Multiclass classification of toddler nutritional status using support vector machine: A case study of community health centers in Bangkalan, Indonesia

Open Access

Issue		BIO Web Conf. Volume 146, 2024 2^nd Biology Trunojoyo Madura International Conference (BTMIC 2024)


Article Number		01082
Number of page(s)		7
Section		Dense Matter
DOI		https://doi.org/10.1051/bioconf/202414601082
Published online		27 November 2024

K. D. Dhiu and D. N. L. Laksana, The Aspects Of Child Development On Early Childhood Education Curriculum, Journal of Education Technology, 5, 1, 1–7, (2021), doi: https://doi.org/10.23887/jet.v5i1.30764 [CrossRef] [Google Scholar]
A. Likhar, P. Baghel and M. Patil, Early Childhood Development and Social Determinants, Cureus, 14, 9, (2022), DOI: 10.7759/cureus.29500. [Google Scholar]
T. Beal, A. Tumilowicz, A. Sutrisna, D. Izwardy and L. Neufeld, A review of child stunting determinants in Indonesia, Maternal & Child Nutrition, 14, 2, (2018), doi: https://doi.org/10.1111/mcn.12617 [CrossRef] [Google Scholar]
Mardiyanti and A. Case, Developmental assessment and early intervention for children with developmental delays: A case study in South Australia, Belitung Nursing Journal, 7, 3, 251–259, (2021), DOI: 10.33546/bnj.1480 [CrossRef] [Google Scholar]
S. Khadijah, D. A. Palifiana, K. Astriana and C. Amalinda, The effect of eating behavior on the nutritional status of toddlers, Indonesian Journal of Nutrition and Dietetics, 10, 3, 199–124, (2022), DOI: 10.21927/ijnd.2022.10(3).119-124 [Google Scholar]
F. Morales, S. M.-D. L. Paz, M. J. Leon and F. R. Pino, Effects of Malnutrition on the Immune System and Infection and the Role of Nutritional Strategies Regarding Improvements in Children ‘s Health Status: A Literature Review, MDPI, 16, 1, (2023), doi: https://doi.org/10.3390/nu16010001 [Google Scholar]
A. Kustanto, THE PREVALENCE OF STUNTING, POVERTY, and ECONOMIC GROWTH IN INDONESIA: A DYNAMIC PANEL DATA CAUSALITY ANALYSIS, JDE (Journal of Developing Economies), 2, 4, (2018), DOI: 10.20473/jde.v6i2.22358 [Google Scholar]
M.C. Ariestyani, P.P. Adikara, R.S. Perdana, Klasfikasi Penyimpangan Tumbuh Kembang Anak Menggunakan Metode Extreme Learning Machine (ELIM), Jurnal Pengembangan Teknologi Informasi dan Ilmu Komput, 6, 2, 150–173, (2021) [Google Scholar]
F. Riandari, H. T. Sitohang, T. Tarigan and M. Rafli, Classification of Book Types Using the Support Vector Machine (SVM) Method, Institute of Computer Science (IOCS), 6, 1, 43–49, (2022) [Google Scholar]
H. Shen, H. Zhao and Y. Jiang, Machine Learning Algorithms for Predicting Stunting among Under- Five Children in Papua New Guinea, MDPI Journal, 10, 10, (2023), doi: https://doi.org/10.3390/children10101638 [Google Scholar]
D. Fitria, T. H. Saragih, Muliadi, D. Kartini and F. Indriani, Classification of Appendicitis in Children Using SVM with KNN Imputation and SMOTE Approach to Improve Prediction Quality, Journal of Electronics, Electromedical Engineering, and Medical Informatics, 6, 3, 302–311, doi: https://doi.org/10.35882/jeeemi.v6i3.470 [Google Scholar]
N. S. Dwijayanti and M. Mufdlilah, Factors Influencing The Growth and Development of Children with Stunting, Scoping Review, Women, Midwives and Midwifery Journal, 2, 1, 40–57, (2022), DOI: 10.36749/wmm.2.1.40-57.2022 [CrossRef] [Google Scholar]
H. Gani and M. Tomayahu, Perbandingan Metode Naïve Bayes dan C4.5 Klasifikasi Status Gizi Bayi Balita, SPECTA Journal of Technology, 6, 3, 273283, (2022), doi: https://doi.org/10.35718/specta.v6i3.789 [Google Scholar]
R. Sinha, A Study on Importance of Data Mining in Information Technology, International Journal of Research in Engineering, IT and Social Sciences, 8, 11, 162–168, (2018), DOI: 10.13140/RG.2.2.29311.53921 [Google Scholar]
M. Z. Al-Taie, S. Kadry and J. P. Lucas, Online data preprocessing: a case study approach, International Journal of Electrical and Computer Engineering (IJECE), 9, 4, 2620–2626, (2019), DOI: 10.11591/ijece.v9i4.pp2620-2626 [CrossRef] [Google Scholar]
S. S. Berutu, H. Budiati, J. Jatmika and F. Gulo, Data preprocessing approach for machine learning-based sentiment classification, INFOTEL, 15, 4, 317–325, (2023), doi https://doi.org/10.20895/infotel.v15i4.1030 [CrossRef] [Google Scholar]
A. E. Karrar, The Effect of Using Data PreProcessing by Imputations in Handling Missing Values, Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 10, 2, 375384, (2022), DOI: 10.52549/ijeei.v10i2.3730 [CrossRef] [Google Scholar]
D. K. Lee, Data transformation: a focus on the interpretation, Korean Journal of Anesthesiology, 73, 6, 503–508, (2020), doi: https://doi.org/10.4097%2Fkja.20137 [CrossRef] [PubMed] [Google Scholar]
M. Gheorghe and R. Petre, The importance of Normalization Method For Mining Medical Data, JOURNAL INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 14, 6014–6020, (2015), doi: https://doi.org/10.24297/ijct.v14i8.1855. [Google Scholar]
P. J. M. Ali, Investigating the Impact of Min-Max Data Normalization on the Regression Performance of K-Nearest Neighbor with Different Similarity Measurements, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 10, 1, 85–91, (2022) [CrossRef] [Google Scholar]
A. Rachmad, N. Chamidah, R. Rulaningtyas. Mycobacterium tuberculosis images classification based on combining of convolutional neural network and support vector machine, Communications in Mathematical Biology and Neuroscience. 2020, (2020) doi: https://doi.org/10.28919/cmbn/5035 [Google Scholar]
M. A. Chandra and S. S. Bedi, Survey on SVM and their application in image classification, International Journal of Information Technology, 13, 2, (2018), DOI: 10.1007/s41870-017-0080-1 [Google Scholar]
A. Wu, J. Zhu, Y. Yang, X. Liu, X. Wang, L. Wang, H. Zhang and J. Chen, Classification of corn kernels grades using image analysis and support vector machine, SAGE Journals, 10, 12, (2018) [Google Scholar]
Liu, Z., Du, Y., Peng, M., Liu, X., Chen, W., Study on corn disease identification based on PCA and SVM, Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, 661–664, (2020) [Google Scholar]
S. A. Hicks, I. Strumke, V. Thambawita, M. Hammou, M. A. Riegler, P. Halvorsen and S. Parasa, On evaluation metrics for medical applications of artificial intelligence, PubMed Central, 12, (2022) [Google Scholar]
M. U. Devi, Categorizing The Age Group and Measuring Accuracy Of Fuzzy Model, International Journal of Electronics and Communication Engineering and Technology, 10, 5, 36–46, (2019) [Google Scholar]
A. Rachmad, M. Fuad, E.M.S. Rochman. Convolutional neural network-based classification model of corn leaf disease, Mathematical Modelling of Engineering Problems, 10, 2, 530536, (2023), doi: https://doi.org/10.18280/mmep.100220 [Google Scholar]

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