Optimization of Decision Tree Algorithm for Chronic Kidney Disease Classification Based on Particle Swarm Optimization (PSO)
Abstract
The body's most important vital organ is the kidney. The kidneys are responsible for maintaining acid and alkaline balance, regulating blood pressure, and filtering blood to prevent the accumulation of metabolic waste in the body. However, chronic kidney disease does not always show symptoms and signs but can progress to kidney failure. Algorithm-based predictive methods in data processing show great potential in the health field to predict various diseases, one of which is kidney disease. One of the techniques in data mining is classification. One of the classification algorithms in data mining that is often used to detect diseases is Decision Tree. In this study, it is expected that by combining these two methods, it will make a new contribution to the Decision Tree algorithm that is optimized with Particle Swarm Optimization (PSO) for the selection of relevant features, and improve the weaknesses in the model to improve more accurate predictions. By performing feature selection with the Particle Swarm Optimization (PSO) algorithm, it is shown that the use of Particle Swarm Optimization (PSO) can improve the accuracy and performance of the Decision Tree algorithm in the chronic kidney disease classification process. The accuracy of the Decision Tree algorithm with feature selection using Particle Swarm Optimization (PSO) is higher, reaching 0.967%, compared to the accuracy of Decision Tree without Particle Swarm Optimization (PSO) feature selection which is only 0.95%. This shows that Particle Swarm Optimization (PSO) is effective in selecting relevant features so that it can significantly improve model performance.
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References
F. Yanto, M. I. Hatta, I. Afrianty, and L. Afriyanti, ‘Pengaruh Image Enhancement Contrast Stretching dalam Klasifikasi CT-Scan Tumor Ginjal menggunakan Deep Learning’, INOVTEK Polbeng - Seri Inform., vol. 9, no. 1, pp. 408–419, 2024, doi: 10.35314/isi.v9i1.4233.
F. Yanto, N. Jannata, L. Handayani, and E. P. Cynthia, ‘Pengaruh Contrast Limited Adaptive Histogram Equlization dalam Klasifikasi CT-Scan Tumor Ginjal menggunakan Deep Learning’, INOVTEK Polbeng - Seri Inform., vol. 9, no. 1, pp. 420–433, 2024, doi: 10.35314/isi.v9i1.4235.
C. P. Kovesdy, ‘Epidemiology of chronic kidney disease: an update 2022’, Kidney Int. Suppl., vol. 12, no. 1, pp. 7–11, 2022, doi: 10.1016/j.kisu.2021.11.003.
A. Tarisa Akbar, N. Yudistira, and A. Ridok, ‘Identifikasi Gagal Ginjal Kronis Dengan Mengimplementasikan Metode Support Vector Machine Beserta K-Nearest Neighbour (SVM-KNN)’, J. Teknol. Inf. dan Ilmu Komput., vol. 10, pp. 301–307, Apr. 2023, doi: 10.25126/jtiik.2023106059.
A. Fakhira, F. Insani, M. Irsyad, Y. Vitriani, and F. Kurnia, ‘Diagnosis Dini Penyakit Gagal Ginjal Dengan Metode Dempster’, INOVTEK Polbeng - Seri Inform., vol. 8, no. 2, p. 441, 2023, doi: 10.35314/isi.v8i2.3728.
V. Kyneissia Gliselda, ‘Diagnosis dan Manajemen Penyakit Ginjal Kronis (PGK)’, JMH J. Med. Hutama, Jul. 2021, [Online]. Available: http://jurnalmedikahutama.com
Imaniar Ikko Mulya Rizky, Suhendro Yusuf Irianto, and Sriyanto, ‘Perbandingan Kinerja Algoritma Naive Bayes, Support Vector Machine dan Random forest untuk Prediksi Penyakit Ginjal Kronis’, Semin. Nas. Has. Penelit. dan Pengabdi. Masy., pp. 139–151, Aug. 2023.
A. K. Hermawan and A. Nugroho, ‘Analisa Data Mining Untuk Prediksi Penyakit Ginjal Kronik Dengan Algoritma Regresi Linier’, Bull. Inf. Technol., vol. 4, no. 1, pp. 37–48, 2023, doi: 10.47065/bit.v3i1.
L. Ariyanti and A. Alamsyah, ‘C4.5 Algorithm Optimization and Support Vector Machine by Applying Particle Swarm Optimization for Chronic Kidney Disease Diagnosis’, Recursive J. Informatics, vol. 1, no. 1, pp. 18–26, Mar. 2023, doi: 10.15294/rji.v1i1.65196.
I. G. A. Mahardika Pratama, L. G. Astuti, I. M. Widiartha, I. G. N. A. Cahyadi Putra, C. R. Adi Pramartha, and I. D. M. B. Atmaja Darmawan, ‘Diagnosis Penyakit Ginjal Kronis dengan Algoritma C4.5, K-Means dan BPSO’, JELIKU (Jurnal Elektron. Ilmu Komput. Udayana), vol. 10, no. 4, p. 371, 2022, doi: 10.24843/jlk.2022.v10.i04.p07.
R. R. Adhitya, Wina Witanti, and Rezki Yuniarti, ‘Perbandingan Metode Cart Dan Naïve Bayes Untuk Klasifikasi Customer Churn’, INFOTECH J., vol. 9, no. 2, pp. 307–318, 2023, doi: 10.31949/infotech.v9i2.5641.
J. Elektronik, I. K. Udayana, J. H. Abednigo, A. Raharja, and K. Selatan, ‘Implementasi Decision Tree berbasis Forward Selection untuk Klasifikasi Penyakit Ginjal Kronis’, J. Elektron. Ilmu Komput. Udayana, vol. 12, pp. 277–286, Nov. 2023.
C. N. Syahputri and M. S. Hasibuan, ‘Optimasi Klasifikasi Decision Tree Dengan Teknik Pruning Untuk Mengurangi Overfitting’, JSiI | J. Sist. Inf., vol. 11, no. 2, pp. 87–96, 2024, doi: 10.30656/jsii.v11i2.9161.
T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh, and S. Mirjalili, ‘Particle Swarm Optimization: A Comprehensive Survey’, IEEE Access, vol. 10, no. January, pp. 10031–10061, 2022, doi: 10.1109/ACCESS.2022.3142859.
I. K. Ananda, A. Z. Fanani, D. Setiawan, and D. F. Wicaksono, ‘Penerapan Random Oversampling dan Algoritma Boosting untuk Memprediksi Kualitas Buah Jeruk’, Edumatic J. Pendidik. Inform., vol. 8, no. 1, pp. 282–289, 2024, doi: 10.29408/edumatic.v8i1.25836.
L. Rubini and P. P. E. Soundarapandian, ‘Dataset Penyakit Ginjal Kronis.pdf’, UC Irvine Machine Learning Repository. [Online]. Available: https://archive.ics.uci.edu/dataset/336/chronic+kidney+disease
P. M. S. Madani, T. Rohana, K. A. Baihaqi, and A. Fauzi, ‘Perbandingan Kinerja Klasifikasi Penyakit Ginjal Menggunakan Algoritma Support Vector Machine (SVM) dan Decision Tree (DT)’, Build. Informatics, Technol. Sci., vol. 6, no. 1, pp. 74−82-74−82, 2024, doi: 10.47065/bits.v6i1.5206.
W. Widiati, N. Iriadi, I. Ariyati, I. Nawawi, and S. Sugiono, ‘Pendekatan Hibrida Decision Tree-Particle Swarm Optimization untuk Deteksi Dini Penyakit Ginjal Kronis’, JASIEK (Jurnal Apl. Sains, Informasi, Elektron. dan Komputer), vol. 6, no. 1, pp. 11–22, 2024, doi: 10.26905/jasiek.v6i1.13006.
E. Purwaningsih and E. Nurelasari, ‘Peningkatan Akurasi Metode Support Vector Machine melalui Particle Swarm Optimization pada Penyakit Ginjal Kronis’, Inf. Manag. Educ. Prof., vol. 9, no. 1, pp. 61–70, 2024.
A. Rosyida and T. B. Sasongko, ‘Early Detection of Alzheimer’s Disease with the C4.5 Algorithm Based on BPSO (Binary Particle Swarm Optimization)’, J. Sisfokom (Sistem Inf. dan Komputer), vol. 12, no. 3, pp. 341–349, 2023, doi: 10.32736/sisfokom.v12i3.1716.
‘Eskiyaturrofikoh’ and R. R. ’Suryono, ‘Analisis Sentimen Aplikasi X Pada Google Play Store Menggunakan Algoritma Naïve Bayes Dan Support Vector Machine (Svm)’, JIPI(Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 9, no. 3, pp. 1408–1419, 2024, [Online]. Available: https://www.jurnal.stkippgritulungagung.ac.id/index.php/jipi/article/view/5392
P. B. N. Setio, D. R. S. Saputro, and Bowo Winarno, ‘Klasifikasi Dengan Pohon Keputusan Berbasis Algoritme C4.5’, Prism. Pros. Semin. Nas. Mat., vol. 3, pp. 64–71, 2020.
A. Pramudyantoro, E. Utami, and D. Ariatmanto, ‘Penggabungan K-Nearest Neighborsdan Lightgbm Untukprediksi Diabetes Pada Dataset Pima Indians: Menggunakanpendekatan Exploratory Data Analysis’, JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 9, no. 3, pp. 1133–1144, 2024, [Online]. Available: https://jurnal.stkippgritulungagung.ac.id/index.php/jipi/article/view/4966/2114
R. N. Ramadhon, A. Ogi, A. P. Agung, R. Putra, S. S. Febrihartina, and U. Firdaus, ‘Implementasi Algoritma Decision Tree untuk Klasifikasi Pelanggan Aktif atau Tidak Aktif pada Data Bank’, Karimah Tauhid, vol. 3, no. 2, pp. 1860–1874, 2024, doi: 10.30997/karimahtauhid.v3i2.11952.
Z. M. Ahmad Putra, P. Asri, F. Romadloni, and R. R. Arnestanta, ‘Penerapan Algoritma Particle Swarm Optimization Untuk Meningkatkan Efisiensi Daya Keluaran Panel Surya’, J. Tek. Elektro dan Komput. TRIAC, vol. 10, no. 2, pp. 56–64, 2023, doi: 10.21107/triac.v10i2.20717.
E. P. Saputra, S. Nurajizah, M. Maulidah, N. Hidayati, and T. Rahman, ‘Komparasi Machine Learning Berbasis Pso Untuk Prediksi Tingkat Keberhasilan Belajar Berbasis E-Learning’, J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 2, pp. 321–328, 2023, doi: 10.25126/jtiik.20231026469.
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