EARLY DETECTION OF STUNTING IN CHILDREN USING HSV AND GLCM FEATURES WITH CNN-BASED CLASSIFICATION
DOI:
https://doi.org/10.33480/jitk.v11i4.8258Keywords:
CNN, Digital Image Processing, GLCM, HSV, StuntingAbstract
Stunting is a chronic nutritional issue in children that can have long-term impacts on physical growth and cognitive development. Early detection is therefore important to support timely intervention. This study develops an image-based stunting detection approach that integrates HSV color descriptors, Gray Level Co-occurrence Matrix (GLCM) texture descriptors, and CNN features extracted with MobileNetV2. Images are preprocessed through resizing and Region of Interest (ROI) cropping based on bounding-box annotations. The handcrafted HSV and GLCM features are fused with CNN features at the feature level through vector concatenation before classification into stunting and non-stunting categories. This design was selected to preserve complementary low-level color-texture information and high-level semantic representations in a single classifier input. The hybrid model achieved a test accuracy of 84.39% with a stunting recall of 83%. Although the results indicate that multimodal visual descriptors can improve classification performance compared with single-feature approaches, the model still shows mild overfitting and was evaluated on a relatively limited dataset. In addition, inference efficiency and robustness to variations in imaging conditions were not yet quantitatively measured. Therefore, the present system should be interpreted as a proof of concept for objective, early, image-based stunting screening by healthcare personnel.
Downloads
References
[1] Bhakti Vichave, Nikhil Jain, Pankaj Garad, Namit Gandhi, and Prof. Dewanand Meshram, “Malnutrition Detection using AI,” Int. J. Adv. Res. Sci. Commun. Technol., pp. 285–291, 2023, doi: 10.48175/ijarsct-9692.
[2] A. T. Mulyani, M. A. Khairinisa, A. Khatib, and A. Y. Chaerunisaa, “Understanding Stunting: Impact, Causes, and Strategy to Accelerate Stunting Reduction—A Narrative Review,” Nutr. , vol. 17, no. 9, 2025, doi: 10.3390/nu17091493.
[3] T. A. E. Permatasari, Y. Chadirin, E. Ernirita, A. N. Syafitri, and D. A. Fadhilah, “The accuracy of a novel stunting risk detection application based on nutrition and sanitation indicators in children aged under five years,” BMC Nutr., vol. 11, no. 1, 2025, doi: 10.1186/s40795-025-01074-6.
[4] Debby Ratno Kustanto, Indah Putri Ramadhanti, and Masya Putri, “Stunting Diberbagai Negara: Perbandingan Global,” J. Pengabdi. Masy. Aufa, vol. 7, no. 1, 2025, doi: 10.51933/jpma.v7i1.1932.
[5] H. Shen, H. Zhao, and Y. Jiang, “Machine Learning Algorithms for Predicting Stunting among Under-Five Children in Papua New Guinea,” Children, vol. 10, no. 10, 2023, doi: 10.3390/children10101638.
[6] B. Alnur, Mulyono, Fitri Amillia, and S. Sutoyo, “Performance Analysis of 10 Mbps Wireless Iconnet in Perumahan Bumi Mi’raj,” J. Informatics Telecommun. Eng., vol. 7, no. 1, pp. 102–111, 2023, doi: 10.31289/jite.v7i1.9548.
[7] F. M. T. Pane and D. Hindarto, “Comparative Analysis of Machine Learning Models for Stunting Prediction in Jakarta,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 9, no. 4, pp. 1365–1375, 2025, doi: 10.35870/jtik.v9i4.3853.
[8] I. Sofyan Iryad, M. Qamal, and A. Razi, “Classification For Determining Nutritional Status of Toddlers Using Random Forest Method at Tanah Pasir Primary Health Centre, North Aceh”, JAIC, vol. 9, no. 6, pp. 3312–3321, Dec. 2025, doi: 10.30871/jaic.v9i6.10855.
[9] S. Abrori and Z. Fatah, “Implementasi Metode CNN Untuk Klasifikasi Status Stunting Pada Balita ”. Gudang Jurnal Multidisiplin Ilmu, vol. 2, no. 10, Oct. 2024, pp. 380-5, doi:10.59435/gjmi.v2i10.1022.
[10] H. Mulyani, M. Musawarman, R. Faturrohman, and D. H. Permana, “Machine Learning-Based Early Detection of Stunting and Intervention Recommendations”, bit-Tech, vol. 8, no. 2, pp. 2160–2170, Dec. 2025, doi: 10.32877/bt.v8i2.3213.
[11] Mardiana, N. A., & Windari, W. O., "Penerapan metode SMED dalam peningkatan efisiensi proses manufaktur," G-Tech: Jurnal Teknologi Terapan, vol 8, no. 1, pp. 186–195, 2024.
[12] S. A. Wicaksono, S. H. Wijoyo, Fatmawati, T. Afirianto, D. Kurnianingtyas, and M. C. Saputra, “Naive Bayes Analysis for Nutritional Fulfillment Prediction in Children,” J. Appl. Eng. Technol. Sci., vol. 6, no. 2, pp. 1135–1147, 2025, doi: 10.37385/jaets.v6i2.6105.
[13] A. Nugraheni, R. D. Ramadhani, A. B. Arifa, and A. Prasetiadi, "Perbandingan Performa Antara Algoritma Naive Bayes Dan K-Nearest Neighbour Pada Klasifikasi Kanker Payudara. Journal of Di,” J. Dinda Data Sci. Inf. Technol. Data Anal., vol. 2, no. 1, pp. 11–20, 2022.
[14] M. Khabir, A. A. Jabbari, and M. H. Razmi, “Flipped Presentation of Authentic Audio-Visual Materials: Impacts on Intercultural Sensitivity and Intercultural Effectiveness in an EFL Context,” Front. Psychol, vol. 13, pp. 1–10, Feb. 2022, doi: 10.3389/fpsyg.2022.832862.
[15] D. P. Sari, S. Widodo, and K. Mustofa, “Development of an Image-Based Calorie Detection Model in Indonesian Food for Stunting Prevention,” Proceeding Int. Conf. Sci. Heal. Technol., pp. 468–480, 2025, doi: 10.47701/6d407123.
[16] E. Rianti, I. Fitri, Sumijan, and F. F. Yani, “Development of GLCM Method in Calculate Entropy Value for Digital Visualization in Identifying Childhood Pneumonia Based on Chest X-Ray Images,” Int. J. Online Biomed. Eng., vol. 21, no. 2, pp. 137–156, 2025, doi: 10.3991/ijoe.v21i02.52909.
[17] R. F. H. Pasaribu, M. Zarlis, and E. B. Nababan, “Performance Level Analysis On Learning Vector Quantization And Cohonen Algorithms,” Sinkron, vol. 9, no. 1, pp. 267–282, 2025, doi: 10.33395/sinkron.v9i1.14313.
[18] Pradeep M and Dr. M Siddappa, “Classification of Rice Using Convolutional Neural Network (Cnn),” Int. J. Eng. Technol. Manag. Sci., vol. 7, no. 5, pp. 455–463, 2023, doi: 10.46647/ijetms.2023.v07i05.056.
[19] A. Nur Sahid and D. R. Cahyadi, “Image Classification Using MobileNet Based on CNN Architecture for Grape Leaf Disease Detection,” J. Intell. Syst. Technol. Informatics, vol. 1, no. 1, pp. 15–21, 2025, doi: 10.64878/jistics.v1i1.7.
[20] A. S. Musliman, A. Fadlil, and A. Yudhana, “Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction,” J. Online Inform., vol. 6, no. 1, pp. 63–72, 2021, doi: 10.15575/join.v6i1.704.
[21] M. R. Syahputra et al., “Accurate Skin Tone Classification for Foundation Shade Matching using GLCM Features-K-Nearest Neighbor Algorithm,” J. Tek. Inform., vol. 6, no. 5, pp. 3558–3571, 2025, doi: 10.52436/1.jutif.2025.6.5.4723.
[22] S. M. Javidan, A. Banakar, K. Rahnama, K. A. Vakilian, and Y. Ampatzidis, “Feature engineering to identify plant diseases using image processing and artificial intelligence: A comprehensive review,” Smart Agric. Technol., vol. 8, no. May, p. 100480, 2024, doi: 10.1016/j.atech.2024.100480.
[23] D. S. Darmawan and E. Sediyono, “Perancangan Aplikasi Sistem Informasi Gelanggang Olahraga Berbasis Web Menggunakan Framework Laravel,” J. Tek. Inform. dan Sist. Inf., vol. 10, no. 1, pp. 51–62, 2023, doi: 10.35957/jatisi.v10i1.2587.
[24] P. F. Johari, N. Arifin, M. Muzaki, and M. S. A. Utama, “Corn Leaf Diseases Classification Using CNN with GLCM, HSV, and L*a*b* Features,” J. Tek. Inform., vol. 6, no. 2, pp. 709–722, 2025, doi: 10.52436/1.jutif.2025.6.2.4345.
[25] Y. Yunidar, Y. Yusni, N. Nasaruddin, and F. Arnia, “CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach,” J. RESTI, vol. 9, no. 1, pp. 62–68, 2025, doi: 10.29207/resti.v9i1.6068.
[26] A. Bahtiar, M. Mulyawan, A. Faqih, L. Lidina, and A. R. Fitria, “1D-CNN-Based Childhood Stunting Prediction through Socio-Economic Data Integration and Community Participation,” JISA(Jurnal Inform. dan Sains), vol. 8, no. 2, pp. 162–169, 2025, doi: 10.31326/jisa.v8i2.2490.
[27] M. Mahendran, R. Visalakshi, and S. Balaji, “Dysarthria detection using convolution neural network,” Meas. Sensors, vol. 30, no. 05, pp. 11–16, 2023, doi: 10.1016/j.measen.2023.100913.
[28] T. Pujitha, K. Rohitha, K. Nalini, G. Madhulatha, B. Sudharani, and J. Dar, “High-Performance Computing-Based Brain Tumor Detection Using Parallel Quantum Dilated Convolutional Neural Network,” vol. 3, pp. 836–843, 2025, doi: 10.5220/0013906600004919.
[29] S. Aanjankumar et al., “Prediction of malnutrition in kids by integrating ResNet-50-based deep learning technique using facial images,” Sci. Rep., vol. 15, no. 1, pp. 1–26, 2025, doi: 10.1038/s41598-025-91825-z.
[30] A. Husaini, I. Hoeronis, H. H. Lumana, and L. D. Puspareni, “Early Detection of Stunting in Toddlers Based on Ensemble Machine Learning in Purbaratu Tasikmalaya,” J. Sist. dan Teknol. Inf., vol. 11, no. 3, p. 487, 2023, doi: 10.26418/justin.v11i3.66465.
[31] N. P. Sari, “Analisis Performa Algoritma CNN dalam Klasifikasi Citra Medis Berbasis Deep Learning,” J. Komput., vol. 2, no. 2, pp. 87–92, 2024, doi: 10.70963/jk.v2i2.113.
[32] H. Yi, “A Review of Convolutional Neural Networks in Cancer Image Classification,” Appl. Comput. Eng., vol. 97, no. 1, pp. 69–74, 2024, doi: 10.54254/2755-2721/97/20241334.
[33] M. Caldwell, “Research on Medical Image Diagnosis Models Based on Convolutional Neural Networks,” J. Comput. Technol. Softw., vol. 4, no. 2, p. 2025, 2025, doi: 10.5281/zenodo.14984979.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ade Cristian Silalahi, Bonatio Vincent E Hutagalung, Sandy Walfredo Ritonga, Marlince NK Nababan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
