<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Glucose monitoring carried out through the urine testing to make it easier for patients to check their blood sugar without having to physically injure themselves and to prevent external bacteria from entering the body, which happens while using needles. This study aims to classify glucose-containing urine specimens based on diabetes levels by using the K-nearest neighbor method. Classification of urine specimens is achieved by using the Benedict method to produce the color of the urine specimen and the AS7262 sensor to detect the color produced by the specimen. The results showed that the classification of data on urine specimens has an accuracy of 96.33%. Previous studies conducted this experiment using a photodiode sensor and a TCS sensor, which produced red, green, and blue (RGB) colors. For identifying the color of a specimen, the AS7262 sensor can produce six colors (red, green, blue, yellow, violet, and orange) to identify the glucose level.</p>
</abstract>

Glucose monitoring carried out through the urine testing to make it easier for patients to check their blood sugar without having to physically injure themselves and to prevent external bacteria from entering the body, which happens while using needles. This study aims to classify glucose-containing urine specimens based on diabetes levels by using the K-nearest neighbor method. Classification of urine specimens is achieved by using the Benedict method to produce the color of the urine specimen and the AS7262 sensor to detect the color produced by the specimen. The results showed that the classification of data on urine specimens has an accuracy of 96.33%. Previous studies conducted this experiment using a photodiode sensor and a TCS sensor, which produced red, green, and blue (RGB) colors. For identifying the color of a specimen, the AS7262 sensor can produce six colors (red, green, blue, yellow, violet, and orange) to identify the glucose level.

Identification of glucose levels in urine based on classification using <italic>k</italic>-nearest neighbor algorithm method

Department of Electrical Engineering, Faculty of Industrial Technology

Public Health Science Study Program, Faculty of Public Health

Department of Agro-industrial Technology, Faculty of Science and Technology

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Identification of glucose levels in urine based on classification using -nearest neighbor algorithm method

Glucose monitoring carried out through the urine testing to make it easier for patients to check their blood sugar without having to physically injure...

No.	Urine sample	Glucose level (%)	Glucose rate (g)	Color result	Urine positive rate (mg/dL)
1.	Sample 1	0–0.5	0	Slightly greenish blue and a bit cloudy	Normal
2.	Sample 2	0.5–1	0.2	Yellowish green	Positive 1
3.	Sample 3	1–1.5	0.3	Greenish yellow	Positive 2
4.	Sample 4	2–3.5	0.4	Slightly brownish orange	Positive 3
5.	Sample 5	>3.5	1	Slightly brownish brick red	Positive 4

Sensor color indicator						Class category

Violet	Blue	Green	Yellow	Orange	Red
21.34	23.36	18.68	19.64	14.02	6.66	Normal
14.93	13.53	24.27	21.48	19.23	6.53	Positive 1
12.19	10.07	22.63	24.14	21.87	9.05	Positive 2
11.12	8.21	16.97	21.91	26.02	15.74	Positive 3
13.68	9.93	22.58	20.77	22.58	10.43	Positive 4

Identification of glucose levels in urine based on classification using k-nearest neighbor algorithm method

Article Category: Article

Published Online: Aug 03, 2023

Page range: -

Received: Aug 15, 2022

DOI: https://doi.org/10.2478/ijssis-2023-0006

Keywords
Glucose, urine, AS7262, classification, K-nearest neighbor

© 2023 Anton Yudhana et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Figure 10:

Glucose characterization

Results of the data from the sensor

Identification of glucose levels in urine based on classification using k-nearest neighbor algorithm method

Article Category: Article

Published Online: Aug 03, 2023

Page range: -

Received: Aug 15, 2022

DOI: https://doi.org/10.2478/ijssis-2023-0006

KeywordsGlucose, urine, AS7262, classification, K-nearest neighbor

© 2023 Anton Yudhana et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Figure 10:

Glucose characterization

Results of the data from the sensor

Keywords
Glucose, urine, AS7262, classification, K-nearest neighbor