Integrasi K-Modes dalam Analisis Data Gizi Balita untuk Model Klasifikasi Risiko Stunting
-
Desyanti Desyanti
*
Institut Teknologi dan Bisnis Riau Pesisir, Dumai,
Indonesia
- Reno Renaldi Universitas Hang Tuah, Pekanbaru, Indonesia
- Mesran Mesran Sekolah Tinggi Ilmu Manajemen Sukma, Medan, Indonesia (*) Corresponding Author
Abstract
The nutritional status of toddlers is an important indicator in assessing child growth and development and is closely related to the risk of stunting. However, the process of recording and classifying nutritional status at the Bukit Kapur Community Health Center is still done manually, making it prone to analysis delays and data processing errors. This study aims to implement the K-Modes algorithm in classifying toddler nutritional status based on categorical data, such as age, weight, and height. Toddler data were collected from the Bukit Kapur Community Health Center and underwent pre-processing, data transformation, and the application of the K-Modes algorithm to determine toddler nutritional groups. The results showed that the K-Modes algorithm was able to group toddler data into three main categories: well-nourished, at-risk of overnutrition, and overnourished. The majority of toddlers fell into the well-nourished category (98 toddlers), while only a small proportion fell into the at-risk of overnutrition and overnourished categories (1 toddler each). These findings indicate that the K-Modes method is effective in classifying toddler nutritional status based on categorical data and can assist health workers in monitoring child growth and preventing stunting.
Downloads
References
T. P. Puguh Santoso, “The Analysis of Factors that Influence Stunting,” J. Ners Midwifery, vol. 11, no. 1, pp. 200–208, 2024, doi: 10.37598/jam.v11i1.1039.
et al Enikmawati, S.M., “Correlation between Mother’s Knowledge of Balanced Nutrition and Stunting Prevention in Toddlers.,” J. Ners dan Kebidanan, vol. 11, no. 1, pp. 87–96, 2024, doi: 10.26699/jnk.v11i1.ART.p87-96.
A. I. Aliyah, “Analisis Asupan Protein dan Energi pada Balita,” in Prosiding Semnas Unimus, 2023. [Online]. Available: https://prosiding.unimus.ac.id/index.php/semnas/article/viewFile/1578/1581
N. Aini, “Analisis Tingkat Konsumsi Zat Gizi terhadap Kejadian Gizi Kurang pada Balita di Puskesmas Jelbuk, Kabupaten Jember,” HIJP Heal. Inf. J. Penelit., vol. 11, no. 2, 2019, [Online]. Available: https://myjurnal.poltekkes-kdi.ac.id/index.php/hijp/article/download/140/88/308
S. Juliyusman and Afrinis, Nur and Syukrianti, “Hubungan Asupan Energi Protein dengan Kejadian Stunting pada Balita di Desa IV Koto Setingkai,” SEHAT J. Kesehat. Terpadu, vol. 3, no. 4, 2023, doi: 10.31004/sjkt.v2i4.23194.
R. D. Fitriyani, F. and Aisyah, “Edukasi Nutrisi dan Optimalisasi Tumbuh Kembang Balita pada Masa Pandemi COVID-19 di Desa Pekajangan Kabupaten Pekalongan,” J. ABDINUS J. Pengabdi. Nusant., vol. 6, no. 1, pp. 91–98, 2022, doi: 10.29407/ja.v6i1.16100.
A. N. Sari, D. H. Qurotu’ain, F. Harianto, and S. Z. Jannah, “Regional Mapping in Bangkalan District Based on Potential Indicators of Total Stunting Using K-Mode Cluster Algorithm,” Media Gizi Indones., vol. 17, no. 1SP, pp. 76–82, 2022, doi: 10.20473/mgi.v17i1SP.76-82.
D. A. Indah Purnama Sari, Al-Khowarizmi, Oris Krianto Sulaiman, “Implementation of Data Classification Using K-Means Algorithm in Clustering Stunting Cases,” J. Comput. Sci. Inf. Technol. Telecommun. Eng., vol. 4, no. 2, pp. 402–412, 2023, doi: 10.30596/jcositte.v4i2.15765.
Arda, “Application of K-Prototypes Clustermix for Clustering Risk Factors of Diabetes,” J. Apl. Sains Data, vol. 1, no. 1, pp. 40–49, 2025, doi: 10.33005/jasid.v1i1.9.
F. Afdhal and R. Arsi, “Faktor-Faktor Yang Mempengaruhi Status Gizi Balita di Wilayah Kerja Puskesmas Prabumulih Timur Tahun 2022,” J. Kesehat. Terap., vol. 10, no. 1, pp. 82–95, 2023, doi: 10.54816/jk.v10i1.593.
M. Ratnawati, R. Probowati, M. Sawitri Prihatini, S. F. Ningtyas, and A. F. Ulfa, “Pemberian Makanan Tambahan Modifikasi Terhadap Status Gizi Balita,” J. Heal. Sains, vol. 4, no. 2, pp. 104–111, 2023, doi: 10.46799/jhs.v4i2.801.
M. Hildha, H. Pratiwi, and E. Zukhronah, “Application of K-Prototypes Clustermix Algorithm for Clustering Risk Factors of Diabetes Disease,” J. Apl. Sains Data, vol. 01, no. 01, pp. 40–49, 2025, [Online]. Available: https://doi.org/10.33005/jasid.v1i1.9
K. H. Izzuddin and A. W. Wijayanto, “Pemodelan Clustering Ward, K-Means, Diana, dan PAM dengan PCA untuk Karakterisasi Kemiskinan Indonesia Tahun 2021,” Komputika J. Sist. Komput., vol. 13, no. 1, pp. 41–53, 2024, doi: 10.34010/komputika.v13i1.10803.
E. A. Kusuma, A. Dharmawati, and Nisrina, “Implementasi Algoritma K-Means Dalam Pengelompokan Penerima Bantuan Bedah Rumah,” Jutisi J. Ilm. Tek. Inform. dan Sist. Inf., vol. 13, no. 1, pp. 775–784, 2024, [Online]. Available: http://ojs.stmik-banjarbaru.ac.id/index.php/jutisi/article/view/2091
et al Pratama, Y.M.W., “Pengelompokan Mahasiswa Berdasarkan Persepsi Kesehatan Menggunakan Gaussian Mixture,” JNATIA Udayana, vol. 11, no. 3, pp. 153–160, 2024, doi: 10.24843/JNATIA.2024.v11.i03.p02.
N. Suaib et al., “Hubungan Pengetahuan Ibu Tentang Mp-Asi Dan Jenis Pemberian Mp-Asi Dengan Status Gizi Balita Umur 6-24 Bulan,” J. Sehat Mandiri, vol. 19, no. 2, pp. 278–288, 2024.
D. Meilani, M. Masjkur, and F. M. Afendi, “Grouping Provinces in Indonesia Based on the Causes of Stunting Variables using Hierarchical Clustering Analysis,” Indones. J. Stat. Its Appl., vol. 7, no. 1, pp. 32–43, 2023, doi: 10.29244/ijsa.v7i1p32-43.
C. Syalom, U. Khaira, and D. Arsa, “Clustering Daerah Rawan Stunting Di Indonesia Menggunakan Algoritma K-Means,” Djtechno J. Teknol. Inf., vol. 6, no. 1, pp. 389–401, 2025, doi: 10.46576/djtechno.v6i1.6223.
M. M. Desyanti Desyanti, Welly Desriyati, “Implementasi Metode K-Means Clustering dalam Mengukur Tingkat Gizi Balita Berdasarkan Z-Score,” Build. Informatics, Technol. Sci., vol. 6, no. 3, pp. 1808–1817, 2024, doi: 10.47065/bits.v6i3.6177.
N. Hasdyna, R. K. Dinata, Rahmi, and T. I. Fajri, “Hybrid Machine Learning for Stunting Prevalence: A Novel Comprehensive Approach to Its Classification, Prediction, and Clustering Optimization in Aceh, Indonesia,” Informatics, vol. 11, no. 4. 2024. doi: 10.3390/informatics11040089.
M. N. Rahman, M. S., Ferdous, J., & Hoque, “Application of K-Modes Clustering in Healthcare Data Mining,” J. Health Inform. Dev. Ctries., vol. 16, no. 2, pp. 45–56, 2022, doi: https://doi.org/10.48550/arXiv.2205.01234.
F. T. Nurhayati S, “K-Modes Algorithm for Categorizing Health Data: Case Study on Toddler Nutrition,” J. Teknol. dan Sist. Inf., vol. 9, no. 2, pp. 75–84, 2021, doi: https://doi.org/10.33364/jtsi.v9i2.1762.
D. Yuliana, R., Santoso, A., & Putri, “Data Preprocessing Techniques for Improving Categorical Clustering Performance,” Indones. J. Data Sci., vol. 4, no. 1, pp. 25–33, 2023, doi: https://doi.org/10.11591/ijds.v4i1.18956.
D. Putri Kartikasari, Y. A., Agus Pranoto, Y., & Rudhistiar, “Penerapan Metode K-Modes untuk Proses Penentuan Penerima Bantuan Langsung Tunai (BLT).,” JATI (Jurnal Mhs. Tek. Inform., vol. 5, no. 1, pp. 389–397, 2021, doi: https://doi.org/10.36040/jati.v5i1.3300.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Integrasi K-Modes dalam Analisis Data Gizi Balita untuk Model Klasifikasi Risiko Stunting
Pages: 1414-1421
Copyright (c) 2025 Desyanti Desyanti, Reno Renaldi, Mesran Mesran
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
