Integrasi Principal Component Analysis dan DBSCAN untuk Klasterisasi Tingkat Keparahan Stunting pada Balita
Azzah Aulia Syarif(1); Rizki Yusliana Bakti(2*); Muhammad Faisal(3); Nini Apriani Rumata(4); Titik Khawa Abd Rahman(5); Andi Makbul Syamsuri(6); M. Agusalim(7);
(1) Department of Informatics, Universitas Muhammadiyah Makassar, Makassar, Indonesia
(2) Department of Informatics, Universitas Muhammadiyah Makassar, Makassar, Indonesia
(3) Department of Informatics, Universitas Muhammadiyah Makassar, Makassar, Indonesia
(4) Regional and Urban Planning, Universitas Muhammadiyah Makassar, Makassar, Indonesia
(5) School Science and Technology, Asia E University, Selangor, Malaysia
(6) Water Resources Engineering, Universitas Muhammadiyah Makassar, Makassar, Indonesia
(7) Water Resources Engineering, Universitas Muhammadiyah Makassar, Makassar, Indonesia
(*) Corresponding Author
AbstractStunting masih menjadi masalah kesehatan masyarakat yang serius. Variasi karakteristik antropometri pada balita stunting membuat satu kategori klasterisasi tidak cukup merepresentasikan kondisi pertumbuhan secara menyeluruh. Penelitian ini mengusulkan integrasi Principal Component Analysis (PCA) dan DBSCAN untuk mengidentifikasi subkelompok keparahan stunting pada balita di Puskesmas Turikale (Agustus 2025; n = 406). Setelah pembersihan dan standardisasi usia, PCA diterapkan pada HAZ, WAZ, WHZ, dan usia saat pengukuran, menghasilkan tiga komponen utama (PC1–PC3) yang menjelaskan 99.35% variasi data. DBSCAN pada ruang PCA (ε = 0.60; MinPts = 5; Euclidean) menghasilkan tiga klaster dengan 12.56% noise yang direalokasi menggunakan nearest-centroid agar seluruh sampel berlabel. Hasil menunjukkan tiga subkelompok: stunting sedang dengan underweight pada usia lebih tua (n = 357), stunting sedang disertai risiko gizi lebih pada usia lebih muda (n = 24), dan stunting berat pada usia dini (n = 25). Dibandingkan K-medoids dan Fuzzy C-Means, PCA–DBSCAN memberikan validasi internal terbaik (Silhouette = 0.277; DBI = 1.062; Dunn = 0.0765). Pendekatan ini menghasilkan profil antropometri yang dapat digunakan sebagai alat bantu penentuan prioritas pemantauan dan penilaian gizi di layanan kesehatan primer, tanpa menggantikan klasifikasi WHO.
KeywordsStunting; Status Gizi Balita; Klasterisasi Tanpa Pengawasan; Principal Component Analysis (PCA); DBSCAN
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References
Y. S. Arief, F. C. Yunita, F. Efendi, F. A. K. Murti, R. O. Pradipta, and L. McKenna, “Social and Environmental Determinants of Childhood Stunting in Indonesia: National Cross-Sectional Study,” JMIR Pediatr. Parent., vol. 8, no. 1, p. e68918, 2025, doi: 10.2196/68918.
World Health Organization, “The use of nutrition administrative data to generate child malnutrition estimates,” Geneva, 2025. [Online]. Available: https://www.who.int/publications/m/item/the-use-of-nutrition-administrative-data-to-generate-population-level-child-malnutrition-estimates-technical-note
S. Supadmi et al., “Factor related to stunting of children under two years with working mothers in Indonesia,” Clin. Epidemiol. Glob. Heal., vol. 26, no. May 2023, pp. 1–6, 2024, doi: 10.1016/j.cegh.2024.101538.
T. T. Tamir, M. A. Techane, M. T. Dessie, and K. A. Atalell, “Applied nutritional investigation spatial variation and determinants of stunting among children aged less than 5 y in Ethiopia: A spatial and multilevel analysis of Ethiopian Demographic and Health Survey 2019,” Nutrition, vol. 103–104, p. 111786, 2022, doi: 10.1016/j.nut.2022.111786.
U. H. Jalloh et al., “A mixed-methods study of the drivers of stunting reduction among children under-5 in Sierra Leone, 2005–2017,” Am. J. Clin. Nutr., vol. 121, no. February, pp. S106–S112, 2025, doi: 10.1016/j.ajcnut.2025.02.015.
D. Sudigyo, A. A. Hidayat, R. Nirwantono, R. Rahutomo, J. P. Trinugroho, and B. Pardamean, “Literature study of stunting supplementation in Indonesian utilizing text mining approach,” Procedia Comput. Sci., vol. 216, no. 2022, pp. 722–729, 2022, doi: 10.1016/j.procs.2022.12.189.
R. E. Caraka et al., “Bayesian Network analysis of spatial disparities in child nutrition status and health determinants across Indonesia,” Int. J. Appl. Earth Obs. Geoinf., vol. 144, no. November, pp. 1–19, 2025, doi: 10.1016/j.jag.2025.104936.
Pemerintah Kabupaten Maros, “Fokus Entaskan Stunting di Kabupaten Maros, Bupati Maros: Prevalensi Stunting Menurun 12,3 Persen,” Kabupaten Maros. Accessed: Aug. 15, 2026. [Online]. Available: https://maroskab.go.id/2025/08/fokus-entaskan-stunting-di-kabupaten-maros-bupati-maros-prevalensi-stunting-menurun-123-persen/
Christianto, “Maros Catat Penurunan Stunting Drastis, Terbesar di Sulsel,” Radio Republik Indonesia. Accessed: Aug. 16, 2026. [Online]. Available: https://rri.co.id/makassar/regional/1762499/maros-catat-penurunan-stunting-drastis-terbesar-di-sulsel
T. A. E. Permatasari and Y. Chadirin, “Assessment of undernutrition using the composite index of anthropometric failure (CIAF) and its determinants: A cross-sectional study in the rural area of the Bogor District in Indonesia,” BMC Nutr., vol. 8, p. 133, 2022, doi: 10.1186/s40795-022-00627-3.
L. A. Jokhu and A. Syauqy, “Determinants of concurrent wasting and stunting among children 6 to 23 mo in Indonesia,” Nutrition, vol. 122, p. 112390, 2024, doi: 10.1016/j.nut.2024.112390.
B. A. Paramashanti, Y. Paratmanitya, I. I. Kusumaningtyas, T. M. Khasana, A. Yugistyowati, and T. Siswati, “Minimum dietary diversity and the concurrence of stunting and overweight among infants and young children in Yogyakarta, Indonesia,” Nutr. Food Sci., vol. 54, no. 1, pp. 120–130, 2024, doi: 10.1108/NFS-02-2023-0042.
D. B. Rahut, R. Mishra, and S. Bera, “Geospatial and environmental determinants of stunting, wasting, and underweight: Empirical evidence from rural South and Southeast Asia,” Nutrition, vol. 120, p. 112346, 2024, doi: 10.1016/j.nut.2023.112346.
N. Umwali, C. N. Kunyanga, and D. W. M. Kaindi, “Determinants of stunting in children aged between 6–23 months in Musanze region, Rwanda,” Front. Nutr., vol. 9, pp. 1–12, 2022, doi: 10.3389/fnut.2022.1044350.
W. R. Mgomezulu, P. Thangata, B. Mkandawire, and N. Amoah, “Advancing predictive analytics in child malnutrition: Machine, ensemble and deep learning models with balanced class distribution for early detection of stunting and wasting,” Hum. Nutr. Metab., vol. 42, no. August, p. 200340, 2025, doi: 10.1016/j.hnm.2025.200340.
B. Rawat, H. Pant, and A. Bist, “Clustering Medical Conditions in Patient Records Using Unsupervised Learning Techniques: A Comparative Study,” Biomed. Pharmacol. J., vol. 18, no. 3, pp. 2050–2060, 2025, doi: 10.13005/bpj/3236.
G. S. Mohamed Khamis, N. S. Alqahtani, S. Munadi Alanazi, M. M. Alruwaili, M. S. Alenazi, and M. A. Alrawaili, “Using Fuzzy C-Means clustering and PCA in public health: A machine learning approach to combat CVD and obesity,” Informatics Med. Unlocked, vol. 57, no. August 2024, pp. 1–10, 2025, doi: 10.1016/j.imu.2025.101666.
N. A. Rizki, A. Asyril, M. Maulidah, and C. F. Dewi, “Spatial Clustering of Urban Villages on Stunting Babies Data in Samarinda Using the DBSCAN Model,” Media Kesehat. Masy. Indones. Vol., vol. 21, no. 1, pp. 24–32, 2025, doi: 10.30597/mkmi.v21i1.41250.
A. Fahim, “Adaptive Density-Based Spatial Clustering of Applications with Noise (ADBSCAN) for Clusters of Different Densities,” Comput. Mater. Contin., vol. 75, no. 2, pp. 3695–3712, 2023, doi: 10.32604/cmc.2023.036820.
R. B. Fanda et al., “The availability of essential medicines in primary health centres in Indonesia: achievements and challenges across the archipelago,” Lancet Reg. Heal. - Southeast Asia, vol. 22, pp. 1–13, 2024, doi: 10.1016/j.lansea.2023.100345.
M. Faisal et al., “Assessing AI-Driven Personalization in Smart Cities Using Hybrid Machine Learning and MCDM Approach,” HighTech Innov. J., vol. 6, no. 3, pp. 770–792, Sep. 2025, doi: 10.28991/HIJ-2025-06-03-03.
S. D. Darcho, F. S. Bayisa, and T. D. Nimani, “Prevalence of appropriate feeding practices and nutritional status among infant and young children aged 0–23 Months attending public hospitals in Harar town, eastern Ethiopia, 2024: An institutional-based cross-sectional study,” Clin. Epidemiol. Glob. Heal., vol. 32, pp. 1–6, 2025, doi: 10.1016/j.cegh.2025.101937.
S. A. Norris et al., “Infant growth and body composition from birth to 24 months : are infants developing the same ?,” Eur. J. Clin. Nutr., vol. 78, pp. 952–962, 2024, doi: 10.1038/s41430-023-01386-5.
M. Greenacre, P. J. F. Groenen, T. Hastie, A. Iodice D’Enza, A. Markos, and E. Tuzhilina, “Principal component analysis,” Nat. Rev. Methods Prim., vol. 2, no. 1, pp. 1–21, 2022, doi: 10.1038/s43586-022-00184-w.
D. N. Aisyah et al., “Using WhatsApp for Nutrition Surveillance Among Children Under 5 Years in West Java, Indonesia: Cross-Sectional Survey and Feasibility Study,” JMIR Pediatr. Parent., vol. 8, no. 1, p. e58752, 2025, doi: 10.2196/58752.
S. Tong, J. Chen, and C. Xu, “A framework for evaluating the combined effects of water transfer and storage strategies on water stress alleviation,” Ecol. Indic., vol. 166, pp. 1–13, 2024, doi: 10.1016/j.ecolind.2024.112542.
S. Oprea and B. Adela, “Heliyon Profiling public perception of emerging technologies : Gene editing , brain chips and exoskeletons . A data-analytics framework,” vol. 10, pp. 1–22, 2024, doi: 10.1016/j.heliyon.2024.e40268.
A. Keramatian, V. Gulisano, and M. Papatriantafilou, “IP.LSH.DBSCAN: Integrated parallel density-based clustering by locality-sensitive hashing,” Discret. Appl. Math., vol. 382, pp. 183–196, 2025, doi: 10.1016/j.dam.2025.11.047.
K. Giri, T. K. Biswas, and P. Sarkar, “ECR-DBSCAN: An improved DBSCAN based on computational geometry Kinsuk,” Mach. Learn. with Appl., vol. 6, no. August, p. 100148, 2021, doi: 10.1016/j.mlwa.2021.100148.
D. Y. Badawood, “StatVis : A visual analytics framework for statistical cluster validation in high dimensions,” Array, vol. 28, pp. 1–16, 2025, doi: 10.1016/j.array.2025.100560.
A. N. Belkacem and A. Hireche, “Exploring student engagement and learning preferences: A comparative study between virtual- and robot-based tutors,” Meas. Sensors, vol. 38, pp. e1–e5, 2025, doi: 10.1016/j.measen.2024.101704.
M. Monshizadeh, V. Khatri, R. Kantola, and Z. Yan, “A deep density based and self-determining clustering approach to label unknown traffic,” J. Netw. Comput. Appl., vol. 207, pp. 1–18, 2022, doi: https://doi.org/10.1016/j.jnca.2022.103513.
H. Ghaderi, B. Foreman, A. Nayebi, S. Tipirneni, C. K. Reddy, and V. Subbian, “A self-supervised learning-based approach to clustering multivariate time-series data with missing values ( SLAC-Time ): An application to TBI phenotyping,” J. Biomed. Inform., vol. 143, 2023, doi: 10.1016/j.jbi.2023.104401.
N. Pakgohar, A. Lengyel, and Z. B. Dukat, “Quantitative evaluation of internal cluster validation indices using binary data sets,” J. Veg. Sci., vol. 35, p. e13310, 2024, doi: 10.1111/jvs.13310.
A. N. Brzezinska and I. Gaibei, “How the Outliers Influence the Quality of Clustering?,” Entropy, vol. 24, no. 7, 2022, doi: 10.3390/e24070917.
A. Rafisa, E. Sarilita, B. Delage, R. G. Munger, and P. A. Mossey, “Situational analysis of nutritional status among 1899 children presenting with cleft lip and/or palate in Indonesia,” J. Glob. Health, vol. 13, p. 04127, 2023, doi: 10.7189/jogh.13.04127.
B. N. Damanik and H. Hely, “Determinants of Hospital Length of Stay Among Pulmonary Tuberculosis Inpatients at A Secondary General Hospital in Indonesia: A Comparative Analysis,” J. Med. Informatics Technol., vol. 4, no. 2, pp. 64–69, 2026, doi: 10.37034/medinftech.v4i2.150.
M. Thamrin et al., “Machine learning for global trade analysis: a hybrid clustering approach using DBSCAN, elbow, and SOM,” IAES Int. J. Artif. Intell., vol. 14, no. 4, pp. 3033–3046, 2025, doi: 10.11591/ijai.v14.i4.pp3033-3046.
M. Faisal et al., “Utilizing Machine Learning-Based Decision-Making to Align Higher Education Curriculum with Industry Requirements,” Int. J. Mod. Educ. Comput. Sci., vol. 17, no. 4, pp. 1–25, 2025, doi: 10.5815/ijmecs.2025.04.01.
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