Pemanfaatan Multi-Layer Perceptron (MLP) untuk Deteksi Kanker

Authors

  • Fahrul Firmansyah Informatika, UPN “Veteran” Jawa Timur, Jl.Raya Rungkut Madya, Gunung Anyar, Surabaya, Indonesia
  • Anggraini Puspita Sari Informatika, UPN “Veteran” Jawa Timur, Jl.Raya Rungkut Madya, Gunung Anyar, Surabaya, Indonesia
  • Sugiarto Sugiarto Informatika, UPN “Veteran” Jawa Timur, Jl.Raya Rungkut Madya, Gunung Anyar, Surabaya, Indonesia

DOI:

https://doi.org/10.26905/jasiek.v7i2.13438

Keywords:

Artificial Neural Networks, Artificial Intelligence, Cancer, Early Detection, Pattern Recognition

Abstract

Cancer is one of the deadliest diseases in the world. This is because patients often do not realize the presence of cancer in their bodies, leading to delayed treatment and the cancer becoming aggressive. Early diagnosis of cancer in women is necessary since the majority of cancer patients are women. One of the markers that can be used to diagnose cancer is the anti-Mullerian hormone, accompanied by other indicators such as lifestyle, BMI, and others. Early diagnosis can utilize the Multi-Layer Perceptron (MLP) algorithm, which is currently a rapidly developing technology. By using the MLP algorithm, an accuracy of 84% is achieved on the training data and test data, with a training-to-testing data ratio of 65:35.

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References

D. Crosby et al., “Early detection of cancer,” Science (1979), vol. 375, no. 6586, p. eaay9040, Feb. 2024, doi: 10.1126/science.aay9040.

G. Luo et al., “Projections of Lung Cancer Incidence by 2035 in 40 Countries Worldwide: Population-Based Study,” JMIR Public Health Surveill, vol. 9, p. e43651, 2023, doi: 10.2196/43651.

V. Di Donato, A. Giannini, and G. Bogani, “Recent Advances in Endometrial Cancer Management,” J Clin Med, vol. 12, no. 6, 2023, doi: 10.3390/jcm12062241.

M. Kokabi, M. N. Tahir, D. Singh, and M. Javanmard, “Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis,” Biosensors (Basel), vol. 13, no. 9, 2023, doi: 10.3390/bios13090884.

S. R. Oh, S. Y. Choe, and Y. J. Cho, “Clinical application of serum anti-Müllerian hormone in women,” Clin Exp Reprod Med, vol. 46, no. 2, pp. 50–59, Jun. 2019, doi: 10.5653/cerm.2019.46.2.50.

A. Kemal, “Dataset : Anti-Mullerian hormone levels in female cancer patients of reproductive age in Indonesia: A cross-sectional study,” Jan. 27, 2020.

S. R. Dubey, S. K. Singh, and B. B. Chaudhuri, “Activation functions in deep learning: A comprehensive survey and benchmark,” Neurocomputing, vol. 503, pp. 92–108, 2022, doi: https://doi.org/10.1016/j.neucom.2022.06.111.

J. Abellán-García, “Four-layer perceptron approach for strength prediction of UHPC,” Constr Build Mater, vol. 256, p. 119465, 2020, doi: https://doi.org/10.1016/j.conbuildmat. 2020.119465.

A. D. Rasamoelina, F. Adjailia, and P. Sinčák, “A Review of Activation Function for Artificial Neural Network,” in 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI), 2020, pp. 281–286. doi: 10.1109/SAMI48414.2020.9108717.

A. Sari, H. Suzuki, T. Kitajima, T. Yasuno, and D. Prasetya, “Prediction Model Of Wind Speed And Direction Using Deep Neural Network,” JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science), vol. 3, no. 1, pp. 1–10, 2020, doi: 10.26905/jeemecs.v3i1.3946.

P.-J. Chiang, “Adaptive penalty method with an Adam optimizer for enhanced convergence in optical waveguide mode solvers,” Opt. Express, vol. 31, no. 17, pp. 28065–28077, Aug. 2023, doi: 10.1364/OE.495855.

K. Namdar, M. A. Haider, and F. Khalvati, “A Modified AUC for Training Convolutional Neural Networks: Taking Confidence Into Account,” Front Artif Intell, vol. 4, Nov. 2021, doi: 10.3389/frai.2021.582928.

Wibisono Gunawan and Hermawan Arief, “Faktor-Faktor Penentu Gejala Penyakit Kanker Payudara Dengan Pendekatan Jaringan Saraf Tiruan,” JASIEK, vol. 1, pp. 1–6, Jun. 2019.

Y. Yu and Y. Zhang, “Multi-layer Perceptron Trainability Explained via Variability,” May 2021, [Online]. Available: http://arxiv.org/abs/2105.08911

Batlayeri T, Subairi S, Arifuddin R, Rianu BM. Exploring 3D Convolutional Neural Network Models for Alzheimer’s Disease Classification Based on 3D MRI Images. ENERGY: Jurnal Ilmiah Ilmu-Ilmu Teknik. 2025 Nov 30;15(2):213-26.

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Published

2025-12-11

How to Cite

[1]
F. Firmansyah, A. P. Sari, and S. Sugiarto, “Pemanfaatan Multi-Layer Perceptron (MLP) untuk Deteksi Kanker”, JASIEK, vol. 7, no. 2, pp. 127–137, Dec. 2025.