The Implementation of the K-Medoid Clustering for Grouping Hearing Loss Function on Excessive Smartphone Use

• Published in: JOIV : international journal on informatics visualization Online Vol. 7; no. 4; p. 2523
• Main Authors: Wahyudi, Eri, Meidelfi, Dwiny, -, Nofrizal, Saam, Zulfan, -, Juandi
• Format: Journal Article
• Language: English
• Published: 31.12.2023
• ISSN: 2549-9610; 2549-9904; 2549-9904
• DOI: 10.62527/joiv.7.4.1873

During the current pandemic, smartphones have become a means of learning for all students in Indonesia, including high school students. Students use smartphones to send assignments, learn via video calls, and conduct online exams. The prolonged use of smartphones, from the beginning of learning hours in the morning to study hours in the evening, has a terrible impact on the ear health of high school students in Padang. Excessive smartphone use caused a decrease in the student's hearing function. Therefore, this study aims to group the audiometry results of high school students in Padang who have a hearing loss function. The audiogram result is only performed as the result of a frequency test of the subject's hearing in both the left and right ear. Conventionally, an otolaryngologist concluded the final decision of hearing loss ability. This research proposed an automatic classification of audiometry results using machine learning methods. The K-Medoids clustering was selected to classify the audiometry data in this research. Of 210 audiometry data, 91 data is confirmed by an otolaryngologist as valid data. By using the K-Medoids clustering, 93 data is classified into Normal hearing, Mild Hearing loss, and Moderate Hearing loss. The proposed model successfully grouped the audiometry data into three categories. The confusion matrix is applied to measure the model performance, which has 28,3% accuracy, 64,3% precision, and 21,4% recall.