Analisis Kinerja Recursive Feature Elimination pada Support Vector Machine untuk Klasifikasi Penyakit Stroke pada Data Tidak Seimbang
DOI:
https://doi.org/10.47065/bulletincsr.v6i4.1147Keywords:
ADASYN; Imbalanced Data; RFE; Stroke; SVMAbstract
Stroke is a non-communicable disease with high mortality and disability rates, necessitating a classification approach that can facilitate more effective detection. Class imbalance in stroke datasets causes classification models to be biased toward the majority class, resulting in suboptimal classification performance. This study aims to analyze the performance of Recursive Feature Elimination (RFE) in a Support Vector Machine (SVM) model with data imbalance handling using Adaptive Synthetic Sampling (ADASYN) in stroke classification. The dataset used is a secondary dataset from Kaggle consisting of 5109 data points after the preprocessing stage. The modeling process was conducted by testing various data split ratios as well as combinations of kernels and SVM parameters using a 5-fold cross-validation approach. The results show that the best model was obtained with an 80:20 split ratio, a polynomial kernel, and a C parameter of 0.1, yielding an accuracy of 0.75, precision of 0.14, recall of 0.82, an F1-score of 0.24, and an AUC of 0.8245. The application of RFE resulted in improved model performance compared to without RFE, although the magnitude of the improvement was relatively small. The still low precision value indicates that the model still produces many false positives, so the classification challenge on the stroke dataset has not been fully resolved. On the other hand, an AUC value of 0.8245 indicates that the model performs reasonably well in distinguishing between the two classes overall, although its application in a clinical context still requires further refinement.
Downloads
References
World Health Organization, Integrating stroke services in health-care systems?: A practical approach. 2020. [Online]. Available: https://apps.who.int/iris/handle/10665/336231
V. L. Feigin et al., “World Stroke Organization?: Global Stroke Fact Sheet 2025,” Int. J. Stroke, vol. 20, no. 2, pp. 132–144, 2025, doi: 10.1177/17474930241308142.
Badan Penelitian dan Pengembangan Kesehatan, “Laporan Nasional Riskesdas 2018,” 2019, Jakarta. [Online]. Available: https://repository.kemkes.go.id/book/1323
Badan Kebijakan Pembangunan Kesehatan, “Survei Kesehatan Indonesia (SKI) 2023 Dalam Angka,” Jakarta, 2023. [Online]. Available: https://www.badankebijakan.kemkes.go.id/ski-2023-dalam-angka/
E. Wiyarta, R. Hidayat, M. Kurniawan, G. H. P. Sinaga, and R. A. Budiman, “Factors associated with prehospital and in-hospital delays in acute ischaemic stroke care in Indonesia: a systematic review,” BMJ Open, vol. 15, no. 3, 2025, doi: 10.1136/bmjopen-2024-095845.
T. Vu, Y. Kokubo, M. Inoue, M. Yamamoto, and A. Mohsen, “Machine Learning Approaches for Stroke Risk Prediction?: Findings from the Suita Study,” J. Cardiovasc. Dev. Dis., 2024, doi: doi.org/10.3390/jcdd11070207.
Alwaliyanto, S. K. Gusti, I. Afrianty, and F. Syafria, “Penerapan Metode ADASYN Dalam Mengatasi Imbalanced Data Untuk Klasifikasi Penyakit Stroke Menggunakan Support Vector Machine,” Bull. Comput. Sci. Res., vol. 5, no. 4, pp. 532–541, 2025, doi: 10.47065/bulletincsr.v5i4.612.
M. S. Hamdard and H. Lodin, “Effect of Feature Selection on the Accuracy of Machine Learning Model,” Int. J. Multidiscip. Res. Anal., vol. 06, no. 09, pp. 4460–4466, 2023, doi: 10.47191/ijmra/v6-i9-66.
I. Guyon, “An Introduction to Variable and Feature Selection 1 Introduction,” J. Mach. Learn. Res., vol. 3, pp. 1157–1182, 2003.
E. S. Septiany, H. H. Handayani, T. Al Mudzakir, A. Fitri, and N. Masruriyah, “Optimasi Metode Support Vector Machine Menggunakan Seleksi Fitur Recursive Feature Elimination dan Forward Selection untuk Klasifikasi Kanker Payudara TIN?: Terapan Informatika Nusantara,” TIN Terap. Inform. Nusant., vol. 5, no. 2, pp. 144–154, 2024, doi: 10.47065/tin.v5i2.5324.
H. Sundari, M. A. Amrustian, A. Dwi, and P. Wicaksono, “Penerapan Recursive Feature Elimination pada Support Vector Machine untuk Klasifikasi Kanker Payudara,” LEDGER J. Inform. Inf. Technol., vol. 3, no. 2, pp. 60–65, 2024, doi: 10.20895/ledger.v3i2.1591.
N. D. M. Zulkifli and M. A. As’ari, “Optimized Supervised Learning Framework for Gestational Diabetes Mellitus ( GDM ) Detection based on Recursive Feature Elimination ( RFE ),” Malaysian J. Fundam. Appl. Sci., vol. 21, pp. 2967–2976, 2025, doi: doi.org/10.11113/mjfas.v21n6.4462.
W. Hidayat, T. A. Y. Siswa, and R. Hasudungan, “Penerapan Algoritma Naive Bayes , Recursive Feature Elimination , dan Adaptive Synthetic Sampling Pada Klasifikasi Penyakit Dermatitis,” J. INSTEK, vol. 10, no. 2, pp. 424–436, 2025, doi: https://doi.org/10.24252/instek.v10i2.59737.
L. B. E. Susilo, T. A. Y. Siswa, and N. A. Verdikha, “Penerapan Algoritma Decision Tree, Recursive Elimination Dan ADASYN Pada Data Klasifikasi Penyakit Dermatitis,” JATI (Jurnal Mhs. Tek. Inform., vol. 9, no. 5, pp. 8791–8798, 2025, doi: 10.36040/jati.v9i5.15212.
H. Hamla, K. Ghanem, and M. Constantine, “A Hybrid Feature Selection Based on Fisher Score and SVM-RFE for Microarray Data,” Informatica, vol. 48, pp. 57–68, 2024, doi: 10.31449/inf.v48i1.4759.
H. He, Y. Bai, E. A. Garcia, and S. Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” Proc. Int. Jt. Conf. Neural Networks, no. 3, pp. 1322–1328, 2008, doi: 10.1109/IJCNN.2008.4633969.
N. G. Ramadhan, “Comparative Analysis of ADASYN-SVM and SMOTE-SVM Methods on the Detection of Type 2 Diabetes Mellitus,” Sci. J. Informatics, vol. 8, no. 2, pp. 276–282, 2021, doi: 10.15294/sji.v8i2.32484.
H. N. Jasim, W. Mohammed Jasim, and M. S. Ibrahim, “Optimized Machine Learning Approach for Breast Cancer Detection Using ADASYN with Feature Selection,” in 2025 5th International Conference on Emerging Smart Technologies and Applications (eSmarTA), 2025, pp. 1–7. doi: 10.1109/eSmarTA66764.2025.11132282.
C. Wulandari, I. Afrianty, E. Budianita, and S. K. Gusti, “Thyroid Disease Classification Using Support Vector Machine and Recursive Feature Elimination Method,” bit-Tech (Binary Digit. - Technol., vol. 8, no. 2, 2025, doi: 10.32877/bt.v8i2.3454.
U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “The KDD Process for Extracting Useful Knowledge from Volumes of Data,” Commun. ACM, vol. 39, no. 11, pp. 27–34, 1996, doi: 10.1145/240455.240464.
J. Allgaier and R. Pryss, “Practical approaches in evaluating validation and biases of machine learning applied to mobile health studies,” Commun. Med., pp. 1–11, 2024, doi: 10.1038/s43856-024-00468-0.
Wijiyanto, A. I. Pradana, Sopingi, and V. Atina, “Teknik K-Fold Cross Validation untuk Mengevaluasi Kinerja Mahasiswa,” J. Algoritm. Inst. Teknol. Garut, pp. 239–248, 2024, doi: 10.33364/algoritma/v.21-1.1618.
M. Tiara et al., “Pemanfaatan Algoritma ADASYN Dan Support Vector Machine Dalam Meningkatkan Akurasi Prediksi Kanker Paru-Paru,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 5, pp. 8773–8778, 2024, doi: 10.36040/jati.v8i5.10752.
A. M. Priyatno, T. Widiyaningtyas, I. Engineering, and U. N. Malang, “A Systematic Literatur Review: Recursive Feature Elimination Algorithms,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 9, no. 2, pp. 196–207, 2024, doi: 10.33480/jitk.v9i2.5015.
N. Sulistianingsih, S. A. A. Yusuf, A. Anggreni, F. N. Sari, M. P. Juniarta, and J. Permatasari, “Analisis Pengaruh Recursive Feature Elimination Terhadap Kinerja Model Prediksi Dini Diabetes Mellitus di RS PKU Muhammadiyah Bima,” JTIM J. Teknol. Inf. dan Multimed., vol. 7, no. 3, pp. 548–560, 2025, doi: 10.35746/jtim.v7i3.774.
M. A. Muslim et al., Data Mining Algoritma C4.5, Pertama. Semarang, 2019.
A. S. Nugroho, A. B. Witarto, and D. Handoko, “Support Vector Machine,” Proceeding Indones. Sci. Meeiting Cent. Japan, 2003, doi: 10.18411/trnio-12-2023-769.
D. R. Nurqotimah, A. N. Khudori, and R. S. Pradini, “Implementasi Algoritma Support Vector Machine ( SVM ) Untuk Klasifikasi Penyakit Stroke,” J. Appl. Comput. Sci. Technol. ( JACOST ), vol. 5, no. 2, pp. 179–185, 2024, doi: 10.52158/jacost.v5i2.817.
H. N. Jasim, W. M. Jasim, and M. S. Ibrahim, “Optimized Machine Learning Approach for Breast Cancer Detection Using ADASYN with Feature Selection,” pp. 1–7, 2025, doi: 10.1109/eSmarTA66764.2025.11132282.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Kinerja Recursive Feature Elimination pada Support Vector Machine untuk Klasifikasi Penyakit Stroke pada Data Tidak Seimbang
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2026 Faridatul Jannah, Siska Kurnia Gusti, Elin Haerani, Teddie Darmizal
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).
