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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Hafid Ilmanu Romadhoni
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Implementasi Analisis Perbandingan Filter Kalman, Moving Average dan Eksponensial pada Alat Pengukur Kadar Kolesterol berbasis Non-Invasif Hafid Ilmanu Romadhoni; Rizal Maulana; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stroke is a dangerous disease and can occur at any time. High cholesterol is one of the causes of stroke. Cholesterol measurements are generally carried out on an invasive basis, namely taking blood samples. Invasive-based cholesterol measurements can damage the skin tissue of the patient by using a needle to remove blood from the body. There is a way that is safer than the risk of injury that is based on non-invasive. There are several non-invasive methods, one of which utilizes infrared light that penetrates the skin so that it can measure what is flowing in the blood. Non-invasive measurements have been carried out in the measurement of cholesterol, but there is no filter program in it. Filters are needed to improve sensor accuracy. Kalman, Moving Average, and exponential filters can be applied to reduce errors. This study has aim to examine the role of filters in non-invasive cholesterol measurement. The parameter measured is the BPM value from the sensor. The sensor from the research has an accuracy of 99.86%. Kalman, Moving Average, and exponential filters have been tested for accuracy compared to invasive cholesterol measuring tools. On the measuring instrument with the addition of a kalman filter, an accuracy of 92.595% was obtained. For measuring instruments with the addition of a Moving Average filter, an accuracy of 95.189% is obtained. In measuring instruments with the addition of an exponential filter, an accuracy of 93.682% is obtained.
Co-Authors Rizal Maulana Widasari, Edita Rosana