Implementasi Algoritma Klasifikasi Dengan Teknik Discretization Dan Bagging Untuk Meningkatkan Akurasi Prediksi Penyakit Stroke

Authors

DOI:

https://doi.org/10.32493/informatika.v8i2.30550

Keywords:

Computer Science

Abstract

Penyakit stroke merupakan salah satu penyakit penyebab kematian namun  dapat dikurangi jumlah angka kematiannya apabila terdapat diagnosa sejak secara dini untuk memprediksi penyakit stroke yang akurat. Penelitian yang terkait prediksi penyakit stroke telah dilakukan dengan beberapa metode namun menghasilkan tingkat akurasi yang kurang maksimal pada algoritma klasifikasi, sehingga diperlukan adanya upaya peningkatan akurasi untuk menghasilkan informasi yang akurat dalam menprediksi penyakit stroke. Tujuan dari penelitian ini yaitu melakukan implementasi algoritma klasifikasi dengan menerapkan teknik discretization dan Bagging dalam meningkatkan predikasi penyakit stroke. Hasil yang diperoleh dari penelitian ini adalah algoritma klasifikasi Naïve Bayes dengan menggabungkan teknik discretization dan Bagging memiliki tingkat akurasi lebih tinggi dengan akurasi sebesar 95.21%, meningkat sebesar 7.36% dari pada hanya menggunakan algoritma tunggal saja.

 Keywords: Penyakit Stroke, Algoritma Klasifikasi, Discretization, Bagging

References

Ahmed, H., Abd-el, S. F., Youn, E. M. G., & Omran, N. F. (2019). Stroke Prediction using Distributed Machine Learning Based on Apache Spark Stroke Prediction using Distributed Machine Learning Based on Apache Spark. January. https://doi.org/10.13140/RG.2.2.13478.68162

Alasadi, S. A., & Bhaya, W. S. (2017). Review of data preprocessing techniques in data mining. Journal of Engineering and Applied Sciences, 12(16), 4102–4107. https://doi.org/10.3923/jeasci.2017.4102.4107

Anggi Priliani Yulianto, & Darwis, S. (2021). Penerapan Metode K-Nearest Neighbors (kNN) pada Bearing. Jurnal Riset Statistika, 1(1), 10–18. https://doi.org/10.29313/jrs.v1i1.16

Bhatia, S., & Malhotra, J. (2021). Naïve bayes classifier for predicting the novel coronavirus. Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, Icicv, 880–883. https://doi.org/10.1109/ICICV50876.2021.9388410

Biswas, N., Uddin, K. M. M., Rikta, S. T., & Dey, S. K. (2022). A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach. Healthcare Analytics, 2(August), 100116. https://doi.org/10.1016/j.health.2022.100116

Buntoro, G. A. (2017). Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter. 2(1), 32–41.

Charbuty, B., & Abdulazeez, A. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 2(01), 20–28. https://doi.org/10.38094/jastt20165

Derisma, D. (2020). Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining. Journal of Applied Informatics and Computing, 4(1), 84–88. https://doi.org/10.30871/jaic.v4i1.2152

Dev, S., Wang, H., Shamrock, C., Jain, N., Veeravalli, B., & John, D. (2022). A predictive analytics approach for stroke prediction using machine learning and neural networks. Healthcare Analytics, 2(December 2021), 100032. https://doi.org/10.1016/j.health.2022.100032

García-Gil, D., Ramírez-Gallego, S., García, S., & Herrera, F. (2018). Principal Components Analysis Random Discretization Ensemble for Big Data. Knowledge-Based Systems, 150, 166–174. https://doi.org/10.1016/j.knosys.2018.03.012

Hacibeyoglu, M., & Ibrahim, M. H. (2018). EF_Unique: An Improved Version of Unsupervised Equal Frequency Discretization Method. Arabian Journal for Science and Engineering, 43(12), 7695–7704. https://doi.org/10.1007/s13369-018-3144-z

Kabari, L. G., & Onwuka, U. C. (2019). Comparison of Bagging and Voting Ensemble Machine Learning Algorithm as a Classifier. International Journals of Advanced Research in Computer Science and Software Engineering, 9(3), 19–23. www.ijarcsse.com,

Kemenkes RI. (2018). Hasil Riset Kesehatan Dasar Tahun 2018. Kementrian Kesehatan RI, 53(9), 1689–1699.

Lee, S. J., Xu, Z., Li, T., & Yang, Y. (2018). A novel bagging C4.5 algorithm based on wrapper feature selection for supporting wise clinical decision making. Journal of Biomedical Informatics, 78(October 2017), 144–155. https://doi.org/10.1016/j.jbi.2017.11.005

Moshkov, M. (2022). Decision trees for regular factorial languages. Array, 15(January), 100203. https://doi.org/10.1016/j.array.2022.100203

Osman, A. S. (2019). Data mining techniques: Review. International Journal of Data Science Research, 2(1), 1–4.

Panhalkar, A. R., & Doye, D. D. (2022). Optimization of decision trees using modified African buffalo algorithm. Journal of King Saud University - Computer and Information Sciences, 34(8), 4763–4772. https://doi.org/10.1016/j.jksuci.2021.01.011

Peker, N., & Kubat, C. (2021). Application of Chi-square discretization algorithms to ensemble classification methods. Expert Systems with Applications, 185(June 2020), 115540. https://doi.org/10.1016/j.eswa.2021.115540

Riyanto, U. (2019). Analisis Perbandingan Algoritma Naive Bayes Dan Support Vector Machine Dalam Mengklasifikasikan Jumlah Pembaca Artikel Online. JIKA (Jurnal Informatika), 2(2), 62–72. https://doi.org/10.31000/.v2i2.1521

Saleh, A., & Nasari, F. (2018). Implementation Equal-Width Interval Discretization in Naive Bayes Method for Increasing Accuracy of Students’ Majors Prediction. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, September, 104. https://doi.org/10.24843/lkjiti.2018.v09.i02.p05

Salmi, N., & Rustam, Z. (2019). Naïve Bayes Classifier Models for Predicting the Colon Cancer. IOP Conference Series: Materials Science and Engineering, 546(5). https://doi.org/10.1088/1757-899X/546/5/052068

Srinivas, B., & Sasibhushana Rao, G. (2019). A hybrid CNN-KNN model for MRI brain tumor classification. International Journal of Recent Technology and Engineering, 8(2), 5230–5235. https://doi.org/10.35940/ijrte.B1051.078219

Sudarsono, B. G., Leo, M. I., Santoso, A., & Hendrawan, F. (2021). Analisis Data Mining Data Netflix Menggunakan Aplikasi Rapid Miner. JBASE - Journal of Business and Audit Information Systems, 4(1), 13–21. https://doi.org/10.30813/jbase.v4i1.2729

Tuysuzoglu, G., & Birant, D. (2020). Enhanced bagging (eBagging): A novel approach for ensemble learning. International Arab Journal of Information Technology, 17(4), 515–528. https://doi.org/10.34028/iajit/17/4/10

Virantika, E., Kusnawi, K., & Ipmawati, J. (2022). Evaluasi Hasil Pengujian Tingkat Clusterisasi Penerapan Metode K-Means Dalam Menentukan Tingkat Penyebaran Covid-19 di Indonesia. Jurnal Media Informatika Budidarma, 6(3), 1657. https://doi.org/10.30865/mib.v6i3.4325

Widaningsih, S. (2019). Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve Bayes, Knn Dan Svm. Jurnal Tekno Insentif, 13(1), 16–25. https://doi.org/10.36787/jti.v13i1.78

Wu, Y., Ke, Y., Chen, Z., Liang, S., Zhao, H., & Hong, H. (2020). Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping. Catena, 187(February 2018), 104396. https://doi.org/10.1016/j.catena.2019.104396

Xing, W., & Bei, Y. (2020). Medical Health Big Data Classification Based on KNN Classification Algorithm. IEEE Access, 8, 28808–28819. https://doi.org/10.1109/ACCESS.2019.2955754

Yang, F. J. (2018). An implementation of naive bayes classifier. Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018, 301–306. https://doi.org/10.1109/CSCI46756.2018.00065

Jurnal Informatika Universitas Pamulang Vol. 8 No. 2 Juni 2023

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2023-06-30

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Vol. 8 No. 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG

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