COMPARISON OF FACIAL IMAGE SEGMENTATION USING K-MEANS AND FUZZY C-MEANS CLUSTERING METHODS

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Fitri Nuraeni
Helfy Susilawati
Yoga Handoko Agustin

Abstract

Accuracy in face recognition is very important, so the process always begins with image segmentation. This segmentation is how the process of dividing the image into several objects, so that the object to be analyzed can be found. The easiest image segmentation is to use the clustering method. However, with so many clustering algorithms, it is necessary to know which algorithm can produce the best image segmentation for facial image datasets taken from employee attendance applications. This study uses an experimental method with image preparation stages, segmentation with k-means and fuzzy c-means algorithms, followed by evaluation using RSME, PSNR, and SSIM. The results of this study indicate that it can be said that for facial image segmentation taken from this employee attendance application, the segmentation of the clustering results with fuzzy c-means has the RMSE, PSNR, and visual effects values ​​needed for segmentation quality. on the image that is better than the image from the k-means segmentation.


 

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How to Cite
Nuraeni, F., Helfy Susilawati, & Yoga Handoko Agustin. (2022). COMPARISON OF FACIAL IMAGE SEGMENTATION USING K-MEANS AND FUZZY C-MEANS CLUSTERING METHODS. Jurnal Scientia, 11(02), 201-207. Retrieved from http://seaninstitute.org/infor/index.php/pendidikan/article/view/823

References

[1] N. S. Irjanto and R. Oktavia H, “Sistem Absensi Pegawai Dengan Pengenalan Wajah Employee Attendance System with Face Recognition,” J. Sisfotenika, vol. 12, no. 2, pp. 146–155, 2022.
[2] S. Sugeng and A. Mulyana, “Sistem Absensi Menggunakan Pengenalan Wajah (Face Recognition) Berbasis Web LAN,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 11, no. 1, pp. 127–135, 2022, doi: 10.32736/sisfokom.v11i1.1371.
[3] T. H. Andika and A. Hafiz, “Analisis Perbandingan Segmentasi Citra Menggunakan Metode K-Means dan Fuzzy C-Means,” Semin. Nas. Teknol. dan Bisnis 2018, pp. 237–246, 2018.
[4] J. Salat and S. Achmady, “Minimalisasi Distorsi Dari Segmentasi Citra Metode Otsu Menggunakan Fuzzy Clustering,” Ilk. J. Ilm., vol. 10, no. 1, pp. 80–85, 2018, doi: 10.33096/ilkom.v10i1.234.80-85.
[5] F. Nuraeni and L. Listiani, “Implementation of K-Means Algorithm with Distance of Euclidean Proximity in Clustering Cases of Violence Against Women and Children,” in 2019 1st International Conference on Cybernetics and Intelligent System (ICORIS), 2019, no. August, pp. 162–167.
[6] A. Premana, R. M. H. Bhakti, and D. Prayogi, “SEGEMENTASI K-MEANS CLUSTERING PADACITRA MENGGUNAKAN EKSTRAKSI FITUR WARNA DAN TEKSTUR,” J. Ilm. Intech Inf. Technol. J. UMUS, vol. 2, no. 1, pp. 89–97, 2020.
[7] A. Setiawan, Y. A. Sari, and B. Rahayudi, “Segmentasi Citra Makanan menggunakan Clustering Improved K-Means untuk Estimasi Sisa Makanan,” J. Pengemb. Teknol. …, vol. 5, no. 10, 2021, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/download/9969/4438.
[8] D. Arthur and S. Vassilvitskii, “How slow is the k-means method?,” in Proceedings of the twenty-second annual symposium on Computational geometry, 2006, pp. 144–153.
[9] M. Mellyadi and P. Harliana, “Segmentasi Citra Satelit dalam Observasi dan Konservasi Hutan Lindung Taman Nasional Gunung Lauser Menggunakan Algoritma Fuzzy C-Means,” Hello World J. Ilmu Komput., vol. 1, no. 2, pp. 90–96, 2022, doi: 10.56211/helloworld.v1i2.44.
[10] T. Herdian Andika, “Pengenalan Pola Berbasis Segmentasi Citra Menggunakan Algoritma Fuzzy C-Means Dan K-Means,” Aisyah J. Informatics Electr. Eng., vol. 1, no. 1, pp. 1–10, 2019, doi: 10.30604/jti.v1i1.3.
[11] A. Badruttamam, S. Sudarno, and D. A. I. Maruddani, “PENERAPAN ANALISIS KLASTER K-MODES DENGAN VALIDASI DAVIES BOULDIN INDEX DALAM MENENTUKAN KARAKTERISTIK KANAL YOUTUBE DI INDONESIA (Studi Kasus: 250 Kanal YouTube Indonesia Teratas Menurut Socialblade),” J. Gaussian, vol. 9, no. 3, pp. 263–272, 2020, doi: 10.14710/j.gauss.v9i3.28907.
[12] S. K. Uppada, “Centroid Based Clustering Algorithms- A Clarion Study,” Int. J. Comput. Sci. Inf. Technol., vol. 5, no. 6, pp. 7309–7313, 2014, [Online]. Available: https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.658.3904.
[13] A. Ramadhan, Mustakim, and R. Handinata, “Implementasi Algoritma Fuzzy C Means Dan Moora Untuk Pengelompokan Dan Penentuan Wilayah Penanggulangan Bencana Banjir,” no. November, p. Pekanbaru, 2019.
[14] G. I. W. Tamtama, “Perbandingan dan Analisis Untuk Algoritma Deteksi Tepi Pada Jaringan Saraf Tiruan,” CESS (Journal Comput. Eng. Syst. Sci., vol. 6, no. 1, p. 67, 2021, doi: 10.24114/cess.v6i1.19003.
[15] J. Gao, B. Wang, Z. Wang, Y. Wang, and F. Kong, “A wavelet transform-based image segmentation method,” Opt. - Int. J. Light Electron Opt., vol. 208, no. April, pp. 506–575, 2020, doi: 10.1515/9783110914252-043.
[16] M. S. Wibawa, “Studi Komparasi Metode Segmentasi Paru-Paru Pada Citra Ct-Scan Aksial,” J. Nas. Pendidik. Tek. Inform., vol. 7, no. 3, p. 283, 2019, doi: 10.23887/janapati.v7i3.15751.