IMPLEMENTATION OF THE DBSCAN ALGORITHM FOR CLUSTERING STUNTING PREVALENCE TYPOLOGY IN WEST JAVA, CENTRAL JAVA, AND EAST JAVA REGIONS

Keywords: Clustering, DBSCAN, KNN-Distance graph, Stunting prevalence, Typology

Abstract

Stunting, a condition where children are malnourished for a long period, causes growth failure in children. West Java, Central Java, and East Java are the 3 provinces with the highest prevalence of stunting in 2021. This study aims to group districts/cities in these provinces based on factors that influence stunting using the DBSCAN method (there has been no previous research using this method for this case), so the typology of stunting prevalence is implied. The group results can be valuable input for policy priorities in overcoming stunting. The study used the DBSCAN (Density-Based Spatial Clustering of Application with Noise) method, which can also detect noises (outliers). The determination of eps and MinPts is based on the average value of the distance from each data to its closest neighbor. The distance obtained then was used in the KNN algorithm to determine eps and MinPts parameters. Clustering is done using standardized data and DBSCAN parameters obtained from the k-dist plot, eps is 1.92, and MinPts is 2. The validation test used is the silhouette coefficient to determine the goodness of the cluster results. The clustering results show that there are 2 clusters and 1 noise that have special characteristics related to factors that influence the prevalence of stunting. Cluster 1 consisted of 97 districts/cities and was characterized by a high percentage of infants under 6 months receiving exclusive breastfeeding and the lowest average per capita household expenditure. Cluster 2 (Bekasi City and Depok City) was characterized by the lowest percentage of households with proper health facilities and infants aged 0-59 months receiving complete immunization. The noise (high stunting prevalence) in Bandung City is characterized by the lowest percentage of households having proper sanitation.

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References

Kemenkes RI, BUKU SAKU HASIL STUDI STATUS GIZI INDONESIA (SSGI) TINGKAT NASIONAL, PROVINSI, DAN KABUPATEN/KOTA TAHUN 2021. Jakarta, 2021. doi: 10.36805/bi.v2i1.301.

R. R. Akbar, W. Kartika, and M. Khairunnisa, “THE EFFECT OF STUNTING ON CHILD GROWTH AND DEVELOPMENt,” Sci. J., vol. 2, no. 4, pp. 153–160, 2023, doi: https://doi.org/10.56260/sciena.v2i4.118.

C. R. Montenegro et al., “THE PEDIATRIC GLOBAL BURDEN OF STUNTING: FOCUS ON LATIN AMERICA,” Lifestyle Med., vol. 3, no. 3, pp. 1–11, 2022, doi: https://doi.org/10.1002/lim2.67.

S. Wulandari, “CLUSTERING INDONESIAN PROVINCES ON PREVALENCE OF STUNTING TODDLERS USING AGGLOMERATIVE HIERARCHICAL CLUSTERING,” Fakt. Exacta, vol. 16, no. 2, 2023, doi: https://doi.org/10.30998/faktorexacta.v16i2.17186.

A. J. Pitoyo, A. Saputri, R. E. Agustina, and T. Handayani, “ANALYSIS OF DETERMINAN OF STUNTING PREVALENCE AMONG STUNTED TODDLERS IN INDONESIA,” Populasi, vol. 30, no. 1, p. 36, 2022, doi: https://doi.org/10.22146/jp.75796.

Azizah Andzar Ridwanah, Hario Megatsari, Agung Dwi Laksono, and Mursyidul Ibad, “FACTORS RELATED TO STUNTED IN EAST JAVA PROVINCE IN 2019: AN ECOLOGICAL ANALYSIS,” Med. Leg. Updat., vol. 21, no. 2, pp. 230–235, 2021, doi: https://doi.org/10.37506/mlu.v21i2.2678.

T. Beal, A. Tumilowicz, A. Sutrisna, D. Izwardy, and L. M. Neufeld, “A REVIEW OF CHILD STUNTING DETERMINANTS IN INDONESIA,” Matern. Child Nutr., vol. 14, no. 4, pp. 1–10, 2018, doi: https://doi.org/10.1111/mcn.12617.

L. I. P. Ariati, “FAKTOR-FAKTOR RESIKO PENYEBAB TERJADINYA STUNTING PADA BALITA USIA 23-59 BULAN,” OKSITOSIN J. Ilm. Kebidanan, vol. 6, no. 1, pp. 28–37, 2019, doi: https://doi.org/10.35316/oksitosin.v6i1.341.

F. O. Aridiyah, N. Rohmawati, and M. Ririanty, “FAKTOR-FAKTOR YANG MEMPENGARUHI KEJADIAN STUNTING PADA ANAK BALITA DI WILAYAH PEDESAAN DAN PERKOTAAN (THE FACTORS AFFECTING STUNTING ON TODDLERS IN RURAL AND URBAN AREAS),” e-Jurnal Pustaka Kesehat., vol. 3, no. 1, 2015, doi: 10.1007/s11746-013-2339-4.

J. Jumhur, “THE EFFECT OF ECONOMIC GROWTH AND POVERTY ON STUNTING IN INDONESIA,” J. Perspekt. Pembiayaan dan Pembang. Drh., vol. 11, no. 6, pp. 433–448, 2024, doi: https://doi.org/10.22437/ppd.v11i6.26871.

H. Hatijar, “The Incidence of Stunting in Infants and Toddlers,” J. Ilm. Kesehat. Sandi Husada, vol. 12, no. 1, pp. 224–229, 2023, doi: 10.35816/jiskh.v12i1.1019.

Taufik Hidayat, Mohamad Jajuli, and Susilawati, “CLUSTERING DAERAH RAWAN STUNTING DI JAWA BARAT MENGGUNAKAN ALGORITMA K-MEANS,” INFOTECH J. Inform. Teknol., vol. 4, no. 2, pp. 137–146, 2023, doi: https://doi.org/10.37373/infotech.v4i2.642.

S. S. Nagari and L. Inayati, “IMPLEMENTATION OF CLUSTERING USING K-MEANS METHOD TO DETERMINE NUTRITIONAL STATUS,” J. Biometrika dan Kependud., vol. 9, no. 1, pp. 62–68, 2020, doi: https://doi.org/10.20473/jbk.v9i1.2020.62-68.

M. A. Robbani, G. Firmansyah, and A. M. Widodo, “CLUSTERING OF CHILD STUNTING DATA IN TANGERANG REGENCY USING COMPARISON OF K-MEANS , HIERARCHICAL CLUSTERING AND DBSCAN METHODS,” vol. 2, no. 2015, pp. 3105–3112, 2024.

M. Bariklana and A. Fauzan, “IMPLEMENTATION OF THE DBSCAN METHOD FOR CLUSTER MAPPING OF EARTHQUAKE SPREAD LOCATION,” BAREKENG J. Ilmu Mat. dan Terap., vol. 17, no. 2, pp. 0867–0878, 2023, doi: https://doi.org/10.30598/barekengvol17iss2pp0867-0878.

T. D. Harjanto, A. Vatresia, and R. Faurina, “ANALISIS PENETAPAN SKALA PRIORITAS PENANGANAN BALITA STUNTING MENGGUNAKAN METODE DBSCAN CLUSTERING,” J. Rekursif, vol. 9, no. 1, pp. 30–42, 2021.doi: https://doi.org/10.33369/rekursif.v9i1.14982

N. P. Sutramiani, I. M. T. Arthana, P. F. Lampung, S. Aurelia, M. Fauzi, and I. W. A. S. Darma, “THE PERFORMANCE COMPARISON OF DBSCAN AND K-MEANS CLUSTERING FOR MSMES GROUPING BASED ON ASSET VALUE AND TURNOVER,” J. Inf. Syst. Eng. Bus. Intell., vol. 10, no. 1, pp. 13–24, 2024, doi: https://doi.org/10.20473/jisebi.10.1.13-24.

B. Tan, M. Steinbach, A. Karpatne, and V. Kumar, CLUSTER ANALYSIS: BASIC CONCEPT AND ALGORITHMS. New York: Pearson Education, 2019.

V. Wulandari, Y. Syarif, Z. Alfian, M. A. Althof, and M. Mufidah, “COMPARISON OF DENSITY-BASED SPATIAL CLUSTERING OF APPLICATIONS WITH NOISE (DBSCAN), K-MEANS AND X-MEANS ALGORITHMS ON SHOPPING TRENDS DATA,” IJATIS Indones. J. Appl. Technol. Innov. Sci., vol. 1, no. 1, pp. 1–8, 2024, doi: https://doi.org/10.57152/ijatis.v1i1.1135.

M. Nishom, “PERBANDINGAN AKURASI EUCLIDEAN DISTANCE, MINKOWSKI DISTANCE, DAN MANHATTAN DISTANCE PADA ALGORITMA K-MEANS CLUSTERING BERBASIS CHI-SQUARE,” J. Inform. J. Pengemb. IT, vol. 4, no. 1, pp. 20–24, 2019, doi: https://doi.org/10.30591/jpit.v4i1.1253.

C. Andrade, “Z SCORES, STANDARD SCORES, AND COMPOSITE TEST SCORES EXPLAINED,” Indian J. Psychol. Med., vol. 43, no. 6, pp. 555–557, 2021, doi: https://doi.org/10.1177/02537176211046525.

A. Migliore and C. Rossi-Lamastra, “CLUSTER ANALYSIS: GROUPING WORKERS BY WORK LOCATION CHOICE,” Methodol. Approaches Work. Res. Manag., pp. 95–107, 2023, doi: https://doi.org/10.1201/9781003289845-7.

Suyanto, DATA MINING UNTUK KLASIFIKASI DAN KLASTERISASI DATA, Penerbit I. Bandung, 2019.

I. Syahzaqi, M. Effendi, H. Rahmawati, H. Kuswanto, and S. Sediono, “GROUPING PROVINCES IN INDONESIA BASED ON THE NUMBER OF VILLAGES AFFECTED BY ENVIROMENTAL POLLUTION WITH K-MEDOIDS, FUZZY C-MEANS, AND DBSCAN,” BAREKENG J. Ilmu Mat. dan Terap., vol. 18, no. 2, pp. 0923–0936, 2024, doi: https://doi.org/10.30598/barekengvol18iss2pp0923-0936.

P. Silitonga, “ANALISIS POLA PENYEBARAN PENYAKIT PASIEN PENGGUNA BADAN PENYELENGGARA JAMINAN SOSIAL (BPJS) KESEHATAN DENGAN MENGGUNAKAN METODE DBSCAN CLUSTERING,” TIMES, vol. V, no. 1, pp. 36–39, 2016.doi: https://doi.org/10.51351/jtm.5.1.2016482

M. S. Amir, “HOW TO DETERMINE EPSILON AND MINPTS PARAMETERS OF DBSCAN CLUSTERING,” Sefidian Academy, [Online]. [Online]. Available: https://www.sefidian.com/2022/12/18/how-to-determine-epsilon-and-minpts-parameters-of-dbscan-clustering/

A. Vysala and D. J. Gomes, “EVALUATING AND VALIDATING CLUSTER RESULTS,” pp. 37–47, 2020, doi: https://doi.org/10.5121/csit.2020.100904.

N. Kaoungku, K. Suksut, R. Chanklan, K. Kerdprasop, and N. Kerdprasop, “THE SILHOUETTE WIDTH CRITERION FOR CLUSTERING AND ASSOCIATION MINING TO SELECT IMAGE FEATURES,” Int. J. Mach. Learn. Comput., vol. 8, no. 1, pp. 69–73, 2018, doi: https://doi.org/10.18178/ijmlc.2018.8.1.665.

H. Řezanková, “DIFFERENT APPROACHES TO THE SILHOUETTE COEFFICIENT CALCULATION IN CLUSTER EVALUATION,” 21st Int. Sci. Conf. AMSE, no. September, pp. 1–10, 2018.

L. S. Rakasiwi, “PENGARUH FAKTOR DEMOGRAFI DAN SOSIAL EKONOMI TERHADAP STATUS KESEHATAN INDIVIDU DI INDONESIA,” Kaji. Ekon. dan Keuang., vol. 5, no. 2, pp. 146–157, 2021, doi: https://doi.org/10.31685/kek.v5i2.1008.

Published
2025-07-01
How to Cite
[1]
B. Sumargo, “IMPLEMENTATION OF THE DBSCAN ALGORITHM FOR CLUSTERING STUNTING PREVALENCE TYPOLOGY IN WEST JAVA, CENTRAL JAVA, AND EAST JAVA REGIONS”, BAREKENG: J. Math. & App., vol. 19, no. 3, pp. 1779-1790, Jul. 2025.