Implementation of Data Classification Using K-Means Algorithm in Clustering Stunting Cases

Indah Purnama Sari, Al-Khowarizmi Al-Khowarizmi, Oris Krianto Sulaiman, Dicky Apdilah

Abstract


Stunting is still a serious public health problem in Indonesia, where the prevalence of this condition is 37.2%, up from 35.6% in 2019 and 36.8% in 2020. The length or height of a child who is short (dwarf) is below average for his age. Stunting has a negative impact on IQ deficiencies, infectious diseases, mental health problems, and child development. Toddlers with stunting cases are detected when their growth and development does not match their age, but currently there is no data grouping based on these criteria that is of concern to parents and posyandu cadres. Data can be grouped using the K-Means data mining technique. The K-Means algorithm is often used by researchers as a grouping procedure to ascertain whether children are stunted or not. 395 datasets are used in this research data. The Knowledge Discovery In Databases (KDD) approach, a comprehensive nontrivial procedure for detecting and recognizing patterns in data, underlies this research. Based on the variables of age, weight and height, this study aims to identify groups or clusters of stunting status in children under five. The best number of clusters with K = 2 was determined by the findings of this investigation. There are 392 children in cluster 0-Shanum, Rizka, Nurjanah, and others-and three toddlers in cluster 1-Ezra, M. Abidza, and Abd Mahmud. With a total of 287 stunted toddlers and 108 toddlers with normal development, the most ideal DBI value is 0.007 which is close to 0, this shows that the clusters under review provide quality clusters.


Keywords


K-Means Algorithm; Data Mining; KDD; Clusterization; Stunting.

Full Text:

PDF

References


Sari, I.P., Al-Khowarizmi, AK., and Batubara, I.H. (2021). Cluster Analysis Using K-Means Algorithm and Fuzzy C-Means Clustering For Grouping Students' Abilities In Online Learning Process. Journal of Computer Science, Information Technology and Telecommunication Engineering, 139-144.

Sari, I.P., Batubara, I.H, and Al-Khowarizmi, AK. (2021). Sensitivity Of Obtaining Errors In The Combination Of Fuzzy And Neural Networks For Conducting Student Assessment On E-Learning. International Journal of Economic, Technology and Social Sciences (Injects), 331-338.

G. Apriluana and S. Fikawati, "Analysis of Risk Factors for the Incidence of Stunting in Toddlers (0-59 Months) in Developing Countries and Southeast Asia," Health Research and Development Media, vol. 28, no. 4, pp. 247-256, Dec 2018, doi: 10.22435/mpk.v28i4.472.

T. Prasetiya, I. Ali, C. L. Rohmat, and O. Nurdiawan, "Classification of Toddler Stunting Status in Slangit Village Using the K-Nearest Neighbor Method," INFORMATICS FOR EDUCATORS AND PROFESSIONALS, vol. 4, no. 2, pp. 93-104, 2020.

Sari, I.P., and Batubara, I.H. (2021). Optimization of the FP-Growth Algorithm in Data Mining Techniques to Get the Electric Power Theft Pattern for the Development of Smart City. 2021 4th International Conference of Computer and Informatics Engineering (IC2IE), 293-298.

Ramadhani, F., Satria, A., and Sari, I.P. (2023). Implementation of Fuzzy K-Nearest Neighbor Method in Dengue Fever Disease Classification. Hello World Journal of Computer Science. 58-62.

Sari, I.P., Al-Khowarizmi, AK., Ramadhani, F., and Sulaiman, O.K. (2023). Implementation of the Selection Sort Algorithm to Sort Data in PHP Programming Language. Journal of Computer Science, Information Technology and Telecommunication Engineering.

Ramadhani, F., and Sari, I.P. (2021). Improving the Performance of Naïve Bayes Algorithm by Reducing the Attributes of Dataset Using Gain Ratio and Adaboost. 2021 International Conference on Computer Science and Engineering (IC2SE), 1-5.

H. Pohan et al., "Application of the K-Medoids Algorithm in Grouping Stunting Toddlers in Indonesia," JUKI: Journal of Computers and Informatics, 2021.

E. Irfiani, S. Sulistia Rani, J. Kamal Raya No, R. Road Barat Cengkareng West Jakarta, S. Nusa Mandiri Jl Kramat Raya No, and J. Pusat, "K-Means Clustering Algorithm to Determine Toddler Nutrition Value," 2018.

et al Gustientiedina, "Application of K-Means Algorithm for Clustering Drug Data at. RSUD Pekanbaru," 2018. Accessed: January 20, 2023. [Online]. Available at: http://repository.potensi-utama.ac.id/jspui/bitstream/123456789/4745/3/BAB%20II.pdf

U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, "Knowledge Discovery and Data Mining: Towards a Unifying Framework," 1996. [Online]. Available at: www.aaai.org

A. Dwi, N. Yadika, K. N. Berawi, and S. H. Nasution, "The Effect of Stunting on Cognitive Development and Learning Achievement," 2019.

Ministry of Health of the Republic of Indonesia 2020, "REGULATION OF THE MINISTER OF HEALTH OF THE REPUBLIC OF INDONESIA STANDARDS FOR CHILD ANTROPOMETRY."

R. A. Indraputra and R. Fitriana, "K-Means Clustering COVID-19 Data," Journal of Industrial Engineering, 2020.

ary good jiwandono, "Analysis of BPJS Health Class Grouping Using the K-Means Method," 2021.

Z. Nabila, A. Rahman Isnain, and Z. Abidin, "DATA MINING ANALYSIS FOR CLUSTERING COVID-19 CASES IN LAMPUNG PROVINCE WITH K-MEANS ALGORITHMA," Journal of Information Technology and Systems (JTSI), vol. 2, no. 2, pp. 100, 2021, [Online]. Available at: http://jim.teknokrat.ac.id/index.php/JTSI.

rahmawati, "Determining the Welfare Level of Central Kalimantan Province by Applying the K-Means Clustering Algorithm Using Rapidminer," 2023.

D. Pascalina, R. Widhiastono, and C. Juliane, "Measuring the Readiness of Smart City Digital Transformation Using Rapid Miner Application," Technomedia Journal, vol. 7, no. 3, pp. 293-302, Dec 2022, doi: 10.33050/tmj.v7i3.1914.

Beal, T., Tumilowicz, A., Sutrisna, A., Izwardy, D., & Neufeld, L. M. (2018). A review of child stunting determinants in Indonesia. Maternal and Child Nutrition, 14(4), 1-10.

https://doi.org/10.1111/mcn.12617

Byna, A., & Anisa, F. N. (2018). Backward Elimination to improve the Accuracy of Stunting Incidence with Support Vector Machine Algorithm Analysis. Health Dynamics, 9(2), 217-225.

Indraswari, R., Zainal Arifin, A., & Darlis, H. (2017). RBF Kernel Optimazation Method With Particle Swarm Optimization On SVM Using The Analysis Of Input Data'S Movement. Journal of Computer Science and Information, 13(3), 1576-1580.

https://doi.org/http://dx.doi.org/10.21609/jiki.v10i1.410 RBF

Isnain, A. R., Sakti, I. A., Alita, D., & Marga, N. S. (2021). Public Sentiment Analysis on Jakarta Government Lockdown Policy Using Svm Algorithm. Jdmsi, 2(1), 31-37. https://t.co/NfhnfMjtXw




DOI: https://doi.org/10.30596/jcositte.v4i2.15765

Refbacks

  • There are currently no refbacks.