Aplikasi Prediksi Gangguan Tidur Berbasis Gaya Hidup Menggunakan Teknik Data Mining

Budi Ramadhani, Fauzi Yussa Rahman, Ihsanul Fikri, Erfan Karyadiputra

Abstract


Gangguan tidur seperti insomnia dan sleep apnea merupakan permasalahan kesehatan yang berdampak pada penurunan kualitas hidup dan produktivitas individu. Sayangnya, banyak penderita yang tidak menyadari gangguan ini secara dini. Penelitian ini bertujuan membangun sistem prediksi gangguan tidur berbasis machine learning dengan pendekatan ensemble learning menggunakan kombinasi algoritma Random Forest dan XGBoost melalui metode soft voting. Dataset yang digunakan mencakup variabel gaya hidup dan kesehatan seperti usia, jenis kelamin, tekanan darah, detak jantung, durasi tidur, aktivitas fisik, tingkat stres, dan kualitas tidur. Data dianalisis dan dilatih menggunakan Google Colab, kemudian diimplementasikan dalam aplikasi berbasis web menggunakan Streamlit. Hasil pengujian menunjukkan bahwa model ensemble memberikan performa prediksi yang sangat baik dengan akurasi sebesar 96%, serta precision dan recall tinggi pada semua kelas. Aplikasi ini juga dilengkapi fitur rekomendasi berbasis hasil prediksi dan kemampuan ekspor laporan ke format PDF. Temuan ini menunjukkan bahwa integrasi kecerdasan buatan dalam bentuk aplikasi dapat membantu masyarakat mengenali gangguan tidur secara dini dan mengambil langkah preventif secara mandiri.

Kata Kunci: Gangguan Tidur, Ensemble Learning, Random Forest, XGBoost, Streamlit


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DOI: https://doi.org/10.35308/jti.v4i2.13099

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



Jurnal Teknologi Informasi
e-ISSN: 2829-8934  I  DOI: 10.35308
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