ANALISIS PREDIKSI GANGGUAN TIDUR MENGGUNAKAN MACHINE LEARNING-BASED TECHNIQUE

ALDIANTO, DENISE (2024) ANALISIS PREDIKSI GANGGUAN TIDUR MENGGUNAKAN MACHINE LEARNING-BASED TECHNIQUE. S1 thesis, Universitas Mercu Buana - Menteng.

	Text (Cover) 4182011051-Denise Aldianto-01 Cover - Denis Aldianto.pdf Download (258kB)
	Text (Abstrak) 4182011051-Denise Aldianto-02 Abstrak - Denis Aldianto.pdf Download (41kB)
	Text (Bab 1) 4182011051-Denise Aldianto-03 Bab 1 - Denis Aldianto.pdf Restricted to Registered users only Download (119kB)
	Text (Bab 2) 4182011051-Denise Aldianto-04 Bab 2 - Denis Aldianto.pdf Restricted to Registered users only Download (434kB)
	Text (Bab 3) 4182011051-Denise Aldianto-05 Bab 3 - Denis Aldianto.pdf Restricted to Registered users only Download (149kB)
	Text (Bab 4) 4182011051-Denise Aldianto-06 Bab 4 - Denis Aldianto.pdf Restricted to Registered users only Download (224kB)
	Text (Bab 5) 4182011051-Denise Aldianto-07 Bab 5 - Denis Aldianto.pdf Restricted to Registered users only Download (43kB)
	Text (Daftar Pustaka) 4182011051-Denise Aldianto-08 Daftar Pustaka - Denis Aldianto.pdf Restricted to Registered users only Download (120kB)
	Text (Lampiran) 4182011051-Denise Aldianto-09 Lampiran - Denis Aldianto.pdf Restricted to Registered users only Download (291kB)
	Text (Lembar Keabsahan) 41820110051-Denise Aldianto-10 Hasil Scan Formulir Pernyataan Keabsahan dan Persetujuan Publikasi Tugas Akhir - Denis Aldianto.pdf Restricted to Repository staff only Download (156kB)

Abstract

Tidur merupakan salah satu indikator penting bagi seseorang. Tidur yang buruk berdampak serius pada kesehatan. Kondisi ini seringkali dipicu oleh tekanan kerja yang tinggi dan ketidakseimbangan antara pekerjaan dan waktu istirahat. Penelitian mengenai hal serupa telah dilakukan sebelumnya. Akan tetapi, penelitian tersebut belum memaparkan faktor apa saja yang paling mempengaruhi gangguan tidur. Oleh karena itu, dalam penelitian ini kami melakukan analisa lebih mendalam mengenai faktor-faktor penyebab gangguan tidur seperti: gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress level, BMI, heart rate, dan daily steps. Kemudian, kami melakukan penelitian terhadap gangguan tidur dengan menggunakan Machine Learning (ML). Model yang kami gunakan antara lain Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logisctic Regression (LR), Convolutional Neural Network (CNN), dan Long Short-Term Memory Network (LSTM) yang bertujuan untuk menguji seberapa efektif penerapan model berdasarkan informasi dari data dan signifkansi faktor tertentu terhadap gangguan tidur. Penelitian ini terdiri dari beberapa tahapan: (1) Pengumpulan data (2) Data yang telah terkumpul akan melalui pre-processing (3) Melatih model yang dapat mengolah data agar dapat dievaluasi untuk memahami kontribusi indikator terhadap prediksi gangguan tidur. Hasil dari penelitian ini memberikan informasi bahwa model yang kami bangun dapat memprediksi gangguan tidur secara efektif. Sleep is crucial indicator for an individual. Poor sleep quality has serious implication for health. This condition is often triggred by high work pressure and imbalance between work and rest time. While previous research with similar topic has been conducted, it has not comprehensively elucidated the key factors influencing sleep disorders. Therefore, this study conducts more in-depth analysis of factors contributing to sleep disorders including; gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress level, BMI, heart rate, and daily steps. Subsequently, we employ Machine Learning (ML) techniques to investigate further sleep disorders. The models include: Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Convolutional Neural Network (CNN), and Long Short-Term Memory Network (LSTM). The objective is to assess to effectiveness of model implementation based on information from data and the significance of specific factors in predicting sleep disturbances. The research comprises several stages: (1) Data collection, (2) Pre- processing of the collected data, and (3) Training models capable of processing data for evaluation to understand the contribution of indicators to sleep disorder predictions. The findings of this study provide insights into the effectiveness of the constructed models in predicting sleep disorders.

Actions (login required)

View Item