Prediksi Penyakit Kanker Paru-Paru Dengan Algoritma Regresi Linier
Abstract
Lung cancer is one of the deadliest types of cancer worldwide. Therefore, efforts to predict the likelihood of developing lung cancer are very important in its prevention and treatment. One way to predict the likelihood of getting lung cancer is to use a linear regression algorithm. This study aims to develop a predictive model that can identify a person's likelihood of developing lung cancer based on certain factors, such as age, passive smoker and level or severity. The data used in this study were collected from 100 patients diagnosed with lung cancer and their severity. The results of the analysis show that the linear regression algorithm can be used to predict the probability of getting lung cancer with an accuracy of about 90% and is able to give good results with a Root Mean Squared Error: 0.686 +/- 0.000 and Squared Error: 0.471 +/- 0.546
References
N. Aifa Zahara and M. Fakultas Tarbiyah dan Keguruan Program Studi Pendidikan Biologi FAKULTAS TARBIYAH DAN KEGURUAN, “PENGEMBANGAN MEDIA PEMBELAJARAN AUDIOVISUAL BERBASIS NILAI ALQURAN PADA MATERI SISTEM PERNAPASAN DI SMP NEGERI 2 KABUPATEN ACEH BESAR SKRIPSI”, 2022
Y. Mahena, M. Rusli and E. Winarso, “Prediksi Harga Emas Dunia Sebagai Pendukung Keputusan Investasi Saham Emas Menggunakan Teknik Data Mining” JURNAL Sains dan Teknologi, vol. 2, no. 1. 2019
M. Kafil, “PENERAPAN METODE K-NEAREST NEIGHBORS UNTUK PREDIKSI PENJUALAN BERBASIS WEB PADA BOUTIQ DEALOVE BONDOWOSO,” 2019.
S. Arafah and Y. Tanjung, “ANALISIS FAKTOR DETERMINAN YANG MEMPENGARUHI PEMAKAIAN METODE JIT (Studi Kasus UD. Pusaka Bakti).” Jurnal Ekonoomi Syariah, 2020 [Online]. Available: http://ejournal.unhasy.ac.id/index.php/bisei/article/view/440
S. Kasus, L. Pendidikan Darul Ulum Bantaran Probolinggo, M. Iqbal, and J. Matematika Fakultas Sains Dan, “ANALISIS TREND LINIER DENGAN METODE KUADRAT TERKECIL UNTUK MERAMALKAN PERKEMBANGAN BANYAKNYA SISWA,” 2000.
S. Anastassia Amellia Kharis and A. Haqqi Anna Zili, “Learning Analytics dan Educational Data Mining pada Data Pendidikan,” Jurnal Riset Pembelajaran Matematika Sekolah, vol. 6, 2022.
D. A. Manalu and G. Gunadi, “IMPLEMENTASI METODE DATA MINING K-MEANS CLUSTERING TERHADAP DATA PEMBAYARAN TRANSAKSI MENGGUNAKAN BAHASA PEMROGRAMAN PYTHON PADA CV DIGITAL DIMENSI,” Infotech: Journal of Technology Information, vol. 8, no. 1, pp. 43–54, Jun. 2022, doi: 10.37365/jti.v8i1.131.
E. A. Firdaus, S. Maulani, A. B. Dharmawan, A. Keperwatan, and R. S. Dustira Cimahi, “PENGUKURAN MINAT BACA MAHASISWA DENGAN METODE CLUSTERING DI PERPUSTAKAAN AKADEMI KEPERAWATAN RS.DUSTIRA CIMAHI MENGGUNAKAN DATA MINING,” vol. 15, no. 1, 2021, [Online]. Available: https://journal.uniku.ac.id/index.php/ilkom
A. R. Wijaya, “MODEL PREDIKSI DATA HARGA MINYAK MENTAH DUNIA DENGAN METODE EXPONENTIAL SMOOTHING,” 2023.
Copyright (c) 2023 Muhammad Abdul Rahman Wahid, Agung Nugroho, Abdul Halim Anshor

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