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Optimizing urine protein detection accuracy using the K-nearest neighbors algorithm and advanced image segmentation techniques

Anton Yudhana

Anton Yudhana

Department of Electrical Engineering, Faculty of Industrial Technology, Universitas Ahmad DahlanYogyakarta, Indonesia
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Yudhana, Anton
, 
Novi Febrianti

Novi Febrianti

Department of Biology, Faculty of Applied Science and Technology, Universitas Ahmad DahlanYogyakarta, Indonesia
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Febrianti, Novi
, 
Ilham Mufandi

Ilham Mufandi

Department of Agro-Industrial Technology, Faculty of Science and Technology, Universitas Darussalam GontorIndonesia
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Mufandi, Ilham
, 
Arsyad Cahya Subrata

Arsyad Cahya Subrata

Department of Electrical Engineering, Faculty of Industrial Technology, Universitas Ahmad DahlanYogyakarta, Indonesia
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Subrata, Arsyad Cahya
, 
Nuni Ihsana

Nuni Ihsana

Faculty of Medicine, Universitas Ahmad DahlanYogyakarta, Indonesia
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Ihsana, Nuni
, 
Son Ali Akbar

Son Ali Akbar

Department of Electrical Engineering, Faculty of Industrial Technology, Universitas Ahmad DahlanYogyakarta, Indonesia
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Akbar, Son Ali
, 
Liya Yusrina Sabila

Liya Yusrina Sabila

Department of Electrical Engineering, Faculty of Industrial Technology, Universitas Ahmad DahlanYogyakarta, Indonesia
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Sabila, Liya Yusrina
, 
Helda Pratama

Helda Pratama

Department of Electrical Engineering, Faculty of Industrial Technology, Universitas Ahmad DahlanYogyakarta, Indonesia
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Pratama, Helda
, 
Nisa Fajriyanti

Nisa Fajriyanti

Department of Electrical Engineering, Faculty of Industrial Technology, Universitas Ahmad DahlanYogyakarta, Indonesia
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Fajriyanti, Nisa
, 
Sri Lestari

Sri Lestari

Rumah Sakit Umum Daerah (RSUD)Purworejo, Indonesia
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Lestari, Sri
 and 
Ismail Rakip Karas

Ismail Rakip Karas

Department of Computer Engineering, Demir Celik Campus, Karabuk UniversityKarabuk, Turkey
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Karas, Ismail Rakip
  
Jul 26, 2025
Optimizing urine protein detection accuracy using the K-nearest neighbors algorithm and advanced image segmentation techniques's Cover Image
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Article Category: Research Article
Published Online: Jul 26, 2025
Received: Sep 14, 2024
DOI: https://doi.org/10.2478/ijssis-2025-0039
Keywords
© 2025 Anton Yudhana et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Protein detection computer program. KNN, K-nearest neighbors.
Protein detection computer program. KNN, K-nearest neighbors.

Figure 2:

Evaluation of the KNN model. KNN, K-nearest neighbors.
Evaluation of the KNN model. KNN, K-nearest neighbors.

Figure 3:

(A) Image of the prototype seen from the outside, (B) prototype components seen from the inside, and (C) shape of the prototype that is ready to be used.
(A) Image of the prototype seen from the outside, (B) prototype components seen from the inside, and (C) shape of the prototype that is ready to be used.

Figure 4:

Distribution of 30 test data.
Distribution of 30 test data.

Figure 5:

Results of confusion matrix values at K = 3.
Results of confusion matrix values at K = 3.

Figure 6:

Results of confusion matrix values at K = 10.
Results of confusion matrix values at K = 10.

Figure 7:

Results of confusion matrix values at K = 20.
Results of confusion matrix values at K = 20.

Comparison of research results

No. Biomarker Author and year Color classification Work principle Ref.
1. Albumin Thakur (2021) RGB, HSV, and Lab RF algorithm to estimate albumin concentration using a smartphone [32]
2. Albumin Thakur (2022) RGB, HSV, and Lab CNN algorithm for classifying Color in detecting albumin using a smartphone. [41]
3. Albumin Kim (2022) RGB RGB extraction uses machine learning and iPhone 11 as a means of detecting color in urine. [42]
4. Protein This study (2023) RGB Protein detection equipped with a digital color sensor type ELP camera. Image data are classified based on RGB and evaluated using the KNN algorithm

Evaluation of the KNN model

K value Accuracy (%) Precision (%) Recall (%) F1 score (%)
3 96.7 97.0 96.7 96.2
10 86.7 75.8 86.7 80.7
20 76.7 60.9 76.7 67.3

Preparation of sample solutions

No. Protein (g) Water (mL) Output strip
1. 0.00 20 Negative (−)
2. 1.00 20 Plus-minus (+−)
3. 3.00 20 Positive 1 (+)
4. 5.00 20 Positive 2 (++)
5. 7.30 20 Positive 3 (+++)
6. 11.60 20 Positive 4 (++++)

Training and test data

Label/class/category Amount of data Information protein content (g/L)
− 6 0
+− 24 0.15
+ 10 0.3
++ 22 1
+++ 30 3
++++ 7 20
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