IMPLEMENTATION OF K-MEANS AND FUZZY C-MEANS CLUSTERING FOR MAPPING TODDLER STUNTING CASES IN GUNUNGKIDUL DISTRICT
Abstract
Gunungkidul Regency has the highest prevalence of stunted toddlers in the Special Region of Yogyakarta. This study aims to describe the optimal clustering results of toddler stunting cases using the k-means and fuzzy c-means methods and to describe the characteristic of the mapping results of stunting-prone areas for toddlers in Gunungkidul Regency for the years 2020 – 2022. This study maps stunting-prone areas for toddlers across 30 community health centers in Gunungkidul Regency from 2020 to 2022, with variables including the percentage of babies with low birth weight, babies born stunted, babies receiving health services, stunted toddlers, toddlers receiving health services, babies given exclusive breastfeeding, poor couples of reproductive ages, and families with adequate drinking water. The k-means clustering method determines cluster membership using the distance between objects and centroids, while the fuzzy c-means method uses the degree of membership. Cluster evaluation uses the silhouette coefficient, Calinski-Harabasz index, Davies-Bouldin index, and Dunn index to obtain optimal clustering results. The mapping results are presented as a stunting vulnerability map. The findings indicate that the optimal number of clusters is two, with the fuzzy c-means method proving more optimal than the k-means method based on evaluation scores. In 2020, there were 23 community health centers in cluster 0 and 7 in cluster 1. In 2021, there were 21 community health centers in cluster 0 and 9 in cluster 1. In 2022, there were 18 community health centers in cluster 0 and 12 in cluster 1. Generally, community health centers in cluster 0 are less optimal in specific nutrition interventions, such as for infants and toddlers. In contrast, those in cluster 1 are less optimal in sensitive nutrition interventions, such as poverty and water adequacy.
Downloads
References
K. A. Atalell, M. A. Techane, B. Terefe, and T. T. Tamir, “Mapping stunted children in Ethiopia using two decades of data between 2000 and 2019. A geospatial analysis through the Bayesian approach,” J Health Popul Nutr, vol. 42, no. 1, pp. 1–9, Dec. 2023, doi: 10.1186/s41043-023-00412-3.
W. Sartika, S. Suryono, and A. Wibowo, “Information System for Evaluating Specific Interventions of Stunting Case Using K-means Clustering,” in E3S Web of Conferences, EDP Sciences, Nov. 2020, pp. 1–10. doi: 10.1051/e3sconf/202020213003.
N. Izza, W. Purnomo, and IMahmudah, “Implementation of the K-means Clustering Method on Stunting Case in Indonesia,” International Journal of Advances in Scientific Research and Engineering, vol. 5, no. 6, pp. 103–107, 2019, doi: 10.31695/ijasre.2019.33258.
M. Handayani and M. F. L. Sibuea, “Performance Analysis of Clustering Models Based on Machine Learning in Stunting Data Mapping,” JURTEKSI (Jurnal Teknologi dan Sistem Informasi), vol. 9, no. 4, pp. 715–720, Sep. 2023, doi: 10.33330/jurteksi.v9i4.2770.
I. P. Sari, Al-Khowarizmi, O. K. Sulaiman, and D. Apdilah, “Implementation of Data Classification Using K-Means Algorithm in Clustering Stunting Cases,” Journal of Computer Science, Information Technology and Telecommunication Engineering, vol. 4, no. 2, pp. 402–412, Sep. 2023, doi: 10.30596/jcositte.v4i2.15765.
M. Ula, A. F. Ulva, Mauliza, I. Sahputra, and Ridwan, “Implementation of Machine Learning in Determining Nutritional Status using the Complete Linkage Agglomerative Hierarchical Clustering Method,” Jurnal Mantik, vol. 5, no. 3, pp. 1910–1914, 2021.
A. Aswi, B. Poerwanto, Sudarmin, and Nurwan, “Bayesian Spatial Modelling of Stunting Cases in South Sulawesi Province: Influential Factors and Relative Risk,” in Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023), 2023, pp. 87–96. doi: 10.2991/978-94-6463-332-0_11.
N. Istiqomah, H. Wijayanto, and F. M. Afendi, “Clustering Districts Based on Influencing Factors of Child Undernutrition (Stunting),” in Proceeding of International Conference On Research, Implementation And Education Of Mathematics And Sciences 2015, 2015, pp. 239–244.
H. Jamaludin and B. Y. Dharmahita, “K-Means Clustering Analysis on the Distribution of Stunting Cases In Mojokerto Regency in June 2022,” Jurnal Media Pratama, vol. 17, no. 1, pp. 33–44, 2023, [Online]. Available: https://data.go.id
P. W. Sudarmadji and C. E. B. Bire, “Implementation of K-means Clustering Algoritm to Determine Stunted Status in Children Under Two Years Old,” in ICESC 2019, European Alliance for Innovation n.o., Dec. 2019. doi: 10.4108/eai.18-10-2019.2289979.
B. Khura et al., “Mapping Concurrent Wasting and Stunting Among Children Under Five in India: A Multilevel Analysis,” Int J Public Health, vol. 68, pp. 1–11, 2023, doi: 10.3389/ijph.2023.1605654.
R. Hemalatha et al., “Mapping of variations in child stunting, wasting and underweight within the states of India: the Global Burden of Disease Study 2000–2017,” EClinicalMedicine, vol. 22, pp. 1–16, May 2020, doi: 10.1016/j.eclinm.2020.100317.
K. Y. Ahmed, K. E. Agho, A. Page, A. Arora, and F. A. Ogbo, “Mapping Geographical Differences and Examining the Determinants of Childhood Stunting in Ethiopia: A Bayesian Geostatistical Analysis,” Nutrients, vol. 13, no. 6, pp. 1–21, Jun. 2021, doi: 10.3390/nu13062104.
L. E. Suranny and F. C. Maharani, “Mapping of Community Empowerment in Prevention Stunting in Kabupaten Wonogiri Through ‘Sego Sak Ceting,’” in IOP Conference Series: Earth and Environmental Science, IOP Publishing Ltd, Nov. 2021, pp. 1–11. doi: 10.1088/1755-1315/887/1/012035.
D. S. Effendy, P. Prangthip, N. Soonthornworasiri, P. Winichagoon, and K. Kwanbunjan, “Nutrition education in Southeast Sulawesi Province, Indonesia: A cluster randomized controlled study,” Matern Child Nutr, vol. 16, no. 4, pp. 1–14, Oct. 2020, doi: 10.1111/mcn.13030.
K. Y. Ahmed, A. G. Ross, S. M. Hussien, K. E. Agho, B. O. Olusanya, and F. A. Ogbo, “Mapping Local Variations and the Determinants of Childhood Stunting in Nigeria,” Int J Environ Res Public Health, vol. 20, no. 4, pp. 1–16, Feb. 2023, doi: 10.3390/ijerph20043250.
S. M. Rambe and Suendri, “Geographic Information System Mapping Risk Factors Stunting Using Methods Geographically Weighted Regression,” Journal of Applied Geospatial Information, vol. 7, no. 2, pp. 1075–1079, 2023, [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAGI
A. Iriany, W. Ngabu, D. Arianto, and A. Putra, “Classification of Stunting Using Geographically Weighted Regression-Kriging Case Study: Stunting in East Java,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 1, pp. 0495–0504, Apr. 2023, doi: 10.30598/barekengvol17iss1pp0495-0504.
E. Selviyanti, M. C. Roziqin, D. S. H. Putra, and M. S. Noor, “Intelligent Application of Stunting Monitoring and Mapping Systems (Smart Ting) in Toddlers Based on Android in Jember,” in 2nd International Conference on Social Science, Humanity and Public Health (ICOSHIP 2021), 2022, pp. 147–157.
F. A. Johnson, “Spatiotemporal clustering and correlates of childhood stunting in Ghana: Analysis of the fixed and nonlinear associative effects of socio-demographic and socio-ecological factors,” PLoS One, vol. 17, no. 2, pp. 1–22, 2022, doi: 10.1371/journal.pone.0263726.
M. A. Ramdani and S. Abdullah, “Application of partitioning around medoids cluster for analysis of stunting in 100 priority regencies in Indonesia,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Jan. 2021, pp. 1–10. doi: 10.1088/1742-6596/1722/1/012097.
S. S. Nagari and L. Inayati, “Implementation of Clustering Using K-means Method to Determine Nutritional Status,” Jurnal Biometrika dan Kependudukan, vol. 9, no. 1, pp. 62–68, Jun. 2019, doi: 10.20473/jbk.v9i1.2020.62-68.
I. K. Hasan, Nurwan, N. Falaq, and M. R. F. Payu, “Optimization Fuzzy Geographically Weighted Clustering with Gravitational Search Algorithm for Factors Analysis Associated with Stunting,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 1, pp. 120–128, Feb. 2023, doi: 10.29207/resti.v7i1.4508.
S. P. Tamba, M. D. Batubara, W. Purba, M. Sihombing, V. M. M. Siregar, and J. Banjarnahor, “Book data grouping in libraries using the k-means clustering method,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Sep. 2019, pp. 1–7. doi: 10.1088/1742-6596/1230/1/012074.
R. D. Christyanti, D. Sulaiman, A. P. Utomo, and M. Ayyub, “Implementation of Fuzzy C-Means in Clustering Stunting Prone Areas,” International Journal of Natural Science and Engineering, vol. 6, no. 3, pp. 110–121, Oct. 2022, doi: 10.23887/ijnse.v6i3.53048.
M. M. Saleck, A. El Moutaouakkil, M. Moucouf, M. Bouchaib, H. Samira, and J. Zineb, “Breast Mass Segmentation Using a Semi-automatic Procedure Based on Fuzzy C-means Clustering,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 16, no. 2, pp. 665–672, Apr. 2018, doi: 10.12928/TELKOMNIKA.v16i2.6193.
D. P. Sari, D. Rosadi, A. R. Effendie, and Danardono, “K-means and bayesian networks to determine building damage levels,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 17, no. 2, pp. 719–727, 2019, doi: 10.12928/TELKOMNIKA.V17I2.11756.
C. C. Aggarwal and C. K. Reddy, Data Clustering Algorithms and Applications. 2014.
S. H. Gebreyesus, D. H. Mariam, T. Woldehanna, and B. Lindtjørn, “Local spatial clustering of stunting and wasting among children under the age of 5 years: Implications for intervention strategies,” Public Health Nutr, vol. 19, no. 8, pp. 1417–1427, Jun. 2015, doi: 10.1017/S1368980015003377.
A. K. Rahmansyah, A. T. S. Aziz, N. Novianto, and D. Rolliawati, “Perbandingan Algoritma K-Means dan Fuzzy C-Means Untuk Clustering Puskesmas Berdasarkan Gizi Balita Surabaya,” Jurnal PROCESSOR, vol. 18, no. 1, pp. 83–88, Apr. 2023, doi: 10.33998/processor.2023.18.1.696.
“Sistem Informasi Komunikasi Data Kesehatan Keluarga.” Accessed: Jun. 27, 2024. [Online]. Available: https://kesgadiy.web.id/lihat-data
J. Mann and S. Truswell, Essentials of Human Nutrition. 2012.
S. A. S. Mahayana, E. Chundrayetti, and Yulistini, “Faktor Risiko yang Berpengaruh terhadap Kejadian Berat Badan Lahir Rendah di RSUP Dr. M. Djamil Padang,” Jurnal Kesehatan Andalas, vol. 4, no. 3, pp. 664–673, 2015.
Badan Kependudukan dan Keluarga Berencana Nasional Republik Indonesia, “Peraturan Badan Kependudukan dan Keluarga Berencana Nasional Republik Indonesia Nomor 1 Tahun 2023, tentang Pemenuhan Kebutuhan Alat Dan Obat Kontrasepsi Bagi Pasangan Usia Subur Dalam Pelayanan Keluarga Berencana,” 2023.
Kementerian Kesehatan Republik Indonesia, Buku Panduan Untuk Siswa Aksi Bergizi. 2019.
A. D. Mengistu, “The Effects of Segmentation Techniques in Digital Image Based Identification of Ethiopian Coffee Variety,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 16, no. 2, pp. 713–717, Apr. 2018, doi: 10.12928/TELKOMNIKA.v16i2.8419.
W. W. Pribadi, A. Yunus, and A. S. Wiguna, “Perbandingan Metode K-Means Euclidean Distance dan Manhattan Distance pada Penentuan Zonasi COVID-19 di Kabupaten Malang,” Jurnal Mahasiswa Teknik Informatika, vol. 6, no. 2, pp. 493–500, 2022.
D. L. Rahakbauw, V. Y. I. Ilwaru, and M. H. Hahury, “Implementasi Fuzzy C-means Clustering dalam Penentuan Beasiswa,” BAREKENG Jurnal Ilmu Matematika dan Terapan, vol. 11, no. 1, pp. 1–11, 2017.
Y. Yang and S. Huang, “Image Segmentation by Fuzzy C-means Clustering Algorithm with a Novel Penalty Term,” Computing and Informatics, vol. 26, pp. 17–31, 2007.
G. R. Suraya and A. W. Wijayanto, “Comparison of Hierarchical Clustering, K-means, K-medoids, and Fuzzy C-means Methods in Grouping Provinces in Indonesia According to the Special Index for Handling Stunting,” Indonesian Journal of Statistics and Its Applications, vol. 6, no. 2, pp. 180–201, Aug. 2022, doi: 10.29244/ijsa.v6i2p180-201.
B. Desgraupes, “Clustering Indices,” 2017, pp. 1–34.
J. Baarsch and M. E. Celebi, “Investigation of Internal Validity Measures for K-Means Clustering,” in Proceedings of the International MultiConference of Engineers and Computer Scientist, Newswood Ltd., 2012, pp. 1701–1706.
T. Zulyanti and Noeryanti, “Perbandingan Pengelompokan Usaha Mikro Kecil dan Menengah di Kabupaten Klaten Tahun 2019 dengan Metode K-Means dan Clustering Large Application,” Jurnal Statistika Industri dan Komputasi, vol. 7, no. 1, pp. 46–59, 2022.
Mundir, Statistik Pendidikan Pengantar Analisis Data Untuk Penulisan Skripsi dan Tesis. 2012.
M. R. Anggraeni, U. Yudatama, and Maimunah, “Clustering Prevalensi Stunting Balita Menggunakan Agglomerative Hierarchical Clustering,” JURNAL MEDIA INFORMATIKA BUDIDARMA , vol. 7, no. 1, pp. 351–359, 2023, doi: 10.30865/mib.v7i1.5501.
Copyright (c) 2024 Bintang Wira Mahardika, Agus Maman Abadi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement 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 websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
1.gif)
