Implementasi Moving Average dan Kalman Filter pada Wireless Odometer untuk Informasi Service Kendaraan Bermotor

Abstract

Regular maintenance is one step to keep your car looking good, but many people still underestimate the importance of regular maintenance. A tool called Wireless Odometer has been created which can notify vehicle owners through the application if the vehicle mileage has entered the recommended criteria for periodic servicing. Based on previous research, Wireless Odometer still has weaknesses. sensor readings on this tool still have an error of 13.2% so it is still less accurate if applied to measure mileage on vehicles. It is necessary to have a digital filter that must be added to the sensor readings so that the sensor is resistant to noise that occurs due to mechanical or electrical. In this research, we implement the Moving Average Filter and Kalman Filter methods for sensor readings on the Wireless Odometer. After several tests were carried out, it was found that the percentage of reading errors when filtered reached 0.80% using the Kalman filter method and 1.81% using the Moving Average Filter method. It can be concluded that the filter that is suitable for use on this Wireless Odometer is the kalman filter.

Sistem Kendali Dan Monitoring Penyiram Tanaman Cabai Menggunakan Metode Fuzzy Dengan Aplikasi Telegram

“…Penelitian yang dibuat dengan memanfaatkan monitoring penyiram tanaman otomatis dengan menggunakan fuzzy serta memanfaatkan koneksi internet untuk sistem pemantauan jarak jauh. Pemantauan dapat dilakukan melalui smartphone (android) dan juga dapat melalui bot telegram untuk monitoring yang lebih efisien [13].…”