RANCANG BANGUN MONITORING DENGAN METODE KLASIFIKASI ASYMMETRY INDEX BAGI PENDERITA TEMPOROMANDIBULAR DISORDER TERINTEGRASI INTERNET OF THINGS - Bagasunni’am, Moh. Hibaturrohman; Nurussa’adah, n/a; Purnomowati, Endah Budi (Jurnal Mahasiswa TEUB, 2024)
Temporomandibular Disorder (TMD) is a condition that causes pain in the orofacial area and musculoskeletal system with a prevalence of 5% to 12% in adults. TMD is common due to lack of awareness of the disease, even though its impact can severely disrupt the daily activities of the sufferer. It can cause periodic or constant pain in the stomatognathic system, namely the Temporomandibular Joint (TMJ) which is the most frequently used part of the body (eating and speaking), patients can experience limited jaw motion, impaired joint function, muscle pain, joint pain, facial pain, and pain when opening the jaw. The sooner it is detected, the faster the treatment for this disease. However, the frequency of monitoring TMJ health conditions is rarely done. Currently, the only methods used are Xray diagnosis or panaromic radiography and the resulting data does not always provide accurate information. When viewed from an economic point of view, the cost required for detection is considered quite expensive and requires a long process. Through these problems, technology is needed that can detect the patient's condition non-invasively and routinely. The technology needed can mainly record the activity of the masseter and temporalis muscles by utilizing Electromyography (EMG) sensors that can be connected to an Android-based monitoring application. And can provide a classification of the severity of the patient with the Asymmetry Index method and provide education in reducing these risk factors. This innovation has been tested on 15 patients with a classification accuracy rate of 86.7% compared to the results of the doctor's diagnosis. In testing the system monitoring application, the average data transmission time is faster using the 5 GHz network than 2.4 GHz, but the difference is very small and almost invisible, namely 0.255 seconds. These results show that this TMD monitoring tool is effective for detecting and monitoring TMD conditions on an ongoing basis, reducing dependence on invasive methods and assisting users in classifying TMD conditions.Keywords : Temporomandibular Disorder (TMD), Internet of Things, Asymmetry Index, Electromyography (EMG), Monitoring