PREDIKSI PENYAKIT DIABETES DENGAN MENGGUNAKAN ALGORITMA C4.5
Abstract
Penyakit diabetes merupakan tantangan kesehatan global yang terus meningkat, memerlukan prediksi yang tepat untuk intervensi dini dan manajemen yang efektif. Dalam studi ini, kami menggunakan algoritma C4.5, suatu metode pembelajaran mesin yang terbukti, untuk memproyeksikan risiko diabetes pada kelompok khusus. Analisis mendalam terhadap atribut klinis yang paling penting juga kami lakukan, memberikan wawasan yang lebih rinci tentang profil penyakit dan faktor-faktor yang mempengaruhi. Harapannya, Hasil riset ini dapat menjadi panduan untuk mengembangkan strategi pencegahan yang lebih efektif dan rekomendasi pengobatan individual untuk individu yang menderita diabetes. Dengan menggunakan kumpulan data klinis yang komprehensif, kami berhasil membuat model prediksi yang mampu mengidentifikasi faktor risiko utama dengan tingkat akurasi mencapai 96.16%. Oleh karena itu, riset ini memberikan masukan yang sangat baik dalam upaya global untuk mengurangi prevalensi diabetes dan meningkatkan kualitas hidup pasien.
References
A, H. R. (2020). Penentuan Rekomendasi Petrainingan Pengembangan Diri Bagi Pegawai Negeri Sipil Menggunakan Algoritma C4.5 Dengan Principal Component Analysis. J. TEKNO KOMPAK.
Chan, P. P. (2018). Pengembangan Aplikasi Perhitungan Prediksi Stock Motor Menggunakan Algoritma C 4.5 Sebagai Bagian dari Sistem Pengambilan Keputusan (Studi Kasus di Saudara Motor). INOVTEK Polbeng -Seri Inform.
Deny Jollyta, W. R. (2020). Konsep Data Mining Dan Penerapan. Deepublish.
Hana, F. M. (2020). Klasifikasi Penderita Penyakit Diabetes Menggunakan AlgoritmaDecision TreeC4.5. Jurnal Sistem Komputer dan Kecerdasan Buatan.
Haikal, M., Kusuma, R. S., Nauvanda, S. E., & Safitri, M. (2022). Perancangan User Interface dan User Experience Pada Web MB Tours and Travel Bekasi. JIKA (Jurnal Informatika), 6(3), 271–278. https://doi.org/10.31000/jika.v6i3.6777
Handayani, P. K. (2020). Penerapan Principal Component Analysis untuk Peningkatan Kinerja Algoritma Decission Tree pada IRIS Dataset. IJTIS.
Ikhsan Romli, A. T. (2020). Penentuan Jadwal Overtime Dengan Klasifikasi Data Karyawan Menggunakan Algoritma C4.5. Jurnal Sains Komputer & Informatika.
Muhamad Ichsan Gunawan, D. S. (2020). Peningkatan Kinerja Akurasi Prediksi Penyakit Diabetes Mellitus Menggunakan Metode Grid Seacrh pada Algoritma Logistic Regression. Jurnal Edukasi dan Penelitian Informatika.
Nasrulloh, A. h. (2021). Implementasi Algoritma Decision Tree untuk Klasifikasi Produk LARIS . Jurnal Ilmiah Ilmu Komputer.
Nurlina. (2019). Politeknik Kesehatan Makassar Jurnal Media Keperawatan: Politeknik Kesehatan Makassar. Jurnal Media Keperawatan.
Praba, A. D., Safitri, M., & Faridi, F. (2021). Implementasi Datatables Server-Side untuk Mempercepat Load Halaman pada Aplikasi E-Commerce. JIKA (Jurnal Informatika), 5(2), 139–144. https://doi.org/10.31000/jika.v5i2.4339
Ramadhan, M. A. (2019). Patient Empowerment Dan Self-Management Pada Pasien Diabetes Mellitus Tipe 2. Jurnal Ilmiah Kesehatan Sandi Husada.
Rokhman, K. A. (2021). Perbandingan Metode Support Vector Machine dan Decision Tree untuk Analisi Sentimen Review Komentar pada Aplikasi Transportasi Online. Jurnal of Information System Management.
Sanni Ucha Putri, E. I. (2021). Implementasi Data Mining Untuk Prediksi Penyakit Diabetes Dengan Algoritma C4.5 . KESATRIA: Jurnal Penerapan Sistem Informasi.
Siska Febriani, H. S. (2021). ANALISIS DATA HASIL DIAGNOSA UNTUK KLASIFIKASI GANGGUAN KEPRIBADIAN MENGGUNAKAN ALGORITMA C4.5 . Jurnal Teknologi dan Sistem Informasi.
Srisulistiowati, D. B. (2021). Sistem Informasi Prediksi Penjualan Alat Tulis Kantor dengan Metode FP-GROWTH. Jurnal Sistem Informatika Universita Suryadarma.
Zai, C. (2022). Implementasi Data Mining Sebagai Pengolahan Data. Portal Data.
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