Klasifikasi Gender Berbasis Citra Wajah Menggunakan Clustering Dan Deep Learning
DOI:
https://doi.org/10.47065/bulletincsr.v5i4.581Keywords:
Gender Classification; Facial Image; Deep Learning; OpenCV; Clustering; Inception_v3; Agglomerative ClusteringAbstract
Gender classification based on facial images is a significant challenge in the field of computer vision, especially when dealing with unstructured data sourced from social media platforms. This study proposes an integrated approach combining facial image preprocessing, clustering methods, and deep learning to enhance the accuracy of gender classification. The dataset used was obtained from a Big Data Competition and consists of male and female face images sourced from Instagram. Preprocessing was performed using OpenCV for face detection and cropping. Subsequently, the data were clustered using K-Means and DBSCAN algorithms to reduce noise and redundancy. Gender classification was then conducted using a sequential learning model based on Inception_v3, enhanced with Agglomerative Clustering for feature refinement. The evaluation of the system demonstrated strong performance with an accuracy of 92.97%, F1-score of 0.89556, precision of 0.97727, and recall of 0.83069. These results confirm that the integration of clustering techniques and deep learning significantly improves the effectiveness of gender classification based on facial images, especially for open-source and non-curated datasets.
Downloads
References
S. Tilki, H. B. Dogru, A. A. Hameed, A. Jamil, and J. Rasheed, “Gender Classification using Deep Learning Techniques,” no. May, 2021.
M. U. Khan, M. Saad, S. Aziz, and J. M. Ch, “Electrocardiogram based Gender Classification,” no. January 2021, 2020, doi: 10.1109/ICECCE49384.2020.9179305.
H. Fauzi, C. Erika, S. Sa’adiah, and F. Oscandar, “Classification of Gender Individual Identification Using Local Binary Pattern on Palatine Rugae Image,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 8, no. 3, p. 422, 2022, doi: 10.26555/jiteki.v8i3.23636.
D. T. Adherda, M. Hikmatyar, and Ruuhwan, “Gender Classification Based on Voice Using Recurrent Neural Network (Rnn),” Antivirus?: Jurnal Ilmiah Teknik Informatika, vol. 17, no. 1, pp. 111–122, 2023, doi: 10.35457/antivirus.v17i1.3049.
D. I. Mulyana and V. V. Pramansah, “Gender Classification for Anime Character Face Image Using Random Forest Classifier Method and GLCM Feature Extraction,” JUITA?: Jurnal Informatika, vol. 10, no. 2, p. 243, 2022, doi: 10.30595/juita.v10i2.13833.
H. Salsabila, E. Rachmawati, and F. Sthevanie, “Klasifikasi Gender Berdasarkan Citra Wajah Menggunakan Metode Local Binary Pattern dan K-Nearest Neighbor,” vol. 8, no. 2, pp. 3137–3146, 2021.
R. Tamisier et al., “FACE Cluster phenotyping predicting outcomes in a prospective multicenter cohort study of chronic heart failure patients with central sleep disorder breathing indicated for adaptive servo ventilation,” Archives of Cardiovascular Diseases Supplements, vol. 12, no. 2–4, p. 246, Oct. 2020, doi: 10.1016/j.acvdsp.2020.03.112.
P. Rosyani, A. Suhendi, D. H. Apriyanti, and A. A. Waskita, “Color Features Based Flower Image Segmentation Using K-Means and Fuzzy C-Means,” Building of Informatics, Technology and Science (BITS), vol. 3, no. 3, pp. 253–259, 2021, doi: 10.47065/bits.v3i3.1060.
R. Adha, N. Nurhaliza, U. Sholeha, and M. Mustakim, “Perbandingan Algoritma DBSCAN dan K-Means Clustering untuk Pengelompokan Kasus Covid-19 di Dunia,” SITEKIN: Jurnal Sains, Teknologi dan Industri, vol. 18, no. 2, pp. 206–211, 2021.
A. Shahcheraghian, A. Ilinca, and N. Sommerfeldt, “K-means and agglomerative clustering for source-load mapping in distributed district heating planning,” Energy Conversion and Management: X, vol. 25, no. October 2024, p. 100860, 2025, doi: 10.1016/j.ecmx.2024.100860.
B. O. Siahaan, “Implementasi Pengenalan Wajah Untuk Absensi Karyawan Dengan Metode Eigenface,” Comasie, vol. 5, pp. 19–28, 2021.
A. Valentino, A. Rangga Sn, F. Wijoyo, I. J. Lestari, T. P. Andari, and P. Rosyani, “Studi literatur review: Alat Identifikasi Wajah untuk Absensi Mahasiswa Dengan Algoritma YOLO pada Flatform Android,” Logic?: Jurnal Ilmu Komputer dan Pendidikan, vol. 1, no. 2, pp. 233–238, 2023, [Online]. Available: https://journal.mediapublikasi.id/index.php/logic
P. Rosyani, S. Saprudin, and R. Amalia, “Klasifikasi Citra Menggunakan Metode Random Forest dan Sequential Minimal Optimization (SMO),” Jurnal Sistem dan Teknologi Informasi (Justin), vol. 9, no. 2, p. 132, 2021, doi: 10.26418/justin.v9i2.44120.
I. H. Ikasari, R. Amalia, and P. Rosyani, “Segmentasi Citra Bunga Menggunakan Blob Analisis,” Building of Informatics, Technology and Science (BITS), vol. 3, no. 3, pp. 228–234, 2021, doi: 10.47065/bits.v3i3.1050.
L. Wijayanti, H. Sachi, and S. Octaviani, “Klasifikasi Gender Berdasarkan Gambar Menggunakan Metode Deep Learning Pada MATLAB,” 2023.
H. Salsabila, E. Rachmawati, and F. Sthevanie, “Klasifikasi Gender Berdasarkan Citra Wajah Menggunakan Metode Local Binary Pattern dan K-Nearest Neighbor,” 2021.
D. Deng, “DBScan Clustering Algorithm Based on Density,” pp. 949–953, 2020, doi: 10.1109/IFEEA51475.2020.00199.
Perani Rosyani and Fabian Syawali, “Application of Advanced Class Determination System Using K-Means Clustering Method (Case Study: SMK Al-Badar Balaraja),” International Journal of Integrative Sciences, vol. 2, no. 10, pp. 1557–1570, 2023, doi: 10.55927/ijis.v2i10.6347.
D. Lakshmi and R. Ponnusamy, “Facial emotion recognition using modified HOG and LBP features with deep stacked autoencoders,” Microprocess Microsyst, vol. 82, no. January, p. 103834, 2021, doi: 10.1016/j.micpro.2021.103834.
C. H. Park, “A feature selection method using hierarchical clustering,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8284 LNAI, pp. 1–6, 2013, doi: 10.1007/978-3-319-03844-5_1.
C. Ding and X. He, “Cluster merging and splitting in hierarchical clustering algorithms,” Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 139–146, 2002, doi: 10.1109/icdm.2002.1183896.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Klasifikasi Gender Berbasis Citra Wajah Menggunakan Clustering Dan Deep Learning
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2025 Okky Prasetia, Syaeful Machfud, Perani Rosyani, Bobi Agustian
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
