New Approach K-Medoids Clustering Based on Chebyshev Distance with Quantum Computing for Anemia Prediction
DOI:
https://doi.org/10.30812/matrik.v25i1.4180Keywords:
Data Mining, Clustering, Chebyshev Distance, K-Medoids, Quantum Computing, Quantum BitAbstract
Anemia is a condition where the number of red blood cells or hemoglobin levels is below normal, reducing the blood’s ability to carry oxygen, which can lead to symptoms such as fatigue, weakness, and shortness of breath.This study aims to utilize a quantum computing approach to improve the performance of the K-Medoids method by calculating the Chebyshev Distance to predict anemia. The method used is the K-Medoids clustering method with the calculation of the Chebyshev Distance and quantum computing. A comparative analysis of these methods is carried out with a focus on their performance, especially the accuracy of the test results. This study was conducted using a dataset of medical records of patients with anemia. The dataset was taken from Kaggle. This dataset includes five attributes used to predict anemia disease patterns. The dataset was tested using the classical method and K-Medoids with a quantum computing approach that utilizes the Chebyshev Distance calculation. The results of this study reveal a new alternative model for the K-Medoids algorithm with the Chebyshev Distance calculation influenced by the integration of the quantum computing framework. Specifically, the simulation test results show the same accuracy as the classical K-Medoids method and the K-Medoids method with a quantum computing approach with Chebyshev Distance calculations with an accuracy of 80%. The conclusion of this study highlights that the performance of the K-Medoids method with a quantum computing approach with Chebyshev Distance calculations can be implemented to predict anemia using the clustering method.
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References
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[2] M. Saberi-Karimian et al., “Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements,†J. Clin. Lab. Anal., vol. 37, no. 1, pp. 1–10, 2023, doi: 10.1002/jcla.24798.
[3] D. Priyanto, B. K. Triwijoyo, D. Jollyta, H. Hairani, N. Gusti, and A. Dasriani, “Data Mining Earthquake Prediction with Multivariate Adaptive Regression Splines and Peak Ground Acceleration,†vol. 22, no. 3, pp. 583–592, 2023, doi: 10.30812/matrik.v22i3.3061.
[4] Y. Liu et al., “Simple Contrastive Graph Clustering,†IEEE Trans. Neural Networks Learn. Syst., pp. 1–10, 2023, doi: 10.1109/TNNLS.2023.3271871.
[5] J. B. Muñoz, J. Mirocha, S. Furlanetto, and N. Sabti, “Breaking degeneracies in the first galaxies with clustering,†Mon. Not. R. Astron. Soc. Lett., vol. 526, no. 1, pp. L47–L55, 2023, doi: 10.1093/mnrasl/slad115.
[6] M. Faisal, E. M. Zamzami, and Sutarman, “Comparative Analysis of Inter-Centroid K-Means Performance using Euclidean Distance, Canberra Distance and Manhattan Distance,†J. Phys. Conf. Ser., vol. 1566, no. 1, 2020, doi: 10.1088/1742-6596/1566/1/012112.
[7] C. Oktarina, K. A. Notodiputro, and I. Indahwati, “Comparison of K-Means Clustering Method and K-Medoids on Twitter Data,†Indones. J. Stat. Its Appl., vol. 4, no. 1, pp. 189–202, 2020, doi: 10.29244/ijsa.v4i1.599.
[8] S. Al-Otaibi, V. Cherappa, T. Thangarajan, R. Shanmugam, P. Ananth, and S. Arulswamy, “Hybrid K-Medoids with Energy-Efficient Sunflower Optimization Algorithm for Wireless Sensor Networks,†Sustain., vol. 15, no. 7, pp. 1–16, 2023, doi: 10.3390/su15075759.
[9] S. Informasi, T. Informatika, U. Bina, S. Informatika, J. Kramat, and R. No, “Optimization of the Number of Clusters on K-Medoids Using Chebychev and Manhattan on Gold Selling Grouping,†vol. 5, no. 36, pp. 2128–2136, 2021.
[10] H. Zhao et al., “Analysis of Euclidean Distance and Manhattan Distance in the K-Means Algorithm for Variations Number of Centroid K Analysis of Euclidean Distance and Manhattan Distance in the K-Means Algorithm for Variations Number of Centroid K,†2020, doi: 10.1088/1742-6596/1566/1/012058.
[11] T. Smets et al., “Evaluation of Distance Metrics and Spatial Autocorrelation in Uniform Manifold Approximation and Projection Applied to Mass Spectrometry Imaging Data,†Anal. Chem., vol. 91, no. 9, pp. 5706–5714, 2019, doi: 10.1021/acs.analchem.8b05827.
[12] M. A. Fusihan and U. Ghoni, “Perbandingan Metode Euclidean Distance, Manhattan Distance, Chebyshev Distance Untuk Menentukan Jarak Terpendek Spbu Di Brebes Selatan,†J. Tek. Inform. dan Sist. Inf., vol. 3, no. 2, pp. 53–59, 2023.
[13] S. Suraya, M. Sholeh, and D. Andayati, “Comparison of distance metric in k-mean algorithm for clustering wheat grain datasheet,†J. Tek. Inform. C.I.T Medicom, vol. 15, no. 2, pp. 73–83, 2023, doi: 10.35335/cit.vol15.2023.408.pp73-83.
[14] D. P. Sari, S. Ridmadhanti, R. Erda, N. J. Margiyanti, T. Y. Handayani, and R. A. Tarigan, “Deteksi Dini Anemia pada Remaja di Pulau Nguan Kecamatan Galang Kota Batam Tahun 2020,†J. Pelayanan dan Pengabdi. Masy., vol. 4, no. 1, pp. 1–8, 2020, doi: 10.52643/pamas.v4i1.767.
[15] R. Buaton and S. Solikhun, “The Application of Numerical Measure Variations in K-Means Clustering for Grouping Data,†MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 23, no. 1, pp. 103–112, 2023, doi: 10.30812/matrik.v23i1.3269.
[16] D. Hastari, F. Nurunnisa, S. Winanda, and D. Dwi Aprillia, “Penerapan Algoritma K-Means dan K-Medoids untuk MengelompokkanData Negara Berdasarkan Faktor Sosial-Ekonomi dan Kesehatan,†SENTIMAS Semin. Nas. Penelit. dan Pengabdi. Masy., pp. 274–281, 2023, [Online]. Available: https://journal.irpi.or.id/index.php/sentimas
[17] I. M. Karo Karo, S. Dewi, M. Mardiana, F. Ramadhani, and P. Harliana, “K-Means and K-Medoids Algorithm Comparison for Clustering Forest Fire Location in Indonesia,†J. Ecotipe (Electronic, Control. Telecommun. Information, Power Eng., vol. 10, no. 1, pp. 86–94, 2023, doi: 10.33019/jurnalecotipe.v10i1.3896.
[18] A. Jauhari, D. R. Anamisa, and F. A. Mufarroha, “Analysis of Clusters Number Effect Based on K-Means Method for Tourist Attractions Segmentation,†J. Phys. Conf. Ser., vol. 2406, no. 1, 2022, doi: 10.1088/1742-6596/2406/1/012024.
[19] G. P. I.R, A. Aziz, and M. P. T.S, “Implementasi Euclidean Dan Chebyshev Distance Pada K-Medoids Clustering,†JATI (Jurnal Mhs. Tek. Inform., vol. 6, no. 2, pp. 710–715, 2022, doi: 10.36040/jati.v6i2.5443.
[20] D. Ayu, “Pengklasteran Puskesmas di Kabupaten Kudus Menggunakan Metode K-Means dengan Perbandingan Jarak Euclidean dan Chebyshev,†vol. 5, pp. 787–798, 2022.
[21] R. Nooraeni and G. Nurfalah, “Kajian Penerapan Jarak Euclidean, Manhattan, Minkowski, dan Chebyshev pada Algoritma Clustering K-Prototype,†Sains, Apl. Komputasi dan Teknol. Inf., vol. 4, no. 2, pp. 72–82, 2022, doi: 10.30872/jsakti.v4i2.9241.
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2025-11-21
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Copyright (c) 2025 Mochamad Wahyudi, Solikhun Solikhun, Lise Pujiastuti, Gerhard-Wilhelm Weber
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[1]
M. Wahyudi, S. Solikhun, L. Pujiastuti, and G.-W. . Weber, “New Approach K-Medoids Clustering Based on Chebyshev Distance with Quantum Computing for Anemia Prediction”, MATRIK, vol. 25, no. 1, pp. 15–24, Nov. 2025, doi: 10.30812/matrik.v25i1.4180.
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