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Journal : JECCOM

Implementation of K-Nearest Neighbor for Fall Position Detection of Dementia Patients Based Microcontroller Yulastri; Era Madona; Laxmy Devy; Anggara Nasution; Nur Iksan
JECCOM: International Journal of Electronics Engineering and Applied Science Vol. 1 No. 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30642/jeccom.1.2.79-85.2023

Abstract

A microcontroller-based detection tool for the presence of patients with dementia has been made using the K-Nearest Neighbor (KNN) method with the help of coordinate points that can be seen via Google Maps. which is based on patient care with a patient-oriented approach. The targets of this research are (a) designing and implementing a fall detection system using the mpu6050 sensor, (b) using the (KNN) method to determine the coordinates of the location of dementia patients using GPS. The research method starts from making a prototype and measuring system performance. The test results on GPS produced an average latitude error of 0.002091% and an average longitude error of 0.000032% in Pauh District, while in Lubuk Kilangan District the average latitude error was 0.002641% and an average longitude error of 0.000150%. The KNN method with the Eucledian distance formula can help supervisors find out the nearest police station to the patient through the coordinate points detected by GPS by taking the smallest value from the comparison of values in the form of degrees between the Pauh police station and the Lubuk Kilangan police station for the patient. Overall the tool can function well.
Design of Internet Of Things (IoT) trainer kit with Multi Communication Era Madona; Efrizon; Anggara Nasution; Rara Yetrisia; Laxsmy Devy
JECCOM: International Journal of Electronics Engineering and Applied Science Vol. 1 No. 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30642/jeccom.1.2.47-57.2023

Abstract

Industry 4.0, or the Fourth Industrial Revolution, is a recent technological development that has significantly changed the industrial production process. One of the keys to this change is the Internet of Things (IoT), where the role of humans is likely to diminish and be replaced by machines. Aligning the workforce with IoT is important, but creating a qualified workforce is a challenge. IoT learning modules are needed to help students understand IoT concepts. The purpose of this research is to design an Internet of Things kit module with Radio communication with LoRa SX1278 Ra-02. The research stages start from literature study, hardware design, software design and overall testing. Module testing is done by sending DHT11 sensor data using LoRa SX1278 Ra-02 and monitored in Thingspeak. From the test results that have been carried out, it can be concluded that sending using LoRa (Long Range) technology is influenced by distance and obstacles. At a distance of 40 to 230 metres, communication between the Lora Transmitter and Lora Receiver is successful, which shows that LoRa technology is able to overcome communication distances in that range. However, at a distance of more than 230 metres to 300 metres, data transmission can still be done by the Lora Transmitter, but the data cannot be received by the Lora Receiver, indicating a bottleneck in communication.