Classification of Low Birth Weight Baby Under Anthropometry uses Algorithms K-Means Clustering on Maternity Hospital
Abstract
LBW infants with birth weight less than 2500 grams regardless gestation period. Low birth weight is the weight of a baby who weighed within 1 hour after birth. World Health Organization (WHO) since 1961 states that all newborns are underweight or equal to 2,500 g called low birth weight infant (low birth weight). According to WHO. Statistically, morbidity and mortality in neonates in developing countries is high, with the main causes is associated with LBW. To facilitate medical personnel in determining the risk of LBW. From the testing that has been done by the author, the k-means clustering algorithm has accuracy in classifying LBW babies by spacing the proximity between variables and the similarities in the test data,
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Budiman, et al. 2016. Comparison of Methods K-Means and Methods
DBSCAN At Boarding House Grouping Students in the Village Tembalang Semarang. Journal Gaussian. Vol. 5 No. 4 Year 2016 Page 757-762. ISSN: 2339-2541.
Kusrini and Lutfhi, ET 2009. Data Mining Algorithm. Andi Yogyakarta.
Mardiani. 2014. Comparison Algorithm K-Means and EM for Clusterisasi Value Based Home School Students. CITEC Journal. Vol. 1 No. 4 August to October 2014. ISSN: 2354-5771.
Prasetyo, Eko. 2012. Data Mining - Concepts and Applications Using MATLAB. ANDI OFFSET.
Pramudyo Eko Rio. Sistem Pakar Diagnosa Penyakit Infeksi Saluran Pernafasan Akut (ISPA) Dengan Menggunakan Metode Sugeno dan Naive Bayes.
Mastian, Hasni. "Faktor-Faktor yang Berhubungan dengan Kejadian Berat Badan Lahir Rendah." NERS Jurnal Keperawatan 7.2 (2011): 114-120.
Alya, dian., And STIK U'BUDIYAH. "Factors associated with low birth weight (LBW) in maternal and child hospital Banda Aceh in 2013." Essay. Midwifery Diploma Course IV STIKes Ubudiyah: Banda Aceh (2014).
Swastina, Liliana, and Bambang Lareno. "Model Penentuan Potensi Status Gizi Bermasalah Menggunakan Decision Tree." (2014).
Patil, Tina R., and S. S. Sherekar. "Performance analysis of Naive Bayes and J48 classification algorithm for data classification." International Journal of Computer Science and Applications 6.2 (2013): 256-261.
C. Boutsidis, A. Zouzias, P. Drineas, Random projections for k-means clustering, in: NIPS, 2010, pp. 298306.
P.S. Bradley, U.M. Fayyad, Refining initial points for k-means clustering, ICML 98 (1998) 9199.
M.B. Cohen, S. Elder, C. Musco, C. Musco, M. Persu, Dimensionality reduction for k-means clustering and low rank approximation, in: Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing, 2015, pp. 163172.
I. Davidson, A. Satyanarayana, Speeding up k-means clustering by bootstrap averaging, IEEE data mining workshop on clustering large data sets, 2003.
V. Gandhi, J.M. Kang, S. Shekhar, Spatial Databases, Wiley Encyclopedia of Computer Science and Engineering, 2008.
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