Setiawan, M.H.Ma’arif, A.Marhoon, H.M.Sharkawy, A.-N.Çakan, A.2025-05-112025-05-1120232685-79362685-9572https://doi.org/10.12928/biste.v5i2.8089https://hdl.handle.net/20.500.13091/10058This research focuses on distance estimation using the Kalman Filter method in ultrasonic sensors. The study addresses the issue of accuracy levels in ultrasonic sensors and proposes the use of Kalman Filter to enhance accuracy. The Kalman Filter is comprised of two key components: prediction and update. In this research, the Kalman Filter method is implemented using Arduino Uno and the HC-SR04 ultrasonic sensor. The experimental results involve a comparison between sensor data before and after the application of the Kalman Filter. The filtering outcomes are influenced by the values assigned to the noise sensor covariance matrix (R) and process noise covariance (Q). Through experimentation, it was determined that the optimal values for R and Q are 100 and 0.01, respectively. It is important to strike a balance when selecting these values, as values that are too close may render the filtering result imperceptible, while values that are too disparate may lead to the elimination of original sensor data. In conclusion, the application of the Kalman Filter method in ultrasonic sensors enables accurate estimation and enhances sensor value accuracy by up to 7%. This research contributes to the advancement of distance estimation techniques in the field of ultrasonic sensing. © 2023, Universitas Ahmad Dahlan. All rights reserved.eninfo:eu-repo/semantics/openAccessArduinoDistanceKalman FilterUltrasonicArticle10.12928/biste.v5i2.80892-s2.0-105003082684