Revealing Stunting Risk Patterns through Comparative Analysis of Hierarchical and Deep Embedded Clustering

Fifin Ayu Mufarroha; Abdullah Basuki  Rahmat; Husni; Aeri  Rachmad; Vivin Ayu  Lestari; Tasya  Dwiyanti; Malik  Maulana

doi:10.22219/kinetik.v11i2.2555

Issue

Vol. 11, No. 2, May 2026

Issue Published : May 1, 2026

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Revealing Stunting Risk Patterns through Comparative Analysis of Hierarchical and Deep Embedded Clustering

https://doi.org/10.22219/kinetik.v11i2.2555

Fifin Ayu Mufarroha

University of Trunojoyo Madura

Abdullah Basuki Rahmat

University of Trunojoyo Madura

Husni

University of Trunojoyo Madura

Aeri Rachmad

University of Trunojoyo Madura

Vivin Ayu Lestari

State Polytechnic of Malang

Tasya Dwiyanti

University of Trunojoyo Madura

Malik Maulana

University of Trunojoyo Madura

Corresponding Author(s) : Fifin Ayu Mufarroha

fifin.mufarroha@trunojoyo.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 11, No. 2, May 2026
Article Published : May 1, 2026

Abstract

Stunting remains a significant public health issue in Indonesia due to its long-term impact on human resource quality and economic productivity. Despite various intervention programs, disparities in stunting prevalence across regions remain high, particularly in areas characterized by diverse socioeconomic conditions. This study aims to identify regional patterns and group areas based on stunting risk levels using two machine learning approaches: Hierarchical Clustering (HC) and Deep Embedded Clustering (DEC). The data used in this study consist of aggregated toddler measurement data, including the number of toddlers measured, the number of stunting cases, and the percentage of stunting during the 2020–2024 period. The analysis was conducted by comparing the clustering results generated by both methods. The HC method was implemented using the Agglomerative Clustering approach with the Ward linkage criterion, while DEC employed a layered autoencoder architecture optimized using Kullback–Leibler divergence. Cluster quality was evaluated using the Silhouette Score metric. The results show that HC achieved the highest Silhouette Score of 0.5430, while DEC achieved 0.4874, with both methods exhibiting year-to-year performance variation. These findings indicate that HC provides better clustering stability, whereas DEC demonstrates greater adaptability to data complexity and nonlinear patterns. The integration of both methods offers a comprehensive big data–driven health analytics framework, representing an innovative approach for evidence-based decision-making in identifying and addressing stunting-prone regions.

Keywords

Stunting Hierarchical Clustering Deep Embedded Clustering Comparative Analysis Big Data

Mufarroha, F. A., Rahmat, A. B. ., Husni, Rachmad, A. ., Lestari, V. A. ., Dwiyanti, T. ., & Maulana, M. . (2026). Revealing Stunting Risk Patterns through Comparative Analysis of Hierarchical and Deep Embedded Clustering. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 11(2), 295-310. https://doi.org/10.22219/kinetik.v11i2.2555

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References

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Health Development Policy Agency of the Ministry of Health of the Republic of Indonesia, “Data Catalog: Indonesian Health Survei,” Indonesia, 2023.
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P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., vol. 20, pp. 53–65, 1987. https://doi.org/10.1016/0377-0427(87)90125-7
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S. Alrabie and A. Barnawi, “Enhancing Heart Sound Classification with Iterative Clustering and Silhouette Analysis: An Effective Preprocessing Selective Method to Diagnose Rare and Difficult Cardiovascular Cases,” Computer Modeling in Engineering & Sciences, vol. 144, no. 2, p. 2481, 2025. https://doi.org/10.32604/cmes.2025.067977

References

Health Development Policy Agency of the Ministry of Health of the Republic of Indonesia, “Data Catalog: Indonesian Nutrition Status Survey (SSGI) 2022,” Indonesia, 2022.

Health Development Policy Agency of the Ministry of Health of the Republic of Indonesia, “Data Catalog: Indonesian Health Survei,” Indonesia, 2023.

Acceleration of Stunting Prevention/TP2AK, “Baseline Report of the 2018-2024 Stunting Prevention Acceleration Program,” Indonesia, 2021.

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. https://doi.org/10.1016/j.nut.2023.112346

S. A. Bhat and N.-F. Huang, “Big data and ai revolution in precision agriculture: Survey and challenges,” Ieee Access, vol. 9, pp. 110209–110222, 2021. https://doi.org/10.1109/ACCESS.2021.3102227

Y. Shi, “Advances in big data analytics,” Adv Big Data Anal, vol. 10, pp. 978–981, 2022. https://doi.org/10.1007/978-981-16-3607-3

H. B. Abdalla, “A brief survey on big data: technologies, terminologies and data-intensive applications,” J. Big Data, vol. 9, no. 1, p. 107, 2022. https://doi.org/10.1186/s40537-022-00659-3

T. T. Khoei and A. Singh, “Data reduction in big data: a survey of methods, challenges and future directions,” Int. J. Data Sci. Anal., vol. 20, no. 3, pp. 1643–1682, 2025. https://doi.org/10.1007/s41060-024-00603-z

J. Han, M. Kamber, and J. Pei, “Data mining: Concepts and,” Techniques, Waltham: Morgan Kaufmann Publishers, 2012.

X. Ran, Y. Xi, Y. Lu, X. Wang, and Z. Lu, “Comprehensive survey on hierarchical clustering algorithms and the recent developments,” Artif. Intell. Rev., vol. 56, no. 8, pp. 8219–8264, 2023. https://doi.org/10.1007/s10462-022-10366-3

J. Xie, R. Girshick, and A. Farhadi, “Unsupervised deep embedding for clustering analysis,” in International conference on machine learning, PMLR, 2016, pp. 478–487. https://doi.org/10.48550/arXiv.1511.06335

F. E. Harrell and D. G. Levy, “Regression modeling strategies,” R package version, pp. 3–6, 2022. https://doi.org/10.1007/978-3-319-19425-7

B. S. Everitt, S. Landau, M. Leese, and D. Stahl, “Cluster analysis,” 2011.

E. Min, X. Guo, Q. Liu, G. Zhang, J. Cui, and J. Long, “A survey of clustering with deep learning: From the perspective of network architecture,” IEEE access, vol. 6, pp. 39501–39514, 2018. https://doi.org/10.1109/ACCESS.2018.2855437

P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to data mining. Pearson Education India, 2016.

A. Annisa, Y. Munarko, and Y. Azhar, “Peringkasan Tweet Berdasarkan Trending Topic Twitter Dengan Pembobotan TF-IDF dan Single Linkage Angglomerative Hierarchical Clustering,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 9–16, 2016. https://doi.org/10.22219/kinetik.v1i1.7

O. Maimon and L. Rokach, Data mining and knowledge discovery handbook, vol. 2, no. 2005. Springer, 2005. https://doi.org/10.1007/b107408

F. Damayanti, S. Herawati, I. Imamah, and A. Rachmad, “Indonesian license plate recognition based on area feature extraction,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 17, no. 2, pp. 620–627, 2019. http://doi.org/10.12928/telkomnika.v17i2.9017

F. A. Mufarroha and F. Utaminingrum, “Hand gesture recognition using adaptive network based fuzzy inference system and K-nearest neighbor,” International Journal of Technology, vol. 8, no. 3, pp. 559–567, 2017. https://doi.org/10.14716/ijtech.v8i3.3146

R. T. Adek, R. K. Dinata, and A. Ditha, “Online newspaper clustering in Aceh using the agglomerative hierarchical clustering method,” International Journal of Engineering, Science and Information Technology, vol. 2, no. 1, pp. 70–75, 2022. https://doi.org/10.52088/ijesty.v2i1.206

I. Shafi et al., “A review of approaches for rapid data clustering: Challenges, opportunities, and future directions,” IEEE Access, vol. 12, pp. 138086–138120, 2024. https://doi.org/10.1109/ACCESS.2024.3461798

J. H. Ward Jr, “Hierarchical grouping to optimize an objective function,” J. Am. Stat. Assoc., vol. 58, no. 301, pp. 236–244, 1963.

H. Hadipour, C. Liu, R. Davis, S. T. Cardona, and P. Hu, “Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means,” BMC Bioinformatics, vol. 23, no. Suppl 4, p. 132, 2022. https://doi.org/10.1186/s12859-022-04667-1

M. Li, C. Cao, C. Li, and S. Yang, “Deep embedding clustering based on residual autoencoder,” Neural Process. Lett., vol. 56, no. 2, p. 127, 2024. https://doi.org/10.1007/s11063-024-11586-0

P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., vol. 20, pp. 53–65, 1987. https://doi.org/10.1016/0377-0427(87)90125-7

M. Shutaywi and N. N. Kachouie, “Silhouette analysis for performance evaluation in machine learning with applications to clustering,” Entropy, vol. 23, no. 6, p. 759, 2021. https://doi.org/10.3390/e23060759

H.-H. Tan, Y.-F. Tan, W.-H. Tan, and C.-P. Ooi, “Investigating Data Consistency in the ASHRAE Dataset Using Clustering and Label Matching,” IEEE Access, 2025. https://doi.org/10.1109/ACCESS.2025.3615311

S. Alrabie and A. Barnawi, “Enhancing Heart Sound Classification with Iterative Clustering and Silhouette Analysis: An Effective Preprocessing Selective Method to Diagnose Rare and Difficult Cardiovascular Cases,” Computer Modeling in Engineering & Sciences, vol. 144, no. 2, p. 2481, 2025. https://doi.org/10.32604/cmes.2025.067977

Issue

Vol. 11, No. 2, May 2026

Revealing Stunting Risk Patterns through Comparative Analysis of Hierarchical and Deep Embedded Clustering

Corresponding Author(s) : Fifin Ayu Mufarroha

Abstract

Keywords

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References

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