Published: 2025-04-01
Pendekatan Machine Learning untuk Deteksi Stunting pada Balita Menggunakan K-Nearest Neighbors
DOI: 10.35870/jtik.v9i2.3436
Downloads
Abstract
Stunting is a chronic nutritional problem affecting the physical growth and cognitive development of toddlers, especially in early childhood. This study employs the K-Nearest Neighbors (K-NN) algorithm to determine stunting status based on anthropometric variables such as age, weight, and height. The algorithm categorizes data using proximity between samples. Data from the Salatiga City Health Department in 2024 were normalized and encoded for analysis. K-NN was chosen for its ability to provide high-accuracy nutritional classification. Results show that the algorithm achieved 100% accuracy, precision, and recall at certain K values, particularly in the small K range (2-8), demonstrating its effectiveness in identifying nutritional status in toddlers. This study is expected to serve as a reference for utilizing technology and data analysis in early stunting detection, aiding healthcare professionals in designing more targeted and effective interventions. Additionally, it opens opportunities for further development to enhance diagnostic accuracy using machine learning technology.
Keywords
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 9 No. 2 (2025)
-
Section: Computer & Communication Science
-
Published: %750 %e, %2025
-
License: CC BY 4.0
-
Copyright: © 2025 Authors
-
DOI: 10.35870/jtik.v9i2.3436
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Ade Putra Tupu Djoru
Program Studi Teknik Informartika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia.
Sri Yulianto
Program Studi Teknik Informartika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia.
-
Bachri, O. S., & Bhakti, R. M. H. (2021). Penentuan Status Stunting pada Anak dengan Menggunakan Algoritma KNN. Jurnal Ilmiah Intech: Information Technology Journal of UMUS, 3(02), 130-137. https://doi.org/10.46772/intech.v3i02.533.
-
Drajana, I. C. R., & Bode, A. (2022). Prediksi Status Penderita Stunting Pada Balita Provinsi Gorontalo Menggunakan K-Nearest Neighbor Berbasis Seleksi Fitur Chi Square. Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), 5(2).
-
Fitrauni, R., Muchlis, N., & Arman, A. (2022). Strategi percepatan penurunan stunting di Kabupaten Sigi. Journal of Muslim Community Health, 3(4), 193-209. https://doi.org/10.52103/jmch.v3i4.1237.
-
Husaini, A. L., Hoeronis, I., Lumana, H. H., & Puspareni, L. D. (2023). Early Detection of Stunting in Toddlers Based on Ensemble Machine Learning in Purbaratu Tasikmalaya. JUSTIN (Jurnal Sistem dan Teknologi Informasi), 11(3), 487-495.
-
Izwardy, D. (2019). Kebijakan dan strategi penanggulangan stunting di Indonesia. Kementerian Kesehatan Republik Indonesia.
-
Kemenkes, R. I. (2023). Prevalensi Stunting di Indonesia Turun ke 21, 6% dari 24, 4%. Kemenkes.
-
Lonang, S., Yudhana, A., & Biddinika, M. K. (2023). Analisis Komparatif Kinerja Algoritma Machine Learning untuk Deteksi Stunting. JURNAL MEDIA INFORMATIKA BUDIDARMA, 7(4), 2109-2117.
-
Mentari, S., & Hermansyah, A. (2019). Faktor-faktor yang berhubungan dengan status stunting anak usia 24-59 bulan di wilayah kerja UPK puskesmas Siantan Hulu. Pontianak Nutrition Journal (PNJ), 1(1), 1-5. https://doi.org/10.30602/pnj.v1i1.275.
-
Prasetiya, T., Ali, I., Rohmat, C. L., & Nurdiawan, O. (2020). Klasifikasi Status Stunting Balita Di Desa Slangit Menggunakan Metode K-Nearest Neighbor. INFORMATICS FOR EDUCATORS AND PROFESSIONAL: Journal of Informatics, 5(1), 93-104.
-
Priyono, P. (2020). Strategi Percepatan Penurunan Stunting Perdesaan (Studi Kasus Pendampingan Aksi Cegah Stunting di Desa Banyumundu, Kabupaten Pandeglang). Jurnal Good Governance. https://doi.org/10.32834/gg.v16i2.198.
-
Rahman, H., Rahmah, M., & Saribulan, N. (2023). Upaya Penanganan Stunting di Indonesia: Analisis Bibliometrik Dan Analisis Konten. Jurnal Ilmu Pemerintahan Suara Khatulistiwa, 8(1), 44-59. https://doi.org/10.33701/jipsk.v8i1.3184.
-
Rahmawati, A., Nurmawati, T., & Permata Sari, L. (2019). Faktor yang berhubungan dengan pengetahuan orang tua tentang stunting pada balita. Jurnal Ners Dan Kebidanan (Journal of Ners and Midwifery), 6(3), 389–395. https://doi.org/10.26699/jnk.v6i3.art.p389-395.
-
Sany, F. A. (2023). Penerapan Sistem Pakar untuk Deteksi Stunting. Jurnal Ilmiah Ecosystem, 23(3), 602-609. https://doi.org/10.35965/eco.v23i3.3774.
-
Satriawan, E. (2018). Strategi nasional percepatan pencegahan stunting 2018-2024. Jakata: Tim Nasional Percepatan Penanggulangan Kemiskinan (TNP2K).
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright and Licensing Agreement
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
- Authors retain full copyright of their work
- Authors grant the journal right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0)
- This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.