Pendekatan Hibrida Decision Tree-Particle Swarm Optimization untuk Deteksi Dini Penyakit Ginjal Kronis
DOI:
https://doi.org/10.26905/jasiek.v6i1.13006Keywords:
Decision Tree, Particle Swarm Optimization, Pendekatan Hibrida, Penyakit Ginjal KronisAbstract
Penelitian ini mengusulkan pendekatan hibrida yang menggabungkan Decision Tree (DT) dengan Particle Swarm Optimization (PSO) untuk deteksi dini Penyakit Ginjal Kronis (PGK). Kami mengidentifikasi permasalahan dalam akurasi yang kurang maksimal dalam prediksi PGK dengan menggunakan metode DT padahal metode DT dapat ditingkatkan, sehingga kami mengusulkan solusi dengan mengintegrasikan kejelasan interpretasi DT dan kemampuan optimasi PSO. Melalui analisis data klinis CKD, kami menunjukkan peningkatan signifikan dalam akurasi dan AUC dari 98.25% menjadi 98.50% dan 0.931 menjadi 0.984, masing-masing. Evaluasi menunjukkan peningkatan presisi dari 98.04% menjadi 98.71%. Pendekatan hibrida DT+PSO menawarkan kemungkinan aplikasi praktis dalam manajemen dan prognosis PGK, serta unggul dalam akurasi prediksi dan interpretasi model. Temuan ini memiliki implikasi penting dalam pemahaman dan penanganan PGK secara dini.References
I. M. D. Priyatama and Ridwansyah, “Klasifikasi Anak Berkebutuhan Khusus Tunagrahita Menggunakan Metode Algoritma C4.5,†Paradigma, vol. 24, no. 1, pp. 90–95, 2022, doi: https://doi.org/10.31294/paradigma.v24i1.1087.
A. Hamid and Ridwansyah, “Optimizing Heart Failure Detection : A Comparison between Naive Bayes and Particle Swarm Optimization,†Paradigma, vol. 26, no. 1, pp. 30–36, 2024, doi: https://doi.org/10.31294/p.v26i1.3284.
S. B. Akben, “Early Stage Chronic Kidney Disease Diagnosis by Applying Data Mining Methods to Urinalysis, Blood Analysis and Disease History,†Irbm, vol. 39, no. 5, pp. 353–358, 2019, doi: 10.1016/j.irbm.2018.09.004.
S. Zeynu, “Prediction of Chronic Kidney Disease Using Data Mining Feature Selection and Ensemble Method,†WSEAS Trans. Inf. Sci. Appl., vol. 15, pp. 168–176, 2019, [Online]. Available: https://www.wseas.org/multimedia/journals/information/2018/a405909-911.php.
E. H. A. Rady and A. S. Anwar, “Prediction of kidney disease stages using data mining algorithms,†Informatics Med. Unlocked, vol. 15, no. December 2019, p. 100178, 2019, doi: 10.1016/j.imu.2019.100178.
Arif-Ul-Islam and S. H. Ripon, “Rule Induction and Prediction of Chronic Kidney Disease Using Boosting Classifiers, Ant-Miner and J48 Decision Tree,†2nd Int. Conf. Electr. Comput. Commun. Eng. ECCE 2019, pp. 1–6, 2019, doi: 10.1109/ECACE.2019.8679388.
A. Alaiad, H. Najadat, B. Mohsen, and K. Balhaf, “Classification and Association Rule Mining Technique for Predicting Chronic Kidney Disease,†J. Inf. Knowl. Manag., vol. 19, no. 1, 2020, doi: 10.1142/S0219649220400158.
J. Snegha, V. Tharani, S. D. Preetha, R. Charanya, and S. Bhavani, “Chronic Kidney Disease Prediction Using Data Mining,†Int. Conf. Emerg. Trends Inf. Technol. Eng. ic-ETITE 2020, pp. 1–5, 2020, doi: 10.1109/ic-ETITE47903.2020.482.
S. Rezayi, K. Maghooli, and S. Saeedi, “Applying Data Mining Approaches for Chronic Kidney Disease Diagnosis,†Int. J. Intell. Syst. Appl. Eng., 2021, doi: DOI: https://doi.org/10.18201/ijisae.2021473640.
R. Pramanik, S. Khare, and M. K. Gourisaria, “Inferring the Occurrence of Chronic Kidney Failure: A Data Mining Solution,†2021, doi: https://doi.org/10.1007/978-981-16-3346-1_59.
I. Saha, M. K. Gourisaria, and G. M. Harshvardhan, “Classification System for Prediction of Chronic Kidney Disease Using Data Mining Techniques,†2022.
E. Purwaningsih, “Improving the Performance of Support Vector Machine With Forward Selection for Prediction of Chronic Kidney Disease,†JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 8, no. 1, pp. 18–24, 2022, doi: 10.33480/jitk.v8i1.3327.
G. Wijaya, “Improvement of Kernel SVM to Enhance Accuracy in Chronic Kidney Disease,†vol. 9, no. 1, pp. 136–144, 2024, doi: https://doi.org/10.33395/sinkron.v9i1.13112 e-ISSN.
J. Nasir, A. W. Aranski, and Y. L. Setiawan, “Jaringan Syaraf Tiruan untuk Memprediksi Pengambilan Keputusan untuk Memberikan Kredit kapada Calon Nasabah Baru,†J. Apl. Sains, Inf. , Elektron. dan Komput., vol. 5, no. 2, 2023, doi: 10.26905/jasiek.v5i2.11549.
R. Ridwansyah, V. Riyanto, A. Hamid, S. Rahayu, and J. J. Purnama, “Grouping Data in Predicting Infant Mortality Using K-Means and Decision Tree,†Paradigma, vol. 24, no. 2, pp. 168–174, 2022, doi: 10.31294/paradigma.v24i2.1399.
I. Ariyati, S. Rosyida, K. Ramanda, V. Riyanto, S. Faizah, and Ridwansyah, “Optimization of the Decision Tree Algorithm Used Particle Swarm Optimization in the Selection of Digital Payments,†in Journal of Physics: Conference Series, 2020, vol. 1641, no. 1, doi: 10.1088/1742-6596/1641/1/012090.
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