The DEVELOPMENT OF INTELLIGENT SOFTWARE FOR EARLY DETECTION OF STUNTING IN TODDLERS BASED ON ANTHROPOMETRY

Authors

  • Sri Widodo Widodo Universitas Duta Bangsa Surakarta
  • Anik Sulistiyanti - Universitas Duta Bangsa Surakarta

DOI:

https://doi.org/10.47701/icohetech.v3i1.2279

Keywords:

Anthropometry, Health Card, Stunting, SVM

Abstract

Stunting describes chronic undernutrition status during growth and development since early life. This situation is represented by the z-score of height for age (TB/U) less than -2 standard deviations (SD). The current method used to determine the presence of stunting in toddlers is to use Health Card. The way to do this is by weighing toddlers every month, the weighing results are recorded in the Health Card, and between the weight points from last month's weighing results and this month's weighing results are connected by a line. The series of children's growth lines form a child's growth graph. This procedure is of course less effective. In addition, midwives can differ from one another in determining stunting status in toddlers. The research aims to conduct early detection of stunting in toddlers based on anthropometry using an intelligent system. This research includes 3 (three) main things. The first is the development of software for managing children's medical record data. The second is the development of software for early detection of stunting using the Support Vector Machine (SVM). The third is the development of graph software for toddler anthropometric data development. The results of this study are expected to provide a tool for posyandu cadres and midwives in early detection of stunting in toddlers.

References

WHO. (2010). Nutrition landscape information system (NLIS) country profile indicators: Interpretation guide. Geneva: World Health Organization.

UNICEF. (2013). Improving child nutrition, the achievable imperative for global progress. New York: United Nations Children’s Fund.

Departemen Kesehatan Republik Indonesia. (2013). Laporan Riset Kesehatan Dasar tahun 2013. Jakarta: Balitbangkes.

Kukuh Eka Kusuma, Nuryanto, (2013), Faktor Risiko Kejadian Stunting Pada Anak Usia 2-3 Tahun (Studi di Kecamatan Semarang Timur), Journal of Nutrition College, Volume 2, Nomor 4, Tahun 2013, Halaman 523-530.

Khoirun Ni’mah, (2015), Faktor Yang Berhubungan Dengan Kejadian Stunting Pada Balita, Media Gizi Indonesia, Vol. 10, No. 1 Januari–Juni 2015: hlm. 13–19.

Hermaduanti, Ninki & Kusumadewi, Sri. Sistem Pendukung Keputusan Berbasis SMS untuk Menentukan Status Gizi dengan Metode K-Nearest Neighbor. Disampaikan pada Seminar Nasional Aplikasi Teknologi Informasi 2008. Yogyakarta.

Adi Wicaksono (2011), Model Spline Terbobot Untuk Merancang Kartu Menuju Sehat (KMS) Propinsi Jawa Timur, ITS Surabaya.

Blaschke,et al. (2009), Using Mobile Phone to Improve Child Nutrition Surveillance in Malawi. UNICEF Malawi and UNICEF Innovations.

Nugroho, A.S., Witarto, B.A., Handoko, D., (2003), Support Vector Machine –Teori dan Aplikasinya Dalam Bioinformatika, Kuliah Umum Ilmu Komputer.com.

Arif Muntasa, Muhammad Hariadi, Mauridhy Hery Purnomo (2009), A new Formulation of Face Sketch Multiple Features Detektion Using Pyramid Parameter Model dan Simultaneously Landmark Movement, International Journal of Computer Science Network and security, Vol 9.

Duda, R., Hart, P., and Stork, D. (2000), “Pattern Clasiffication”, Second Edition. J. Wiley and Sons, Inc.

Downloads

Published

2022-09-17