PERBANDINGAN HASIL ANALISIS TEKNIK DATA MINING “METODE DECISION TREE, NAIVE BAYES, SMO DAN PART” UNTUK MENDIAGNOSA PENYAKIT DIABETES MELLITUS
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Abstract
Diabetes Mellitus (DM) merupakan salah satu penyakit yang dapat membawa seseorang berujung pada suatu kematian. Peyebab utama dari penyakit ini adalah pola hidup dan makanan yang tidak teratur atau berlebihan. Seseorang yang terserang penyakit diabetes akan ditandai dengan meningkat kadar gula. Hal ini terjadi karena adanya gangguan pada sekresi insulin dan kerja insulin atau bahkan pada keduanya. Di diberbagai negara semakin banyak pasien penyakit diabetes, jika tidak segera dihentikan maka diperkirakan penderita penyakit diabetes akan mencapai 642 jiwa pada tahun 2040 [1]. Penelitian ini bertujuan untuk memilih Clasifiers data mining yang terbaik dalam melakukan diagnosis terhadap penyakit Diabetes Mellitus (DM). Diagnosis yang dilakukan berbasis sistem komputer dengan menggunakan metode seleksi fitur dan klasifikasi terhadap Dataset Pima Indians Diabetes. Metode seleksi fitur yang digunakan adalah Correlation based Featured Selection (CFS). Hasil klasifikasi data mining pada penelitian ini menunjukkan bahwa SMO memiliki nilai akurasi tertinggi dibanding Classifiers yang lainnya.
References
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[2] Trihon dan N. Mboi, “Riset Kesehatan Dasar,” Badan Penelit. Dan Pengemb. Kesehat. Kementeri. Kesehat. RI, hlm. 304, 2013.
[3] E. Shakibazadeh, B. Larijani, D. Shojaeezadeh, A. Rashidian, M. Forouzanfar, dan L. Bartholomew, “Patients’ Perspectives on Factors that Influence Diabetes Self-Care,” Iran. J. Public Health, vol. 40, no. 4, hlm. 146–158, Des 2011.
[4] J. E. Shaw, R. A. Sicree, dan P. Z. Zimmet, “Global estimates of the prevalence of diabetes for 2010 and 2030,” Diabetes Res. Clin. Pract., vol. 87, no. 1, hlm. 4–14, Jan 2010.
[5] L. Guariguata, T. Nolan, J. beagley, U. Linnenkamp, dan olivier Jacqmain, Ed., IDF Diabetes Atlas Sixth edition, 6 ed. International Diabetes Federation, 2013.
[6] Bustam, Epidemologi Penyakit Tidak Menular. Jakarta: PT. Rineka Cipta, 2009.
[7] Perkeni, Konsensus Pengelolaan dan Pencegahan Diabetes Melitus Tipe 2. Perkumpulan Endokrinologi Indonesia, 2011.
[8] N. Chu, L. Ma, J. Li, P. Liu, dan Y. Zhou, “Rough set based feature selection for improved differentiation of traditional Chinese medical data,” dalam 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, Yantai, China, 2010, hlm. 2667–2672.
[9] I. P. D. Lesmana, “Perbandingan Kinerja Decision Tree J48 dan ID3 Dalam Pengklasifikasian Diagnosis Penyakit Diabetes Mellitus,” vol. 2, no. 2, hlm. 10, 2012.
[10] P. Giudici dan S. Figini, Applied Data Mining for Business and Industry. Chichester, UK: John Wiley & Sons, Ltd, 2009.
[11] T. Zheng dkk., “A machine learning-based framework to identify type 2 diabetes through electronic health records,” Int. J. Med. Inf., vol. 97, hlm. 120–127, Jan 2017.
[12] D. Sisodia dan D. S. Sisodia, “Prediction of Diabetes using Classification Algorithms,” Procedia Comput. Sci., vol. 132, hlm. 1578–1585, 2018.
[13] E. S. Kundari, “Perbandingan Kinerja Metode Naive Bayes dan C4.5 Dalam Pengklasifikasian Penyakit Diabetes Melitus di Rumah Sakit Kumala Siwi Kudus,” hlm. 8.
[14] M. Maniruzzaman dkk., “Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm,” Comput. Methods Programs Biomed., vol. 152, hlm. 23–34, Des 2017.
[15] J. A. Putra dan A. L. Akbar, “Klasifikasi Pengidap Diabetes Pada Perempuan Menggunakan Penggabungan Metode Support Vector Machine dan K-Nearest Neighbour,” vol. 1, no. 2, hlm. 6, 2016.
[16] M. Alehegn, R. Joshi, dan D. P. Mulay, “Analysis and Prediction of Diabetes Mellitus using Machine Learning Algorithm,” hlm. 8.
[17] F. Gorunescu, “Data Mining (Intelligent Systems Reference Library, 12),” vol. 12, hlm. 370, 2011.
[18] I. H. Witten dan E. Frank, Data Mining: Practical Machine Learning Tools and Techniques. Elsevier, 2011.
Published
2019-01-31
How to Cite
, & . (2019). PERBANDINGAN HASIL ANALISIS TEKNIK DATA MINING “METODE DECISION TREE, NAIVE BAYES, SMO DAN PART” UNTUK MENDIAGNOSA PENYAKIT DIABETES MELLITUS. Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 4(1), 38-44. https://doi.org/10.25139/inform.v4i1.1403
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Volume 4 No. 1 2019
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