Klasifikasi Tingkat Stress Pada Manusia Dengan Menggunakan Algoritma Naive Bayes Pada Rapidminer - Andriano, Choky; Pratama, Alvian Saputra; Ramadan, Fadli; Amali, Amali (Jurnal SIGMA, 2024)
Stress is a state of anxiety or mental tension caused by a difficult situation. There are different levels of stress, which indicate how severe and strong the impact is on the body and mind. By recognizing and understanding the level of stress we experience, we can be wiser in distinguishing the type of stress that we are experiencing. The study aims to determine whether classification techniques with the application of the Naïve Bayes algorithm can be used to predict stress levels in humans, as well as to obtain information about accuracy, precision, and recall obtained when conducting patient data testing using Naïva Bayes. The study uses classification and phase-stage techniques in data mining to classify patient data for human stress detection with Naïv Bayes' algorithm using the RapidMiner tool. Using the Naïve Bayes algorithm method for human stress level datasets has been proven to be very effective, producing an accuracy rate of 99.17%. Precision for pred. is low (96.77%, precision for pred . is normal (100.00%, and precision for pred. is high (100.00%. Recall for low 100.00%, recall for normal 97.98%, and recall for high 100.00%. The stress level is determined by humidity, step count, and temperature, thus producing the data.