kontribusi penting dalam pemahaman tentang hubungan antara faktor-faktor risiko dan gangguan tidur serta potensi aplikasi machine learning dalam bidang melalui pemilihan model prediksi dengan akurasi terbaik.
Keywords
Gangguan tidur; Machine learning; Prediksi
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DOI: https://doi.org/10.24114/cess.v9i2.61068
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CESS (Journal of Computer Engineering, System and Science)
CESS (Journal of Computer Engineering, System and Science) is licensed under a Creative Commons Attribution 4.0 International License