Acceso abierto

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

Anton Yudhana

Anton Yudhana

Department of Electrical Engineering, Faculty of Industrial Technology, Universitas Ahmad DahlanIndonesia
Buscar este autor en
Sciendo|Google Scholar
Yudhana, Anton
, 
Fathiyyah Warsino

Fathiyyah Warsino

Department of Electrical Engineering, Faculty of Industrial Technology, Universitas Ahmad DahlanIndonesia
Buscar este autor en
Sciendo|Google Scholar
Warsino, Fathiyyah
, 
Son Ali Akbar

Son Ali Akbar

Department of Electrical Engineering, Faculty of Industrial Technology, Universitas Ahmad DahlanIndonesia
Buscar este autor en
Sciendo|Google Scholar
Akbar, Son Ali
, 
Fatma Nuraisyah

Fatma Nuraisyah

Public Health Science Study Program, Faculty of Public Health, Universitas Ahmad DahlanIndonesia
Buscar este autor en
Sciendo|Google Scholar
Nuraisyah, Fatma
 y 
Ilham Mufandi

Ilham Mufandi

  • Department of Electrical Engineering, Faculty of Industrial Technology, Universitas Ahmad DahlanIndonesia
  • Department of Agro-industrial Technology, Faculty of Science and Technology, University of Darussalam GontorIndonesia
Buscar este autor en
Sciendo|Google Scholar
Mufandi, Ilham
  
03 ago 2023
Identification of glucose levels in urine based on classification using k-nearest neighbor algorithm method's Cover Image
Acerca de este artículo
Artículo anterior
Artículo siguiente
Cite
Descargar portada
Categoría del artículo: Article
Publicado en línea: 03 ago 2023
Recibido: 15 ago 2022
DOI: https://doi.org/10.2478/ijssis-2023-0006
Palabras clave
© 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:

Block diagram. PLN, State Electricity Company; I2C, Inter Integrated Circuit System.
Block diagram. PLN, State Electricity Company; I2C, Inter Integrated Circuit System.

Figure 2:

Wiring diagram.
Wiring diagram.

Figure 3:

Flowchart.
Flowchart.

Figure 4:

Machine learning flowchart.
Machine learning flowchart.

Figure 5:

K-nearest neighbor visualization.
K-nearest neighbor visualization.

Figure 6:

Prototype: (a) front view, (b) inside view, and (c) upper view.
Prototype: (a) front view, (b) inside view, and (c) upper view.

Figure 7:

Graphs of the data results: (a) Normal, (b) Positive 1, (c) Positive 2, (d) Positive 3, and (e) Positive 4.
Graphs of the data results: (a) Normal, (b) Positive 1, (c) Positive 2, (d) Positive 3, and (e) Positive 4.

Figure 8:

K-value graph.
K-value graph.

Figure 9:

KNN classification results. KNN, K-nearest neighbor.
KNN classification results. KNN, K-nearest neighbor.

Figure 10:

Confusion matrix.
Confusion matrix.

Glucose characterization

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

Results of the data from the sensor

Result of specimen color 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
Idioma:
Inglés
Calendario de la edición:
1 veces al año
Temas de la revista:
Ingeniería, Introducciones y reseñas, Ingeniería, otros
RSS Feed de revista