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PENERAPAN ALGORITMA J48 UNTUK DETEKSI PENYAKIT TIROID
Abstract
Impaired thyroid function is often difficult to identify because the symptoms are not specific. The symptoms of thyroid disorder are very similar to various complaints due to modern lifestyles so it is often overlooked. As a result, patients often do not notice a problem and do not have to consult a doctor. Therefore, there is a study that implements methods to predict the disease which will facilitate the patient in diagnosing and early detection of thyroid levels. This research aims to predict against thyroid disease with the data used is the secondary data obtained from the UCI repository, this data is about the patient data affected by thyroid disease, while the method uses the J48 algorithm because in some studies, the J48 algorithm is proven to have good performance in detecting an illness, as well as producing high value of Accuasy and AUC. The stage of data analysis is based on the CRISP-DM method while algorithm testing is done with Weka tools. Results of the test obtained an accuracy value of 99.645%, and a AUC value of 0.992 thus the accuracy has Excellent Classification level.
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Astuti, T., Mujiati, I., Ayu, D., Ristianah, V., & Lestari, W. A. (2016). Penerapan Algoritme J48 Untuk Prediksi. Jurnal Telematika, 9(2), 1–10.
Decroli, G. P., & Decroli, E. (n.d.). Laporan Kasus Krisis Tiroid pada Wanita Multipara Usia 42 Tahun, 8(1), 178–181.
Diwandari, S., & Setiawan, N. A. (2015). Perbandingan Algoritme J48 Dan Nbtree Untuk Klasifikasi Diagnosa Penyakit Pada Soybean. Seminar Nasional Teknologi Informasi Dan Komunikasi, 2015(Sentika), 205–212.
Gunawan, A., Palit, H. N., & Handojo, A. (2018). Aplikasi Rekomendasi Metode Analisis Sesuai dengan Karakter Data. Jurnal Infra, 6(1).
Handayani, P., Nurlelah, E., Raharjo, M., & Ramdani, P. M. (2019). PREDIKSI PENYAKIT LIVER DENGAN MENGGUNAKAN METODE, 4(1), 55–59.
Haryati, S., Sudarsono, A., & Suryana, E. (2015). IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI MASA STUDI MAHASISWA MENGGUNAKAN ALGORITMA C4 . 5. JUrnal Media Infotama, 11(2), 130–138.
Henry Crosby, V. P., & Kandidat, M. A. M. (2016). Pola kelainan tiroid di RSUP Prof. Dr. R. D. Kandou Manado periode Januari 2013 - Desember 2015. Jurnal E-Clinic, 4(1), 430–437.
Kementerian Kesehatan. (2015). Infodatin Tiroid.
Kurniawan, L. B., & Arif, M. (2015). DIAGNOSIS TIROID (Diagnosis of Thyroid) Liong. INDONESIAN JOURNAL OF CLINICAL PATHOLOGY AND MEDICAL LABORATORY, 7(1), 2–7.
Lishania, I., Goejantoro, R., & Nasution, N. (2019). Perbandingan Klasifikasi Metode Naive Bayes dan Metode Decision Tree Algoritma ( J48 ) pada Pasien Penderita Penyakit Stroke di RSUD Abdul Wahab Sjahranie Samarinda Comparison of the Classification for Naive Bayes Method and the Decision Tree Algorithm ( , 10, 135–142.
Lombok, H., Mataram, R., Astuti, T., Ferinanto, T., Informatika, J. T., & Informasi, J. S. (2016). Diagnosis Penyakit Parkinson Berdasarkan Kombinasi, 28–29.
Putri, A. W. (2019). Gangguan Tiroid sebagai Beban Baru Negara.
Rahman, M. F., Alamsah, D., Darmawidjadja, M. I., & Nurma, I. (2017). Klasifikasi Untuk Diagnosa Diabetes Menggunakan Metode Bayesian Regularization Neural Network (RBNN). Jurnal Informatika, 11(1), 36. https://doi.org/10.26555/jifo.v11i1.a5452
Santoso, W. B., Santoso, B., Romadhon, & Sukandar. (2016). PERANGKAT SCINTIGRAFI UNTUK TIROID SC-12 pada organ tersebut . Proses pencitraan menggunakan perangkat scintigrafi dilakukan untuk mendiagnosis fungsi kelenjar tiroid ( gondok ) [ 1 ] . Proses pencitraan tiroid radionuklida ke pasien . Sistem deteksi pera. JURNAL PERANGKAT NUKLIR, 10(1978), 1–10.
Septina, L., Zaid, M., Zuraidah, E., Stephanie, A., Septina, L., Zaid, M., … Stephanie, A. (2019). Pratista Patologi Peran Rearrangement RET / PTC pada Karsinoma Papiler Tiroid Pratista Patologi, 6(1).
Sidiq, U., Aaqib, S. M., & Khan, R. A. (2019). Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques, 5(1), 131–136.
Suyanto. (2017). Data Mining untuk Klasifikasi dan Klasterisasi Data. Bandung: Informatika Bandung.
T, L. D., & Larose, C. D. (2015). Data Mining and Predictive Analytics (Second Edi). Canada: Simultaneously.
Widayati, P., Ariyanto, A., Setiyowati, S., Yulianti, V., Teknologi, P., Setu, K. P., & Selatan, T. (2019). Preparasi Pereaksi Kit Immunoradiometricassa ( IRMA ) Thyroglobulin ( TGB ) Untuk Deteksi Kanker Tiroid, (April), 1–8.
Wijonarko, B. (2018). Perbandingan Algoritma Data Mining Naive Bayes Dan Bayes Network Untuk Mengidentifikasi Penyakit Tiroid. PILAR Nusa Mandiri, 14(1), 21–26. Retrieved from https://media.neliti.com/media/publications/227524-perbandingan-algoritma-data-mining-naive-2eb3d17d.pdf
DOI: https://doi.org/10.31294/p.v22i2.8174
Copyright (c) 2020 Sarifah Agustiani, Ali Mustopa, Andi Saryoko, Windu Gata, Siti Khotimatul Wildah
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
ISSN: 2579-3500
