Algoritma Decision Tree untuk Prediksi Deteksi Penyakit Kanker Payudara
DOI:
https://doi.org/10.14421/jiska.2024.9.1.70-78Keywords:
Breast Cancer, Classification, Prediction, Decision Tree, Machine LearningAbstract
Cancer is a deadly disease that is difficult to cure. Early cancer detection can be done through laboratory tests to identify the cancer type. Breast cancer is a type of cancer with initial symptoms in the form of a lump. Data mining and classification methods, such as decision trees with ID3 and C5.0 algorithms, are used to categorize breast cancer. The dataset used is Breast Cancer Coimbra, which was downloaded from UCI Machine Learning in 2018. ID3 has limitations in handling unstructured data and continuous attributes, while C5.0 is better. Both algorithms produce tree models with different levels of accuracy. This study shows that the C5.0 algorithm has the best classification results with 80% accuracy, 84.2% precision, 80% recall, and 80% F1 score. 80% accuracy shows the system's classification ability, so the C5.0 model can be used to predict breast cancer.
References
Agarwal, S. (2013). Data Mining: Data Mining Concepts and Techniques. 2013 International Conference on Machine Intelligence and Research Advancement, 203–207. https://doi.org/10.1109/ICMIRA.2013.45
Grandini, M., Bagli, E., & Visani, G. (2020). Metrics for Multi-Class Classification: an Overview. http://arxiv.org/abs/2008.05756
Mayadewi, P., & Rosely, E. (2015). Prediksi Nilai Proyek Akhir Mahasiswa Menggunakan Algoritma Klasifikasi Data Mining. Seminar Nasional Sistem Informasi Indonesia (SESINDO) 2015, 2015. https://is.its.ac.id/pubs/oajis/index.php/home/detail/1582/PREDIKSI-NILAI-PROYEK-AKHIR-MAHASISWA-MENGGUNAKAN-ALGORITMA-KLASIFIKASI-DATA-MINING
Musa, A. A., & Aliyu, U. M. (2020). Application of Machine Learning Techniques in Predicting of Breast Cancer Metastases Using Decision Tree Algorithm, in Sokoto Northwestern Nigeria. Journal of Data Mining in Genomics & Proteomics, 11(1). https://www.walshmedicalmedia.com/open-access/application-of-machine-learning-techniques-in-predicting-of-breast-cancer-metastases-using-decision-tree-algorithm-in-sokoto-north-53078.html
Patrcio, M., Pereira, J., Crisstomo, J., Matafome, P., Seia, R., & Caramelo, F. (2018). Breast Cancer Coimbra. UCI Machine Learning Repository. https://doi.org/https://doi.org/10.24432/C52P59
Pribadi, D., Athiry, S., Saputra, R. A., Supiandi, A., Prayudi, D., Nusa, S., & Sukabumi, M. (2018). Sistem Pakar Diagnosa Penyakit Demam Berdarah Dengue Menggunakan Algoritma Iterative Dichotomiser 3 (ID3). SNIT 2018, 1(1), 129–133. https://seminar.bsi.ac.id/snit/index.php/snit-2018/article/view/37
Sunjana. (2010). Aplikasi Mining Data Mahasiswa dengan Metode Klasifikasi Decision Tree. Seminar Nasional Aplikasi Teknologi Informasi (SNATI), 1907–5022. https://journal.uii.ac.id/Snati/article/view/1857
Wahyudin. (2009). Metode Iterative Dichotomizer 3 ( ID3 ) Untuk Penerimaan Mahasiswa Baru. Universitas Pendidikan Indonesia.
Wei, W. (2011). ID3 Algorithm and C4.5 Algorithm Based on Decision Tree. Journal of Hubei University of Technology.
Zhang, D., Wang, J., & Zhao, X. (2015). Estimating the Uncertainty of Average F1 Scores. Proceedings of the 2015 International Conference on The Theory of Information Retrieval, 317–320. https://doi.org/10.1145/2808194.2809488
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Ayu Dian Fitri Mellina, Suhartono Suhartono , M. Ainul Yaqin
![Creative Commons License](http://i.creativecommons.org/l/by-nc/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms as stated in http://creativecommons.org/licenses/by-nc/4.0
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
b. 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 acknowledgement of its initial publication in this journal.
c. 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.