Diagnosis Kanker Paru-paru Berbasis Data Klinis: Evaluasi Performa Algoritma Pembelajaran Mesin

Riska Kurnia Septiani; Zulfati Dinul Fatiha; Dicky Octaviano

doi:10.22441/fifo.2026.v18i1.006

Authors

Riska Kurnia Septiani Fakultas Teknologi Informasi, Universitas Nusa Mandiri, Indonesia
Zulfati Dinul Fatiha Universitas Bina Sarana Informatika, Indonesia
Dicky Octaviano Universitas Bina Sarana Informatika, Indonesia

DOI:

https://doi.org/10.22441/fifo.2026.v18i1.006

Keywords:

Kanker Paru-Paru, Pembelajaran Mesin, Jaringan Syaraf Tiruan

Abstract

Kanker paru-paru adalah salah satu penyakit paling mematikan di dunia, menyebabkan lebih dari 1,76 juta kematian setiap tahunnya menurut WHO pada tahun 2020. Deteksi dini menjadi krusial dalam meningkatkan prognosis pasien, namun sering kali menantang karena gejala awal yang tidak spesifik. Dalam beberapa tahun terakhir, teknologi medis dan komputasi telah menghadirkan peluang baru dengan memanfaatkan pembelajaran mesin, terutama deep learning, untuk meningkatkan diagnosa kanker paru-paru. Studi ini mengevaluasi beberapa algoritma Machine Learning seperti K-Nearest Neighbors (k-NN), Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), dan Neural Network berdasarkan data klinis untuk memprediksi kanker paru-paru. Hasil evaluasi menunjukkan bahwa k-NN dan Neural Network memiliki performa terbaik dengan akurasi mencapai 0.92, sementara Naïve Bayes dan Neural Network menunjukkan presisi tertinggi untuk kelas kanker paru-paru (0.94). Logistic Regression dan SVM juga memberikan hasil yang baik, meskipun dengan variasi dalam presisi dan recall untuk kedua kelas. Penelitian ini memberikan wawasan penting untuk pengembangan sistem pendukung keputusan di bidang medis, dengan potensi untuk meningkatkan diagnosis dini, pengelolaan, dan prognosis kanker paru-paru secara efektif, serta mengurangi beban penyakit dan meningkatkan kualitas hidup pasien di masa depan. Implementasi pembelajaran mesin di sektor kesehatan menunjukkan bahwa teknologi ini dapat menjadi alat yang sangat berharga dalam mendeteksi dan mengelola penyakit serius seperti kanker paru-paru.

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References

J. F. Idris, R. Ramadhani, M. M. Mutoffar, S. Tinggi, T. Bandung, and K. Bandung, “Klasifikasi Penyakit Kanker Paru Menggunakan Perbandingan Algoritma Machine Learning,” J. Media Akad., vol. 2, no. 2, 2024.

World Health Organization, “Cancer Fact Sheet,” 2020.

L. Wang, “Deep Learning Techniques to Diagnose Lung Cancer,” Cancers (Basel)., vol. 14, no. 22, p. 5569, 2022, doi: 10.3390/cancers14225569.

S. N. Fadilah, D. C. R. Novitasari, and L. Hakim, “Pengaruh Reduksi Fitur Pada Klasifikasi Kanker Paru Menggunakan CNN Dengan Arsitektur GoogLeNet,” Jurnal Fourier, vol. 12, no. 1, pp. 20–32, 2023, doi: 10.14421/fourier.2023.121.20-32.

S. H. Hosseini, R. Monsefi, and S. Shadroo, “Deep Learning Applications for Lung Cancer Diagnosis: A Systematic Review,” Multimed. Tools Appl., vol. 83, pp. 1–45, 2024, doi: 10.1007/s11042-023-16046-w.

Y. Li, X. Wu, P. Yang, G. Jiang, and Y. Luo, “Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis,” Genomics Proteomics Bioinformatics, vol. 20, no. 5, pp. 850–866, 2022, doi: 10.1016/j.gpb.2022.11.003.

A. Esteva and others, “A Guide to Deep Learning in Healthcare,” Nat. Med., vol. 25, no. 1, pp. 24–29, 2019, doi: 10.1038/s41591-018-0316-z.

W. Shen et al., “Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification,” Pattern Recognition, vol. 61, pp. 663–673, 2017, doi: 10.1016/j.patcog.2016.05.029.

P. M. Shakeel, M. A. Burhanuddin, and M. I. Desa, “Automatic Lung Cancer Detection from CT Image Using Improved Deep Neural Network and Ensemble Classifier,” Neural Comput. Appl., vol. 33, pp. 281–299, 2021, doi: 10.1007/s00521-020-04842-6.

A. A. A. Setio et al., “Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge,” Medical Image Analysis, vol. 42, pp. 1–13, 2017, doi: 10.1016/j.media.2017.06.015.

A. Esteva et al., “A guide to deep learning in healthcare,” Nature Medicine, vol. 25, no. 1, pp. 24–29, 2019, doi: 10.1038/s41591-018-0316-z.

S. Florida, S. Hawkins, H. Wang, and A. Garcia, “Digital Commons @ University of Predicting Malignant Nodules from Screening CT Scans,” 2016.

M. Kanan and others, “AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis,” Cancers (Basel)., vol. 16, no. 3, p. 674, 2024, doi: 10.3390/cancers16030674.

N. S. Reddy and V. Khanaa, “Intelligent Deep Learning Algorithm for Lung Cancer Detection and Classification,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 3, pp. 1747–1754, 2023, doi: 10.11591/eei.v12i3.4579.

S. Huang, I. Arpaci, M. Al-Emran, and S. Klçarslan, “A Comparative Analysis of Classical Machine Learning and Deep Learning Techniques for Predicting Lung Cancer Survivability,” Multimed. Tools Appl., vol. 82, pp. 1–25, 2023, doi: 10.1007/s11042-023-16349-y.

A. C. Pacurari and others, “Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review,” Diagnostics, vol. 13, no. 13, p. 2145, 2023, doi: 10.3390/diagnostics13132145.

T. Riahi and others, “Lung Cancer Management: Revolutionizing Patient Outcomes Through Machine Learning and Artificial Intelligence,” Cancer Rep., vol. 8, no. 1, p. e70240, 2025, doi: 10.1002/cnr2.70240.

M. Geppert and others, “Software Using Artificial Intelligence for Nodule and Cancer Detection in CT Lung Cancer Screening: Systematic Review of Test Accuracy Studies,” Thorax, vol. 79, pp. 1040–1049, 2024, doi: 10.1136/thorax-2024-221662.

C. Pangestu, S. Shaufiah, and R. Wijaya, “X Spotify Cares Clustering Analysis using K-Means and K-Medoids,” Jurnal Media Informatika Budidarma, vol. 8, no. 1, pp. 497–507, 2024.

D. Marutho, “Perbandingan Metode Naïve Bayes, KNN, Decision Tree Pada Laporan Water Level Jakarta,” Jurnal Ilmiah Infokam, vol. 15, no. 2, pp. 90–97, 2019.

I. Firmansyah, J. T. Samudra, D. Pardede, and Z. Situmorang, “Komparasi Random Forest Dan Logistic Regression Dalam Klasifikasi Penderita Covid-19 Berdasarkan Gejalanya,” Journal of Science and Social Research, vol. 5, no. 3, p. 595, 2022, doi: 10.54314/jssr.v5i3.994.

Saifuddin, L. Azhari, E. Widarti, and Wartono, “Evaluasi Kinerja Kernel Linear, RBF, dan Polynomial pada Model Support Vector Machine untuk Prediksi Risiko Hipertensi,” J. Ilm. FIFO, vol. 17, no. 2, pp. 192–201, 2025, doi: 10.22441/fifo.2025.v17i2.008..

V. Y. P. Ardhana et al., “Prediksi Flight Delay Berbasis Algoritma Neural Network,” Journal of Informatics, Electrical and Electronics Engineering, vol. 2, no. 1, pp. 26–30, 2022, doi: 10.47065/jieee.v2i1.429.

N. S. Reddy and V. Khanaa, “Intelligent deep learning algorithm for lung cancer detection and classification,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 3, pp. 1747–1754, 2023, doi: 10.11591/eei.v12i3.4579.

S. P. Maurya, P. S. Sisodia, R. Mishra, and D. P. Singh, “Performance of Machine Learning Algorithms for Lung Cancer Prediction: A Comparative Approach,” Sci. Rep., vol. 14, no. 1, p. 18562, 2024, doi: 10.1038/s41598-024-58345-8.

T. Meeradevi and others, “Lung Cancer Detection with Machine Learning Classifiers with Multi-attribute Decision-making System and Deep Learning Model,” Sci. Rep., vol. 15, no. 1, p. 88188, 2025, doi: 10.1038/s41598-025-88188-w.