Klasifikasi Kanker Kulit menggunakan Convolutional Neural Network dengan Optimasi Arsitektur
Abstract
Skin cancer is a severe condition characterized by the abnormal growth of skin cells, often triggered by ultraviolet exposure and genetic factors. Early detection of skin cancer is essential for improving patient recovery rates, given the high incidence and significant impact of the disease. This study aims to develop a skin cancer classification system using the Convolutional Neural Network (CNN) method with the VGG-16 architecture, known for its effectiveness in medical image analysis. The CNN method was chosen because it can extract complex features from images. At the same time, the VGG-16 architecture was selected for its depth and ability to capture fine details in images—critical for distinguishing between types of skin cancer. The dataset was sourced from the ISIC platform and optimized through data augmentation techniques to address data imbalance issues. The research results indicate that while a basic CNN can provide good accuracy, implementing the VGG-16 architecture significantly increases accuracy. The basic CNN model achieved a training accuracy of 95.68% and a validation accuracy of 89.83%, whereas the CNN with VGG-16 reached a training accuracy of 96.21% and a validation accuracy of 90.89%. These findings suggest that combining CNN with VGG-16 effectively detects skin cancer, with VGG-16 providing a slight accuracy improvement, highlighting this architecture's potential as a more accurate tool to support skin cancer diagnosis.
Downloads
References
Sofia Saidah, I. P. Y. N. Suparta, and E. Suhartono, “Modifikasi Convolutional Neural Network Arsitektur GoogLeNet dengan Dull Razor Filtering untuk Klasifikasi Kanker Kulit,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 11, no. 2, pp. 148–153, May 2022, doi: 10.22146/jnteti.v11i2.2739.
R. R. Saputro, A. Junaidi, and W. A. Saputra, “Klasifikasi Penyakit Kanker Kulit Menggunakan Metode Convolutional Neural Network (Studi Kasus: Melanoma),” Journal of Dinda : Data Science, Information Technology, and Data Analytics, vol. 2, no. 1, pp. 52–57, Feb. 2022, doi: 10.20895/dinda.v2i1.349.
T. Saputra and M. E. Al-Rivan, “Analisis Performa ResNet-152 dan AlexNet dalam Klasifikasi Jenis Kanker Kulit,” STRING (Satuan Tulisan Riset dan Inovasi Teknologi), vol. 8, no. 1, p. 75, Aug. 2023, doi: 10.30998/string.v8i1.16464.
M. A. Kassem, K. M. Hosny, and M. M. Fouad, “Skin Lesions Classification Into Eight Classes for ISIC 2019 Using Deep Convolutional Neural Network and Transfer Learning,” IEEE Access, vol. 8, pp. 114822–114832, 2020, doi: 10.1109/ACCESS.2020.3003890.
P. Muhammad, A. A. Lestari, K. Adistri, R. S. Amalia, and L. P. Wibawa, “Kriteria ABCDE untuk Deteksi Dini Keganasan Kulit,” Cermin Dunia Kedokteran, vol. 49, no. 11, pp. 651–654, Nov. 2022, doi: 10.55175/cdk.v49i11.322.
D. A. Nurlitasari, R. Magdalena, and R. Y. N. Fu’adah, “ANALISIS PERFORMANSI SISTEM KLASIFIKASI KANKER KULIT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK,” JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING, vol. 5, no. 2, pp. 91–99, Feb. 2022, doi: 10.31289/jesce.v5i2.5691.
R. Yohannes and M. E. Al Rivan, “Klasifikasi Jenis Kanker Kulit Menggunakan CNN-SVM,” Jurnal Algoritme, vol. 2, no. 2, pp. 133–144, Apr. 2022, doi: 10.35957/algoritme.v2i2.2363.
F. Dartiko, R. J. Pradana, R. E. Sari, W. Syahputra, and W. K. Oktoeberza, “Klasifikasi Kanker Kulit Berbasis CNN dengan Metode Hybrid Preprocessing,” Medika Teknika : Jurnal Teknik Elektromedik Indonesia, vol. 5, no. 2, pp. 124–132, Apr. 2024, doi: 10.18196/mt.v5i2.22675.
M. M. Musthafa, M. T R, V. K. V, and S. Guluwadi, “Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification,” BMC Med Imaging, vol. 24, no. 1, p. 201, Aug. 2024, doi: 10.1186/s12880-024-01356-8.
L. Syafa’ah, I. Hanami, I. Rusdia Sofiani, and M. Chasrun, “Skin Lesion Image Classification using Convolutional Neural Network,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Nov. 2021, doi: 10.22219/kinetik.v6i4.1353.
R. AGUSTINA, R. MAGDALENA, and N. K. C. PRATIWI, “Klasifikasi Kanker Kulit menggunakan Metode Convolutional Neural Network dengan Arsitektur VGG-16,” ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 10, no. 2, p. 446, Apr. 2022, doi: 10.26760/elkomika.v10i2.446.
F. Findry and Rizal Adi Saputra, “Klasifikasi Kanker Kulit Berdasarkan Data Citra Benign Dan Malignant Menggunakan Convolutional Neural Network,” Jurnal Mahasiswa Teknik Informatika, vol. 3, no. 1, pp. 1–9, Apr. 2024, doi: 10.35473/jamastika.v3i1.2417.
D. A. Nurlitasari, R. Magdalena, and R. Y. N. Fu’adah, “ANALISIS PERFORMANSI SISTEM KLASIFIKASI KANKER KULIT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK,” JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING, vol. 5, no. 2, pp. 91–99, Feb. 2022, doi: 10.31289/jesce.v5i2.5691.
R. Yusuf, A. Rahman, F. Sthevanie, and G. Kosala, “Deteksi Kanker Kulit Melanoma Menggunakan Derivative of Gaussian dan Convolutional Neural Network,” Jurnal Penelitian Informatika, vol. 2, pp. 1–5, 2024, doi: 10.25124/logic.v2i1.7529.
I. R. Muslem and T. M. Johan, “KLIK: Kajian Ilmiah Informatika dan Komputer Klasifikasi Citra Ikan Menggunakan Algoritma Convolutional Neural Network dengan Arsitektur VGG-16,” Media Online), vol. 4, no. 2, pp. 978–985, 2023, doi: 10.30865/klik.v4i2.1209.
A. E. Putra, M. F. Naufal, and V. R. Prasetyo, “Klasifikasi Jenis Rempah Menggunakan Convolutional Neural Network dan Transfer Learning,” Jurnal Edukasi dan Penelitian Informatika (JEPIN), vol. 9, no. 1, p. 12, Apr. 2023, doi: 10.26418/jp.v9i1.58186.
D. A. Iskandar and A. Salam, “Evaluasi Performa Oversampling dan Augmentasi pada Klasifikasi Penyakit Kulit Menerapkan Convolutional Neural Network,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 8, no. 1, p. 240, Jan. 2024, doi: 10.30865/mib.v8i1.7119.
G. P. H. P. Gusti, E. Haerani, F. Syafria, F. Yanto, and S. K. Gusti, “Implementasi Algoritma Convolutional Neural Network (Resnet-50) untuk Klasifikasi Kanker Kulit Benign dan Malignant,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 3, pp. 984–992, Jun. 2024, doi: 10.57152/malcom.v4i3.1398.
A. Budhiman, S. Suyanto, and A. Arifianto, “Melanoma Cancer Classification Using ResNet with Data Augmentation,” in 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), IEEE, Dec. 2019, pp. 17–20. doi: 10.1109/ISRITI48646.2019.9034624.
M. Lestandy, “Deteksi Dini Kanker Payudara Menggunakan Metode Convolution Neural Network (CNN),” Inspiration: Jurnal Teknologi Informasi dan Komunikasi, vol. 12, no. 1, p. 65, Jun. 2022, doi: 10.35585/inspir.v12i1.2667.
M. F. Naufal, J. Siswantoro, and M. G. K. Wicaksono, “Klasifikasi Tulisan Tangan Pada Resep Obat Menggunakan Convolutional Neural Network,” Techno.Com, vol. 22, no. 2, pp. 508–526, May 2023, doi: 10.33633/tc.v22i2.8075.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Klasifikasi Kanker Kulit menggunakan Convolutional Neural Network dengan Optimasi Arsitektur
Pages: 1490−1498
Copyright (c) 2024 Jesica Trivena Sinaga, Haniifa Aliila Faudyta, Egia Rosi Subhiyakto

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).