Toddlers are among the most vulnerable groups to nutritional problems when viewed from the point of view of health and nutrition problems, while at this time they are experiencing a cycle of relatively rapid growth and development. .7% is quite high where the number of births is relatively large. Researchers try to classify 10 toddlers using WEKA to find out whether they have nutritional disorders or are normal by using 5 attributes as system input and a class namely nutrition which divides this class into 4 namely bad, less, good and more with the amount of training data 219 data then data compared with the actual nutritional conditions and obtained an accuracy of 60% and an error of 40% with these results it can be concluded that the accuracy is not too good. Based on this, it is hoped that the results of this classification can help further research in classifying the nutrition of children under five.
A. L. Costa, “Teaching and Assessing Habits of Mind,” vol. 96741, no. 808, 1999.
R. H. Lestari, M. Mudhawaroh, and M. Ratnawati, “Intelligence Optimization in the Golden Age by Stimulating the Right-Brain in Mojokrapak Village, Tembelang District, Jombang Regency,” Nucleus, vol. 1, no. 2, pp. 58–61, 2020, doi: 10.37010/nuc.v1i2.166.
A. Chapnick, “The golden age,” Int. J., vol. 64, no. 1, pp. 205–221, 2008, doi: 10.1177/002070200906400118.
M. Muslim, H. Ahmad, and S. Rahim, “The effect of emotional, spiritual and intellectual intelligence on auditor professionalism at the inspectorate of South Sulawesi Province,” Indones. Account. Rev., vol. 9, no. 1, p. 73, 2019, doi: 10.14414/tiar.v9i1.1416.
UNICEF., Children, food and nutrition : growing well in a changing world. 2019.
I. G. Pratiwi and D. A. Restanty, “Pengaruh Metode Think, Pair and Share Terhadap Keterampilan Kader Dalam Pengisian Kms Balita,” JPP (Jurnal Kesehat. Poltekkes Palembang), vol. 13, no. 2, pp. 128–135, 2019, doi: 10.36086/jpp.v13i2.237.
W. Yulianti and Salmidi, “Metode Rough Set untuk Menganalisa Problematika Guru Dalam Menggunakan Media Pembelajaran Berbasis Komputer,” J. Teknol. dan Sist. Inf. Univrab, vol. 1, no. 1, pp. 19–25, 2016.
Y. Cheng, K. Chen, H. Sun, Y. Zhang, and F. Tao, “Data and Knowledge Mining with Big Data towards Smart Production,” J. Ind. Inf. Integr., vol. 9, Sep. 2017, doi: 10.1016/j.jii.2017.08.001.
H. Tran, Survey of Machine Learning and Data Mining Techniques used in Multimedia System. 2019.
R. Amanda and E. S. Negara, “Analysis and Implementation Machine Learning for YouTube Data Classification by Comparing the Performance of Classification Algorithms,” J. Online Inform., vol. 5, no. 1, pp. 61–72, 2020, doi: 10.15575/join.v5i1.505.
S. Suhada and E. Setiawan, “Classification Needs Teachers,” no. Icisbc, pp. 408–414, 2013.
A. W. Syaputri, E. Irwandi, and M. Mustakim, “Naïve Bayes Algorithm for Classification of Student Major’s Specialization,” J. Intell. Comput. Heal. Informatics, vol. 1, no. 1, p. 17, 2020, doi: 10.26714/jichi.v1i1.5570.
I. B. A. Peling, I. N. Arnawan, I. P. A. Arthawan, and I. G. N. Janardana, “Implementation of Data Mining To Predict Period of Students Study Using Naive Bayes Algorithm,” Int. J. Eng. Emerg. Technol., vol. 2, no. 1, p. 53, 2017, doi: 10.24843/ijeet.2017.v02.i01.p11.
M. J. Sodiq and E. I. Sela, “Perbandingan Metode Naive Bayes Dan K-Nearest Neighbor Pada Klasifikasi Kualitas Udara Di Dki Jakarta,” 2019.
R. Rino, “The Comparison of Data Mining Methods Using C4.5 Algorithm and Naive Bayes in Predicting Heart Disease,” Tech-E, vol. 4, no. 2, p. 44, 2021, doi: 10.31253/te.v4i2.543.
Mustakim et al., “Data Sharing Technique Modeling for Naive Bayes Classifier for Eligibility Classification of Recipient Students in the Smart Indonesia Program,” J. Phys. Conf. Ser., vol. 1424, no. 1, 2019, doi: 10.1088/1742-6596/1424/1/012009.
C. Fadlan, S. Ningsih, and A. Windarto, “PENERAPAN METODE NAÏVE BAYES DALAM KLASIFIKASI KELAYAKAN KELUARGA PENERIMA BERAS RASTRA,” J. Tek. Inform. Musirawas, vol. 3, p. 1, Jun. 2018, doi: 10.32767/jutim.v3i1.286.
E. S. Ginting, “Ratio-Based Financial Performance Analysis of PT. Mustika Ratu, Tbk”, enrichment, vol. 11, no. 2, pp. 456-462, May 2021.
Ginting, E. S., Apren Halomoan Hutasoit, & Naca Warsinangin. (2021). North Sumatra Economic Growth Analysis. Jurnal Mantik, 5(1), 184-190. https://doi.org/10.35335/mantik.Vol5.2021.1297.pp184-190
Ginting, E. S. ., Lubis, T. W. H. ., & Pertiwi, S. . (2021). Kiat Menghadapi Tantangan Pembelajaran Daring Di Masa Pandemi Covid-19. TRIDARMA: Pengabdian Kepada Masyarakat (PkM), 4(1), 35-43. Retrieved from http://iocscience.org/ejournal/index.php/abdimas/article/view/1286
Ginting, E. S., & Apren Halomoan Hutasoit. (2021). Understanding the Use of Information Technology as a Supporting Media for Student Learning at SMK Negeri 1 Lubuk Pakam. Jurnal Mantik, 4(4), 2449-2452. https://doi.org/10.35335/mantik.Vol4.2021.1179.pp2449-2452
Ginting, E. S., & Hutasoit, A. H. (2021). FACTORS AFFECTING STUDENTS’THESIS COMPLETION ON DEPARTMENT OF MANAGEMENT STIE MIKROSKIL. JURNAL TARBIYAH, 27(2).
M. Collins, “Research methodology,” J. Occup. Heal. Saf. - Aust. New Zeal., vol. 6, no. 5, p. 352, 1990.
S. Jyothi and P. Bhargavi, “Applying Naive Bayes Data Mining Technique for Classification of Agricultural Land Soils,” 57. P.Bhargavi , S. Jyothi, vol. 9, Aug. 2009.
M. Tigor, “Bagaimana Menyatakan Permasalahan Riset (Research Problem/Problem Statement)?,” Rudang Mayang Publ., vol. 1, no. 1, pp. 1–2, 2020
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.