Implementasi Algoritma C4.5 pada Sistem Prediksi Stroke Berdasarkan Data Kesehatan
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Abstract
Stroke is one of the leading causes of death and disability worldwide. Early detection of the risk of stroke is crucial to reducing its impact and improving the effectiveness of treatment. This study aims to predict the probability of a person being at risk of stroke using the C4.5 Algorithm, known as a decision tree-based method. The dataset used includes variables such as age, blood pressure, blood sugar levels, medical history, smoking habits, and physical activity. After data preprocessing, including handling missing data and normalization, a predictive model was built using the C4.5 Algorithm. The results show that the model achieved an accuracy of 95.01%, a precision of 33.33%, a recall of 2.04%, and an F1-score of 3.81%. Additionally, the Negative Predictive Value (NPV) of 95.10% and specificity of 99.79% demonstrate the model's ability to detect risks effectively in negative cases. The model also generates decision rules that are easily interpretable by medical professionals, making it a potential tool to support medical decision-making in the early identification of stroke risks.
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References
Abadi, A. F., Alamsyah, N., Retnanto, F. G., Daniati, E., & Ristyawan, A. (2024). Penerapan Data Mining dalam Mengklasifikasi Penyakit Stroke Menggunakan Algoritma. Agustus, 7, 2549–7952.
Abidin, Z., Tamrin, T., Harsono, V., Aziza, D. N., & Kansania, I. (2024). OPTIMALISASI DIAGNOSIS STROKE DENGAN ALGORITMA C4.5 DAN STRATEGI IMPUTASI k-NN UNTUK MENGATASI MISSING VALUE OPTIMIZATION OF STROKE DIAGNOSIS USING C4.5 ALGORITHM AND k-NN IMPUTATION STRATEGY TO OVERCOME MISSING VALUE. 15(2), 2548–4168.
Alfiyan, R., Hikmatyar, M., & Sundari, S. S. (2024). Indonesian Journal of Digital Business Implementasi Metode Klasifikasi C4 . 5 Penyebab Faktor. 4(October), 37–48.
Gusrialni Fitri, N., Adilya, S., & Azizi, F. (2023). Comparison of the Naive Bayes Classification System and C4.5 for the Diagnosis of Stroke Perbandingan Sistem Klasifikasi Naive Bayes dan C4.5 Untuk Diagnosa Penyakit stoke. SENTIMAS: Seminar Nasional Penelitian Dan Pengabdian Masyarakat, 49–55.
Maulana Sidiq, C., Faqih, A., & Dwilestari, G. (2024). Algoritma Decision Tree C4.5 Digunakan Untuk Mengklasifikasikan Data Stroke. JATI (Jurnal Mahasiswa Teknik Informatika), 8(2), 1869–1874. https://doi.org/10.36040/jati.v8i2.8388
Nabila, F., Afrianty, I., Sanjaya, S., & Syafria, F. (2023). Implementasi Algoritma C4.5 dalam Melakukan Klasifikasi Penyakit Stroke Otak. Jurnal Informatika Universitas Pamulang, 8(2), 229–235. https://doi.org/10.32493/informatika.v8i2.31361
Nadira, A., & Farida Utami, S. (2024). Implementasi Data Mining Dalam Mengidentifikasi Faktor Pasien Yang Berpotensi Mengalami Obesitas Menggunakan Algoritma C4.5. JATI (Jurnal Mahasiswa Teknik Informatika), 8(4), 7872–7876. https://doi.org/10.36040/jati.v8i4.10498
Pradhana, A. H., Irfa, M., Ali, A., Ristyawan, A., & Daniati, E. (2024). Penerapan Regresi Linear Menggunakan RapidMiner Untuk Memprediksi Penjualan dan Persediaan. 8, 291–297.
Rivliansyah, Z., Persyaratan, U., Dan, P., & Tugas, P. (2022). UNTUK MEMPREDIKSI PENYAKIT STROKE OTAK PROGRAM STUDI TEKNIK INFORMATIKA.
Saputra, R. A., Agustina, C., Puspitasari, D., Ramanda, R., Warjiyono, Pribadi, D., Lisnawanty, & Indriani, K. (2020). Detecting Alzheimer’s Disease by the Decision Tree Methods Based on Particle Swarm Optimization. Journal of Physics: Conference Series, 1641(1), 61–67. https://doi.org/10.1088/1742-6596/1641/1/012025
Yuliska, Y., & Syaliman, K. U. (2020). Literatur Review Terhadap Metode, Aplikasi dan Dataset Peringkasan Dokumen Teks Otomatis untuk Teks Berbahasa Indonesia. IT Journal Research and Development, 5(1), 19–31. https://doi.org/10.25299/itjrd.2020.vol5(1).4688