Penggunaan media sosial mengalami peningkatan pada kelompok usia remaja akhir dan dewasa awal, sehingga berisiko menimbulkan kecanduan. Hal ini semakin parah dengan situasi pandemi COVID-19 yang mengharuskan semua kegiatan dilakukan secara daring di rumah dan menyebabkan intensitas penggunaan media sosial semakin meningkat terutama di kalangan remaja. Kecanduan media sosial menyebabkan individu tidak dapat mengontrol waktu penggunaan, terutama ketika sesaat sebelum tidur sehingga mempengaruhi kualitas tidur individu. Penelitian ini bertujuan untuk mengetahui hubungan antara kecanduan media sosial dengan kualitas tidur pada mahasiswa selama situasi pandemi COVID-19. Penelitian deskriptif korelasi dengan pendekatan cross sectional ini melibatkan 110 mahasiswa Universitas Indonesia yang dipilih dengan teknik probability sampling dengan proportional random sampling. Kecanduan media sosial diukur dengan kuesioner Kecanduan Media Sosial dan kualitas tidur diukur dengan dengan kuesioner Pittsburgh Sleep Quality Index. Rata-rata skor kecanduan media sosial yaitu 83,05 dan rata-rata skor kualitas tidur yaitu 8,4. Hasil uji Pearson menunjukkan bahwa terdapat hubungan antara kecanduan media sosial dengan kualitas tidur (p=0,008; r=0,253), semakin kecanduan media sosial, semakin buruk kualitas tidur. Penelitian ini diharapkan dapat meningkatkan peran perawat dalam upaya promotif dan preventif terhadap perubahan gaya hidup akibat kemajuan teknologi dengan kualitas tidur, baik bagi mahasiswa, orangtua, maupun institusi pendidikan.

Kata kunci:

Kecanduan Media Sosial, Kulitas Tidur, Mahasiswa, Remaja, Pittsburgh Sleep Quality Index.

Social media has seen a sharp increase in use by adolescent and young adult that may further leads to an addiction. The escalation of usage largely due to the COVID-19 pandemic outbreak that encourage and ask people to do things online from home which then cause social media overuse mainly on juvenile. Excessive social media use provokes individuals incapable to control the appliance, particularly before-bed time that influence their sleep quality. This study aims to know the relations of social media addiction and sleep quality to undergraduate student amid COVID-19 pandemic. This descriptive correlation study with cross-sectional approach involved 110 undergraduate students form Universitas Indonesia who were  selected by probability sampling with proportional random sampling. The data were collected using Social Media Addiction questionnarie for the level of social media addiction and Pittsburgh Sleep Quality Index for sleep quality. Mean score of the social media addiction is  83,05 and mean score for sleep quality is 8,4. Pearson test results show that there was a correlation between social media addiction and sleep quality (p=0,008; r=0,253), the higher the addiction rate is, the worse the sleep quality will be. This study is expected to increase the role of nurses in promotive and preventive efforts in order to change the lifestyle due to technological advances with sleep quality, for students, parents, and academic institution.

Keywords:

Adolescents, Social Media Addiction, Sleep Quality, Undergraduate Students, Pittsburgh Sleep Quality Index.

Dalam era digital ini, pembelajaran dengan metode e-learning menjadi solusi yang umum diimplementasikan pada pendidikan jarak jauh. Kekurangan dari metode e- learning ini yaitu minimnya informasi pengajar mengenai antusiasme dan tingkat partisipasi siswa dalam pembelajaran. Masalah tersebut dapat diselesaikan dengan sistem yang mampu mendeteksi engagement siswa. Tingkat engagement siswa pada e-learning dapat ditentukan dari pandangan siswa dan ekspresi wajah siswa dalam pembelajaran. Sistem pendeteksi engagement siswa bekerja dengan cara mendeteksi arah mata siswa dan ekspresi wajah siswa menggunakan teknologi OpenCV dengan metode CNN (convolutional neural network) pada input file berupa video atau webcam secara real-time. Sistem akan memberikan output berupa nilai engagement siswa engaged berdasarkan durasi mata siswa menatap layar dan ekspresi wajah siswa berupa ekspresi netral atau positif. Sistem akan memberikan output berupa nilai kehadiran siswa disengaged berdasarkan durasi mata siswa tidak menatap layar dan ekspresi wajah siswa menunjukkan ekspresi negatif. Sistem menganalisis reaksi emosi siswa yang direpresentasikan dalam parameter nilai persentase reaksi netral, positif, dan negatif menggunakan dataset FER-2013. Sistem pendeteksi engagement siswa dapat mengukur presensi, status attendance siswa memperhatikan layar, emosi, impresi dan status engagement siswa dengan tingkat akurasi sebesar 83,33%, presisi sebesar 100%, recall sebesar 66,67% dan f1 score sebesar 80,00%.

In this digital era, the e-learning method is a common solution implemented on distance learning. The disadvantage of the e-learning process is the facilitator has no idea about students enthusiasm and participation rate during a lecture. This problem could be solved by a student engagement detection system. Student engagement can be determined by capturing the students eye-gazing focus rate and students facial expression during an online lecture. The student engagement detection system works by detecting student eye gaze and facial expression using OpenCV technology and CNN (convolutional neural network) method, receiving input through video file input or real-time webcam feed. The system will report on the student engagement level engaged if the students eyes are staring at the screen and student facial expression showing a neutral or positive impression. The system will report on the student engagement level disengaged if the students eye gaze were away from the screen and student facial expression showing a negative impression. This system will analyze students emotional reactions which represented by neutral, positive, or negative reaction percentage value using the FER-2013 dataset. Student Engagement Detection System could calculate student presence, attendance rate calculated through eye gaze focus rate, emotional reaction, impression and engagement status with an accuracy of 83,33%, a precision of 100%, recall of 66,67%, and f1 score 80,00%.