Implementasi Model Deep Learning MobileNetV2 untuk Klasifikasi Citra Melanoma Berbasis Web
-
Deva Safara Alfan
*
Universitas Pamulang, Tangerang Selatan,
Indonesia
- Intan Kumalasari Universitas Pamulang, Tangerang Selatan, Indonesia (*) Corresponding Author
Abstract
Melanoma is one of the most aggressive types of skin cancer with a high mortality rate if not detected at an early stage. In primary healthcare facilities, the lack of dermoscopy equipment causes examinations to rely solely on visual assessment, which may lead to diagnostic errors, particularly false negatives. This study aims to develop a web-based early melanoma detection system as a tool to assist initial screening. The proposed method implements a deep learning model based on the MobileNetV2 architecture using a transfer learning approach with pre-trained ImageNet weights. The dataset used in this study consists of melanoma and notmelanoma images from HAM10000, while the nonskin class is obtained from CIFAR-10 to help the model distinguish between skin lesion images and non-skin images. The dataset is divided into 70% training data, 20% validation data, and 10% testing data. Evaluation results show that the model achieves an accuracy of 90% in multiclass classification, while binary evaluation focusing on melanoma detection yields an accuracy of 90.48%, precision of 81.75%, recall of 91.96%, and an F1-score of 86.50% on the test data. The model is then implemented in a web-based system capable of displaying skin lesion classification results along with a confidence score in real time. The findings indicate that the developed system can perform automated image analysis and has the potential to be used as a supporting tool for early melanoma screening.
Downloads
References
E. Fitraneti, Y. Rizal, S. Riska Nafiah, I. Primawati, and D. Ayu Hamama, “Pengaruh Paparan Sinar Ultraviolet terhadap Kesehatan Kulit dan Upaya Pencegahannya : Tinjauan Literatur,” Scientific Journal, vol. 3, no. 3, pp. 185–194, 2024, doi: 10.56260/sciena.v3i3.147.
R. N. P. Fauziyyah, M. Komariah, and Y. K. Herliani, “Sunlight Exposure and Protection Behavior as Prevention of Skin Cancer in Nursing Students,” Indonesian Journal of Cancer, vol. 17, no. 1, p. 1, 2023, doi: 10.33371/ijoc.v17i1.921.
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, 2022, doi: 10.55175/cdk.v49i11.322.
I. N. G. Semarajana, “Faktor-Faktor Yang Berhubungan Dengan Tingginya Rujukan Kasus Non Spesialistik Oleh Fasilitas Kesehatan Tingkat Pertama Di Era Jaminan Kesehatan Nasional: Systematic Review,” Jurnal ARSI : Administrasi Rumah Sakit Indonesia, vol. 8, no. 3, 2022, doi: 10.7454/arsi.v8i3.3486.
M. M. H. Shandhi and J. P. Dunn, “AI in medicine: Where are we now and where are we going?,” Cell Rep Med, vol. 3, no. 12, p. 100861, 2022, doi: 10.1016/j.xcrm.2022.100861.
H. E. Kim, A. Cosa-Linan, N. Santhanam, M. Jannesari, M. E. Maros, and T. Ganslandt, “Transfer learning for medical image classification: a literature review,” BMC Med Imaging, vol. 22, no. 1, pp. 1–13, 2022, doi: 10.1186/s12880-022-00793-7.
Q. Zhu, H. Zhuang, M. Zhao, S. Xu, and R. Meng, “A study on expression recognition based on improved MobileNetV2 network,” Sci Rep, vol. 14, no. 1, pp. 1–11, 2024, doi: 10.1038/s41598-024-58736-x.
X. Lu and Y. A. F. A. Z. Zadeh, “Deep Learning-Based Classification for Melanoma Detection Using XceptionNet,” J Healthc Eng, vol. 2022, pp. 14–16, 2022, doi: 10.1155/2022/2196096.
R. Indraswari, R. Rokhana, and W. Herulambang, “Melanoma image classification based on MobileNetV2 network,” Procedia Comput Sci, vol. 197, pp. 198–207, 2021, doi: 10.1016/j.procs.2021.12.132.
R. O. Ogundokun et al., “Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models,” Bioengineering, vol. 10, no. 8, p. 979, 2023, doi: 10.3390/bioengineering10080979.
H. Nhut Huynh, M. Thanh Do, G. T. Huynh, A. T. Tran, and T. N. Tran, “Classification of Stages Diabetic Retinopathy Using MobileNetV2 Model Kalpa Publications in Engineering,” Kalpa Publications in Engineering, vol. 4, pp. 147–157, 2022.
M. Akay et al., “Deep Learning Classification of Systemic Sclerosis Skin Using the MobileNetV2 Model,” IEEE Open J Eng Med Biol, vol. 2, pp. 104–110, 2021, doi: 10.1109/OJEMB.2021.3066097.
S. Velu, “An efficient, lightweight MobileNetV2-based fine-tuned model for COVID-19 detection using chest X-ray images,” Mathematical Biosciences and Engineering, vol. 20, no. 5, pp. 8400–8427, 2023, doi: 10.3934/mbe.2023368.
O. Ozaltin and O. Yeniay, “Detection of Monkeypox Disease from Skin Lesion Images using MobileNetV2 Architecture,” Communications Faculty Of Science University of Ankara Series A1: Mathematics and Statistics, vol. 72, no. 2, pp. 482–499, 2023, doi: 10.31801/cfsuasmas.1202806.
W. Asmalinda, D. Setiawati, Jasmi, K. Khotimah, and E. Sapada, “DETEKSI DINI KANKER PAYUDARA MENGUNAKAN DETECTION OF BREAST CANCER USING BREAST SELF-,” Jurnal Abdikemas, vol. 4, no. 1, pp. 10–17, 2022.
S. Syefudin, M. N. Azmi, and G. Gunawan, “Analisis Pengaruh Dimensi Gambar Pada Klasifikasi Motif Batik Dengan Menggunakan Convolutional Neural Network,” Jurnal Sistem Informasi dan Informatika (Simika), vol. 6, no. 2, pp. 190–198, 2023, doi: 10.47080/simika.v6i2.2675.
V. Miftahuljannah and A. Suharso, “Pengimplementasian Berbagai Web Berdasarkan Kebutuhan Pengguna Dengan Menggunakan Metode Systematic Literature Review,” INFOTECH journal, vol. 9, no. 2, pp. 401–405, 2023, doi: 10.31949/infotech.v9i2.6341.
L. Setiyani and B. Setiawan, “Analisis Dan Design Manajemen Control Produksi Menggunakan Business Process Improvement Dan Unified Modelling Language (Studi Kasus: Pt. Multistrada),” Jurnal Interkom: Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi, vol. 16, no. 01, pp. 27–37, 2021.
Haryanto, “Analisis Big Data Dan Artificial Intelligence (AI): Dalam Industri Khususnya Prediksi Penyakit Jantung Dengan Phyton,” GO INFOTECH: JURNAL ILMIAH STMIK AUB, Vol. 30, No. 1, Pp. 76–86, 2024, Doi: 10.36309/Goi.V30i1.262.
E. Irawan, Moh. K. Huda, And R. Purwasih, “A Learning Trajectory For Developing Computational Thinking In Prospective Mathematics Teachers Through Python Programming In Google Colab,” Alifmatika: Jurnal Pendidikan Dan Pembelajaran Matematika, Vol. 7, No. 1, Pp. 34–52, 2025, Doi: 10.35316/Alifmatika.2025.V7i1.34-52.
S. Samrat. Medavarapu, “Advancements In Deep Learning: A Review Of Keras And Tensorflow Frameworks,” Journal Of Scientific And Engineering Research, Vol. 11, No. 5, Pp. 282–286, 2024.
M. Z. U. Rehman, F. Ahmed, S. A. Alsuhibany, S. S. Jamal, M. Z. Ali, And J. Ahmad, “Classification Of Skin Cancer Lesions Using Explainable Deep Learning,” Sensors, Vol. 22, No. 18, Pp. 1–14, 2022, Doi: 10.3390/S22186915.
S. K. E. Putri, F. H. Adiba, And A. K. Sari, “Implementasi Algoritma Cnn Dalam Pengenalan Wajah Menggunakan VGG16,” PROSIDING SEMINAR TEKNOLOGI DAN SAINS, Vol. 4, Pp. 618–623, 2025.
M. A. Rahman, M. B. A. Miah, M. A. Hossain, and A. S. M. S. Hosen, “Enhanced Brain Tumor Classification Using MobileNetV2: A Comprehensive Preprocessing and Fine-Tuning Approach,” BioMedInformatics, vol. 5, no. 2, pp. 1–33, 2025, doi: 10.3390/biomedinformatics5020030.
E. Miranda and M. Aryuni, “Klasifikasi Tutupan Lahan Menggunakan Convolutional Neural Network pada Citra Satelit Sentinel-2,” Jurnal Sistem Informasi, vol. 10, no. 2, pp. 323–335, 2021.
M. D. Gustinov et al., “Analysis of Web-Based E-Commerce Testing Using Black Box and White Box Methods,” International Journal of Information System and Innovation Management, vol. 1, no. 1, pp. 20–31, 2023.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi Model Deep Learning MobileNetV2 untuk Klasifikasi Citra Melanoma Berbasis Web
Pages: 670-680
Copyright (c) 2026 Deva Safara Alfan, Intan Kumalasari
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).
