Klasifikasi Dehidrasi Tubuh Manusia Berdasarkan Citra RGB Pada Warna Urine Menggunakan Metode K-Nearest Neighbor

Authors

  • Putri Nugraheni Utami Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Jakarta
  • Veri Arinal Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Jakarta
  • Dadang Iskandar Mulyana Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Jakarta

DOI:

https://doi.org/10.30865/mib.v6i1.3290

Keywords:

Dehydration, Urine, RGB, KNN, Matlab

Abstract

Dehydration is a condition in the body where the amount of fluid that comes out is more than the amount of fluid that enters so that the body experiences a lack of fluids. The simplest way to find out if you are dehydrated is to check the color and amount of urine. If it is very dark and there is little water, then the body needs a lot of air. If the urine is clear, it means the body is in normal air balance. The level of dehydration between each person is different, so we need a system that can classify the level of dehydration objectively through urine, so that it can facilitate the process of early detection and diagnosis before being enforced. Based on the research that has been done, it is found that the Matrix Laboratory (Matlab) can classify dehydration in the human body, by utilizing urine images through several processes, namely preprocessing, segmentation, feature extraction of Red Green Blue and then the K-Nearest Neighbor method to classify a person's level of dehydration. From 30 urine image training data and 15 urine image test data with 3 classes of urine color levels, namely not dehydrated, mild dehydration and severe dehydration, the results obtained from the accuracy of the dehydration level classification using the K-Nearest Neighbor method of 93.3% obtained from 14 test data with accurate classification, and 1 test data with inaccurate classification.

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Published

2022-01-25

Issue

Vol. 6 No. 1 (2022): Januari 2022

Section

Articles

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