Identifikasi Citra Penyakit Monkeypox dengan Random Forest Serta Ekstraksi Fitur VGG19: Indonesia

Muhammad Azka Zaki; Eka Prakarsa Mandyartha; Achmad Junaidi

doi:10.55606/jitek.v6i1.10132

Authors

Muhammad Azka Zaki Universitas Pembangunan Nasional "Veteran" Jawa Timur
Eka Prakarsa Mandyartha Universitas Pembangunan Nasional "Veteran" Jawa Timur
Achmad Junaidi Universitas Pembangunan Nasional "Veteran" Jawa Timur

DOI:

https://doi.org/10.55606/jitek.v6i1.10132

Keywords:

Monkeypox, Feature Extraction, VGG19, Classification, Random Forest

Abstract

Monkeypox is an infectious disease that can be recognized through images of the patient's skin lesions. A fast and accurate diagnosis method is required to identify Monkeypox. This research aims to identify Monkeypox imagery using the VGG19 feature extraction method, which is then classified using the Random Forest algorithm. The dataset consists of 770 original images, which were expanded to 5,860 images through geometric transformation augmentation. The test results show that the VGG19 feature extraction method with Random Forest classification achieved an accuracy of 95.1%, indicating good performance. This finding suggests the potential of this method as a machine learning approach for detecting Monkeypox and can be further developed with other artificial intelligence approaches.

References

[1] Dinas Kesehatan Provinsi Gorontalo, “WHO Tetapkan Monkeypox Darurat Kesehatan Global, Kadinkes dr. Yana : Tingkatkan Upaya Promotif dan Preventif.” Accessed: Jan. 24, 2025. [Online]. Available: https://dinkes.gorontaloprov.go.id/who-tetapkan-monkeypox-darurat-kesehatan-global-kadinkes-dr-yana-tingkatkan-upaya-promotif-dan-preventif/

[2] M. A. Thaariq1, M. Dimas, M. Baskara2, R. A. Chaniago3, D. Christin4, and I. Ernawati, “SYSTEMATIC LITERATURE REVIEW: ANALISIS PENERAPAN KECERDASAN BUATAN DALAM BIDANG KESEHATAN,” 2024.

[3] M. Altun, H. Gürüler, O. Özkaraca, F. Khan, J. Khan, and Y. Lee, “Monkeypox Detection Using CNN with Transfer Learning,” Sensors, vol. 23, no. 4, p. 1783, Feb. 2023, doi: 10.3390/s23041783.

[4] M. SHARMA and S. BENIWAL, “Feature Extraction Using Hybrid Approach of VGG19 and GLCM For Optimized Brain Tumor Classification,” International Journal of Computational and Experimental Science and Engineering, vol. 10, no. 4, Dec. 2024, doi: 10.22399/ijcesen.714.

[5] W. Wijaya, M. R. Pribadi, and E. P. Widiyanto, “2 ND MDP STUDENT CONFERENCE (MSC) 2023 KLASIFIKASI MONKEYPOX MENGGUNAKAN EKSTRAKSI FITUR GLCM DAN ALGORITMA RANDOM FOREST”.

[6] L. Budiyarto, A. A. Sabila, and H. C. Putri, “INFEKSI CACAR MONYET (MONKEYPOX).” [Online]. Available: http://jurnalmedikahutama.com

[7] N. Faoziatun Khusna et al., “Implementasi Random Forest dalam Klasifikasi Kasus Stunting pada Balita dengan Hyperparameter Tuning Grid Search,” Seminar Nasional Sains Data, vol. 2024.

[8] A. Fendiawati and M. E. Al Rivan, “Klasifikasi American Sign Language Dengan Metode VGG-19,” MDP Student Conference, vol. 2, no. 1, pp. 192–197, Apr. 2023, doi: 10.35957/mdp-sc.v2i1.4466.

[9] F. Ramadhan and J. Hernadi, “Evaluasi Optimizer Adam dan RMSProp pada Arsitektur VGG-19 Klasifikasi Ekspresi Wajah Manusia,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 10, no. 2, pp. 1414–1426, Mar. 2025, doi: 10.29100/jipi.v10i2.6197.

[10] RAHMAD SYUHADA, “ANALISIS ARSITEKTUR DEEP LEARNING VGG UNTUK KLASIFIKASI JENIS JAMUR,” 2023.

[11] A. M. Syafar, “IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI TANAMAN RIMPANG SECARA VIRTUAL DARMATASIA,” vol. 8, no. 1, 2023.

[12] A. Zafar et al., “A Comparison of Pooling Methods for Convolutional Neural Networks,” Applied Sciences, vol. 12, no. 17, p. 8643, Aug. 2022, doi: 10.3390/app12178643.