CesLA (Cegah Stunting Lewat Anemia): Deteksi Anemia Non-Invasif pada Remaja Putri Berbasis Citra Konjungtiva

Hepatika Zidny Ilmadina; Juhrotun Nisa; Dyah Apriliani; Lulu Nadhiatun Anisa; Firda Aulia Rakhmah

doi:10.30591/jpit.v10i3.8873

CesLA (Cegah Stunting Lewat Anemia): Deteksi Anemia Non-Invasif pada Remaja Putri Berbasis Citra Konjungtiva

Hepatika Zidny Ilmadina, Juhrotun Nisa, Dyah Apriliani, Lulu Nadhiatun Anisa, Firda Aulia Rakhmah

Abstract

Stunting is a chronic nutritional problem that will directly affect the quality of human resources in the future. One of the contributing factors to stunting is anemia during pregnancy, which often originates from adolescence. Early detection of anemia in women of reproductive age is a crucial preventive measure to reduce the risk of stunting. This study aims to develop an anemia classification model based on conjunctival images using a combination of MobileNetV2 architecture and Support Vector Machine (SVM), and to implement the model into a mobile application named CeSLA (Cegah Stunting Lewat Anemia). The model was built using a dataset of female conjunctival images annotated based on haemoglobin levels and visual characteristics of the conjunctiva. Evaluation results explain that the model achieved precision, recall, and f1-score values ranging from 0.91 to 0.92 for each class, with a macro average of 0.92, indicating accurate and balanced classification performance. The trained and evaluated model was then integrated into the CeSLA mobile application. This application allows users, particularly adolescent girls, to detect potential anemia non-invasively by scanning the lower eyelid using a smartphone camera. CeSLA is also equipped with educational features such as health articles and a detection history log. With this approach, CeSLA is expected to serve as an innovative solution that supports early, self-administered anemia detection and contributes to the national effort to prevent stunting.

Keywords

Anemia, Conjungtiva, Non-Invasive Detection, Mobilenetv2, Stunting, SVM

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References

R. Cholidah, A. Danianto, R. D. Ayunda, and D. Rahmadhona, “History of Anemia in Pregnancy with Stunting Incidents in Toddlers at Nipah Community Health Center, Malaka, North Lombok Regency,” J. Penelit. Pendidik. IPA, vol. 9, no. 12, pp. 12226–12231, 2023, doi: 10.29303/jppipa.v9i12.4946.

N. Reviani and C. H. Tampubolon, “The Influence of a History of Anemia during Pregnancy on Stunting Incidents,” Int. J. Trop. Dis. Heal., vol. 46, no. 3, pp. 29–36, Feb. 2025, doi: 10.9734/ijtdh/2025/v46i31634.

L. H. Adilah, A. Syafiq, and S. Sukoso, “Correlation of Anemia in Pregnant Women with Stunting Incidence: A Review,” Indones. J. Multidiscip. Sci., vol. 2, no. 9, pp. 3155–3169, Jul. 2023, doi: 10.55324/IJOMS.V2I9.545.

J. Penelitian Kesehatan Suara Forikes ---------------Volume, J. Penelitian Kesehatan Suara Forikes ------, H. Azzahra, L. Hidayati, and E. Nur Widiyaningsih, “Kejadian Anemia pada Masa Kehamilan sebagai Faktor Risiko Balita Stunting di Kota Surakarta,” J. Penelit. Kesehat. “SUARA FORIKES” (Journal Heal. Res. “Forikes Voice”), vol. 15, no. 4, pp. 786–790, Dec. 2024, doi: 10.33846/SF15444.

S. R. Nadhiroh, F. Micheala, S. E. H. Tung, and T. C. Kustiawan, “Association between maternal anemia and stunting in infants and children aged 0–60 months: A systematic literature review,” Nutrition, vol. 115, Nov. 2023, doi: 10.1016/j.nut.2023.112094.

I. I. Putra, J. M. M. Sondakh, and J. J. Kaeng, “Anemia in Pregnancy and Its Maternal Perinatal Outcome,” Indones. J. Obstet. Gynecol., vol. 12, no. 3, pp. 141–146, Jul. 2024, doi: 10.32771/INAJOG.V12I3.1989.

A. Setiyaningsih et al., “HUBUNGAN KADAR HEMOGLOBIN IBU HAMIL DENGAN KEJADIAN STUNTING PADA BALITA DI PUSKESMAS NGEMPLAK,” J. Komun. Kesehat., vol. 14, no. 1, pp. 26–36, Apr. 2023, doi: 10.56772/JKK.V14I1.317.

F. Adhimukti, U. R. Budihastuti, and B. Murti, “Meta-Analysis: The Effect of Anemia in Pregnant Women on the Risk of Postpartum Bleeding and Low Birth Weight,” J. Matern. Child Heal., vol. 8, no. 1, pp. 58–69, Jan. 2023, doi: 10.26911/THEJMCH.2023.08.01.06.

G. T. J. Salakory and I. B. E. U. Wija, “Hubungan Anemia Pada Ibu Hamil Terhadap Kejadian Stunting di RS Marthen Indey Jayapura Tahun 2018-2019,” Maj. Kedokt. UKI, vol. 37, no. 1, pp. 9–12, Sep. 2021, doi: 10.33541/MK.V37I1.3365.

M. Hastuty, U. Pahlawan, and T. Tambusai, “HUBUNGAN ANEMIA IBU HAMIL DENGAN KEJADIAN STUNTING PADA BALITA DI UPTD PUSKESMAS KAMPAR TAHUN 2018,” J. Doppler, vol. 4, no. 2, pp. 112–116, Nov. 2020, Accessed: May 26, 2025. [Online]. Available: https://journal.universitaspahlawan.ac.id/doppler/article/view/1046.

S. D. S. Syarifuddin, A. Khurniawan, R. Munadi, and S. Sussi, “Sistem Informasi Pengukuran Kadar Hemoglobin Non-Invasif Berbasis Android Menggunakan Algoritma Extreme Gradient Boosting,” Komputika J. Sist. Komput., vol. 12, no. 1, pp. 13–23, May 2023, doi: 10.34010/komputika.v12i1.5049.

M. A. Priyadarshini, S. Salma, D. Sailesh, E. Manasa, G. L. Charan, and B. Dinesh, “A Visionary Approach to Anemia Detection: Integrating Eye Condition Data and Machine Learning,” no. Icciet 2024, Atlantis Press International BV, 2024, pp. 781–793.

J. W. Asare, P. Appiahene, and E. T. Donkoh, “Detection of anaemia using medical images: A comparative study of machine learning algorithms – A systematic literature review,” Informatics Med. Unlocked, vol. 40, p. 101283, Jan. 2023, doi: 10.1016/J.IMU.2023.101283.

M. Mansour, T. B. Donmez, M. Kutlu, and S. Mahmud, “Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks,” Front. Big Data, vol. 6, p. 1291329, 2023, doi: 10.3389/FDATA.2023.1291329.

S. Aiwale et al., “Noninvasive Anemia Detection and Prediagnosis,” J. Pharmacol. Pharmacother., Dec. 2024, doi: 10.1177/0976500X241276307/ASSET/E963B5CF-995B-4B09-AD87-D3A9561B4FCD/ASSETS/IMAGES/LARGE/10.1177_0976500X241276307-FIG4.JPG.

G. Dimauro, M. E. Griseta, M. G. Camporeale, F. Clemente, A. Guarini, and R. Maglietta, “An intelligent non-invasive system for automated diagnosis of anemia exploiting a novel dataset,” Artif. Intell. Med., vol. 136, p. 102477, Feb. 2023, doi: 10.1016/J.ARTMED.2022.102477.

S. Das, F. Ahamed, A. Das, D. Das, J. Nandi, and K. Banerjee, “NiADA (Non-invasive Anemia Detection App), a Smartphone-Based Application With Artificial Intelligence to Measure Blood Hemoglobin in Real-Time: A Clinical Validation,” Cureus, vol. 16, no. 7, Jul. 2024, doi: 10.7759/cureus.65442.

S. Hajrianti, M. N. Widyawati, and K. Kurnianingsih, “Deteksi Anemia pada Ibu Hamil Mengunakan Metode Non Invasif Berbasis Kecerdasan Artifisial,” J. Telenursing, vol. 5, no. 2, pp. 3568–3577, Dec. 2023, doi: 10.31539/joting.v5i2.7468.

H. Z. Ilmadina, M. Naufal, and D. S. Wibowo, “Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 22, no. 3, pp. 419–430, Jun. 2023, doi: 10.30812/MATRIK.V22I3.2785.

E. P. A. Meindiawan and M. Muljono, “Application of MobileNetV2 and SVM Combination for Enhanced Accuracy in Pneumonia Classification,” J. Appl. Informatics Comput., vol. 8, no. 2, pp. 332–340, Nov. 2024, doi: 10.30871/JAIC.V8I2.8426.

C. Singh and A. Bala, “A local Zernike moment-based unbiased nonlocal means fuzzy C-Means algorithm for segmentation of brain magnetic resonance images,” Expert Syst. Appl., vol. 118, pp. 625–639, Mar. 2019, doi: 10.1016/j.eswa.2018.10.023.

S. Studer et al., “Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology,” Mach. Learn. Knowl. Extr., vol. 3, no. 2, pp. 392–413, Jun. 2021, doi: 10.3390/MAKE3020020.

S. Ki Hong and Y. Lee, “Optimizing Detection: Compact MobileNet Models for Precise Hall Sensor Fault Identification in BLDC Motor Drives,” IEEE Access, vol. 12, pp. 77475–77485, 2024, doi: 10.1109/ACCESS.2024.3407766.

H. Z. Ilmadina, D. Apriliani, and D. S. Wibowo, “Deteksi Pengendara Mengantuk dengan Kombinasi Haar Cascade Classifier dan Support Vector Machine,” J. Inform. J. Pengemb. IT, vol. 7, no. 1, pp. 1–7, Jan. 2022, doi: 10.30591/jpit.v7i1.3346.

Z. Wang, C. Wu, K. Zheng, X. Niu, and X. Wang, “SMOTETomek-Based Resampling for Personality Recognition,” IEEE Access, vol. 7, pp. 129678–129689, 2019, doi: 10.1109/ACCESS.2019.2940061.

M. J. Adamu et al., “Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2–Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms,” Brain Sci., vol. 14, no. 12, p. 1178, Nov. 2024, doi: 10.3390/brainsci14121178.

E. N F et al., “A Hybrid Model using MobileNetv2 and SVM for Enhanced Classification and Prediction of Tomato Leaf Diseases,” Int. J. Electr. Electron. Eng., vol. 10, no. 8, pp. 37–50, Sep. 2023, doi: 10.14445/23488379/IJEEE-V10I8P104.

DOI: https://doi.org/10.30591/jpit.v10i3.8873