Klasifikasi Penyakit Kulit Berbasis Citra Menggunakan Algoritma Support Vector Machine
Kata Kunci:
klasifikasi penyakit kulit, Support Vector Machine, gambar digital, pembelajaran mesinAbstrak
Penyakit kulit merupakan permasalahan kesehatan global yang memerlukan penanganan diagnosis secara dini dan akurat. Keterbatasan jumlah tenaga ahli dermatologi di berbagai wilayah, khususnya daerah terpencil, mendorong perlunya sistem diagnosis otomatis berbasis kecerdasan buatan. Penelitian ini bertujuan mengembangkan model klasifikasi penyakit kulit berbasis citra menggunakan algoritma Support Vector Machine (SVM). Dataset yang digunakan berasal dari platform Kaggle, terdiri dari 3.020 citra yang dibagi ke dalam lima kategori: acne, eczema, impetigo, melanoma, dan normal skin. Proses praolah data mencakup konversi citra ke array piksel, normalisasi, encoding label, dan pembagian data dengan rasio 80:20. Pengujian dilakukan terhadap tiga jenis kernel SVM, yaitu linear, Radial Basis Function (RBF), dan polynomial. Hasil penelitian menunjukkan bahwa kernel RBF menghasilkan akurasi tertinggi sebesar 67% dengan F1-score 0,67, diikuti kernel linear sebesar 64%, dan polynomial sebesar 62%. Kategori melanoma dan normal skin menunjukkan performa terbaik dengan F1-score masing-masing 0,80 dan 0,76, sementara eczema dan impetigo masih menghadapi tantangan klasifikasi akibat kemiripan karakteristik visual. Penelitian ini menyimpulkan bahwa SVM berpotensi dimanfaatkan sebagai alat bantu klasifikasi awal penyakit kulit, dengan rekomendasi pengembangan melalui teknik augmentasi data, optimasi hiperparameter, dan eksplorasi metode ekstraksi fitur yang lebih canggih.
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[1] A. Rahma and M. E. Lane, “Skin Barrier Function in Infants: Update and Outlook,” Pharmaceutics, vol. 14, no. 2, p. 433, Feb. 2022, doi: 10.3390/pharmaceutics14020433.
[2] M. Fayyad-Kazan et al., “Effect of Ultraviolet Radiation and Benzo[a]pyrene Co-Exposure on Skin Biology: Autophagy as a Potential Target,” Int. J. Mol. Sci., vol. 24, no. 6, p. 5863, Mar. 2023, doi: 10.3390/ijms24065863.
[3] R. Agrawal, A. Hu, and W. B. Bollag, “The Skin and Inflamm-Aging,” Biology (Basel)., vol. 12, no. 11, p. 1396, Nov. 2023, doi: 10.3390/biology12111396.
[4] C. Karimkhani et al., “Global Skin Disease Morbidity and Mortality,” JAMA Dermatol., vol. 153, no. 5, p. 406, May 2017, doi: 10.1001/jamadermatol.2016.5538.
[5] C. Flohr and R. Hay, “Putting the burden of skin diseases on the global map,” British Journal of Dermatology, vol. 184, no. 2, pp. 189–190, Feb. 2021, doi: 10.1111/bjd.19704.
[6] F. J. Adella et al., “Teledermatology to Improve Access to and Quality of Skin Care in Eastern Indonesia,” Am. J. Trop. Med. Hyg., vol. 110, no. 2, pp. 364–369, Feb. 2024, doi: 10.4269/ajtmh.23-0218.
[7] A. Sharma and S. Gupta, “Skin Disease in Tropics: Impacted by Heat, Humidity, and Healthcare Neglect,” in Critical Thinking in Contemporary Dermatology: Cognitive Essays, Singapore: Springer Nature Singapore, 2024, pp. 229–250. doi: 10.1007/978-981-97-0411-8_16.
[8] Z. Li, K. C. Koban, T. L. Schenck, R. E. Giunta, Q. Li, and Y. Sun, “Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends,” J. Clin. Med., vol. 11, no. 22, p. 6826, Nov. 2022, doi: 10.3390/jcm11226826.
[9] O. Uwishema et al., “Diagnostic performance of artificial intelligence for dermatological conditions: a systematic review focused on low- and middle-income countries to address resource constraints and improve access to specialist care,” Int. J. Emerg. Med., vol. 18, no. 1, p. 172, Sep. 2025, doi: 10.1186/s12245-025-00975-4.
[10] R. Pangti et al., “A machine learning‐based, decision support, mobile phone application for diagnosis of common dermatological diseases,” Journal of the European Academy of Dermatology and Venereology, vol. 35, no. 2, pp. 536–545, Feb. 2021, doi: 10.1111/jdv.16967.
[11] M. Goyal, T. Knackstedt, S. Yan, and S. Hassanpour, “Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities,” Comput. Biol. Med., vol. 127, p. 104065, Dec. 2020, doi: 10.1016/j.compbiomed.2020.104065.
[12] I. Muslem, I. Irvanizam, A. Almuzammil, and F. Johar, “Adaptive Heuristic-Based Ant Colony Optimization for Multi-Constraint University Course Timetabling with Morning Slot Preference for Energy Efficiency,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 6, pp. 5930–5943, Jan. 2026, doi: 10.52436/1.jutif.2025.6.6.5588.
[13] I. Muslem R, M. K. M. Nasution, S. Sutarman, and S. Suherman, “A scalable model for Capacitated Vehicle Routing Problem with Pickup and Delivery under dynamic constraints using adaptive heuristic-based ant colony optimization,” Eastern-European Journal of Enterprise Technologies, vol. 1, no. 3 (133), pp. 57–65, Feb. 2025, doi: 10.15587/1729-4061.2025.319733.
[14] I. Muslem and D. Armiady, “A PSO-Based CVRPPD Model with Weather and Traffic Constraints for Two-Wheeled Urban Delivery in Indonesia,” Instal: Jurnal Komputer, vol. 17, pp. 281–290, 2025, doi: 10.54209/jurnalinstall.v17i05.388.
[15] D. Armiady and I. M. R, “Klasifikasi Kualitas Buah Pisang Berdasarkan Citra Buah Menggunakan Stochastic Gradient Descent,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 2, 2023.
[16] R. Melani, S. Sriwinar, D. Azmi, I. Muslem, and M. Amin, “Klasifikasi Kematangan Cabai Berdasarkan Citra Digital Menggunakan Algoritma K-Nearest Neighbor,” NOVAKOMPUTA: Jurnal Ilmu Komputer dan Informatika, vol. 1, no. 1, pp. 30–42, 2026.
[17] D. Fahmi, D. Azmi, I. Muslem, and P. B. N. Simangunsong, “Sistem Pendeteksi Dan Klasifikasi Jenis Hewan Ternak Menggunakan Metode You Only Look Once (YOLO),” NOVAKOMPUTA: Jurnal Ilmu Komputer dan Informatika, vol. 1, no. 1, pp. 43–51, 2026.
[18] N. Rismayanti and S. F. Azzahrah, “Classification of Skin Diseases using Decision Tree Algorithm on an Imbalanced Dataset,” International Journal of Artificial Intelligence in Medical Issues, vol. 2, no. 2, pp. 83–94, 2024.
[19] B. Kalpana, A. K. Reshmy, S. S. Pandi, and S. Dhanasekaran, “OESV-KRF: Optimal ensemble support vector kernel random forest based early detection and classification of skin diseases,” Biomed. Signal Process. Control, vol. 85, p. 104779, 2023.
[20] M. A. Araaf, K. Nugroho, and D. R. I. M. Setiadi, “Comprehensive analysis and classification of skin diseases based on image texture features using K-nearest neighbors algorithm,” Journal of Computing Theories and Applications, vol. 1, no. 1, pp. 31–40, 2023.
[21] L. Luthfi, R. Imam Muslem, D. Armiady, S. Sriwinar, R. Fajri, and I. Iqbal, “Analysis of CNN Method for Image Classification of Coconut Ripeness Levels,” in 2023 Eighth International Conference on Informatics and Computing (ICIC), IEEE, Dec. 2023, pp. 1–6. doi: 10.1109/ICIC60109.2023.10381964.
[22] T. Shanthi, R. S. Sabeenian, and R. Anand, “Automatic diagnosis of skin diseases using convolution neural network,” Microprocess. Microsyst., vol. 76, p. 103074, 2020.
[23] D. N. Raju, H. Shanmugasundaram, and R. Sasikumar, “Fuzzy segmentation and black widow–based optimal SVM for skin disease classification,” Med. Biol. Eng. Comput., vol. 59, no. 10, pp. 2019–2035, 2021.
[24] B. M. Rashed and N. Popescu, “Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning Techniques,” Computation, vol. 11, no. 3, p. 63, Mar. 2023, doi: 10.3390/computation11030063.
[25] Z. Amanda, I. Muslem, and F. Rizani, “Klasifikasi Kematangan Buah Pepaya Menggunakan Algoritma Support Vector Machine,” Jurnal Ilmu Komputer Aceh, vol. 3, no. 1, pp. 96–102, Feb. 2026.
[26] B. Laoli, I. Muslem, and F. Rizani, “ Klasifikasi Tingkat Kematangan Buah Pisang Menggunakan Algoritma Support Vector Machine ,” Jurnal Ilmu Komputer Aceh, vol. 3, no. 1, pp. 142–151, Feb. 2026.
[27] I. R. Muslem, “KLIK: Kajian Ilmiah Informatika dan Komputer Image Classification pada Kasus American Sign Language Menggunakan Support Vector Machine,” Media Online), vol. 4, no. 2, pp. 1184–1191, 2023, doi: 10.30865/klik.v4i2.1242.
[28] F. E. Khoirunisa and N. Charibaldi, “Effect of Using GLCM and LBP+ HOG Feature Extraction on SVM Method in Classification of Human Skin Disease Type,” Inform: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi, vol. 9, no. 2, pp. 145–150, 2024.
[29] Y. Wu and Y. Fang, “Stroke prediction with machine learning methods among older Chinese,” Int. J. Environ. Res. Public Health, vol. 17, no. 6, p. 1828, 2020.
[30] A. K. Leung, B. Barankin, J. M. Lam, K. F. Leong, and K. L. Hon, “Dermatology: how to manage acne vulgaris,” Drugs Context, vol. 10, pp. 1–18, Oct. 2021, doi: 10.7573/dic.2021-8-6.
[31] M. Metz, U. Wahn, U. Gieler, P. Stock, J. Schmitt, and U. Blume‐Peytavi, “Chronic pruritus associated with dermatologic disease in infancy and childhood: Update from an interdisciplinary group of dermatologists and pediatricians,” Pediatric Allergy and Immunology, vol. 24, no. 6, pp. 527–539, Sep. 2013, doi: 10.1111/pai.12115.
[32] S. Wiegele et al., “The epidemiology of superficial Streptococcal A (impetigo and pharyngitis) infections in Australia: A systematic review,” PLoS One, vol. 18, no. 11, p. e0288016, Nov. 2023, doi: 10.1371/journal.pone.0288016.
[33] G. Bessa, “Bacterial Infections,” in Dermatology in Public Health Environments, Cham: Springer International Publishing, 2018, pp. 157–172. doi: 10.1007/978-3-319-33919-1_9.
[34] B. Bandarchi, C. A. Jabbari, A. Vedadi, and R. Navab, “Molecular biology of normal melanocytes and melanoma cells,” J. Clin. Pathol., vol. 66, no. 8, pp. 644–648, Aug. 2013, doi: 10.1136/jclinpath-2013-201471.
[35] N. Rismayanti and S. F. Azzahrah, “Classification of Skin Diseases using Decision Tree Algorithm on an Imbalanced Dataset,” International Journal of Artificial Intelligence in Medical Issues, vol. 2, no. 2, pp. 83–94, 2024.
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Hak Cipta (c) 2026 Dedek Intan Afriana, Sriwinar, Imam Muslem
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