Analisis Akurasi Prediksi Perubahan Aktivitas Pada Sistem Monitoring Aktivitas Jarak Jauh Pasien Isolasi Mandiri Berbasis IOT
Patients who contract the disease should avoid contact with other people. One way to do this is to self-isolate at home. The family of the patient who cares for the activities that are carried out in self-isolation to find out the condition of the patient's condition, his condition is improving or deteriorating. To avoid direct contact, the patient's activity, independently, can be monitored by remotely predicting changes in patient activity using an Internet of Things-based remote monitoring system for self-isolating patient activities. This cellular-based monitoring system uses an accelerometer sensor to retrieve data on changes in patient activity and analyzes the effect of several variations in the number of data samples and sliding-windows on the accuracy of the system in predicting changes in patient activity. Variations in the number of N samples tested were 4,6,8,10,20,30,40,50,60,70,80,90 and 100 samples, while the sliding-window N variation tested was 1 ,2,3,4,5,6,7,8,9 and 10 samples where there is a change in activity every 30 seconds for 330 seconds (10 changes in activity) for each number of N samples and N sliding windows. The results shown are N sample data = 6 providing the highest activity change prediction accuracy, amounting to 90.15%, while N sliding window data = 6 providing the highest activity change prediction accuracy, amounting to 92.72%.
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