Modality-based Modeling with Data Balancing and Dimensionality Reduction for Early Stunting Detection

Authors

  • Yohanes Setiawan Department of Information Technology, Telkom University, Surabaya Campus, Surabaya, Indonesia
  • Mohammad Hamim Zajuli Al Faroby Department of Data Science, Telkom University, Surabaya Campus, Surabaya, Indonesia https://orcid.org/0000-0001-6500-270X
  • Mochamad Nizar Palefi Ma’ady Department of Information Systems, Telkom University, Surabaya Campus, Surabaya, Indonesia
  • I Made Wisnu Adi Sanjaya Department of Data Science, Telkom University, Surabaya Campus, Surabaya, Indonesia
  • Cisa Valentino Cahya Ramadhani Department of Information Technology, Telkom University, Surabaya Campus, Surabaya, Indonesia

DOI:

https://doi.org/10.15575/join.v10i1.1495

Keywords:

Data Balancing, Dimensionality Reduction, Multimodal, Stunting, Unimodal

Abstract

In Indonesia, the stunting rate has reached 36%, significantly higher than the World Health Organization's (WHO) standard of 20%. This high prevalence underscores the urgent need for effective early detection methods. Traditional data mining approaches for stunting detection have primarily focused on unimodal data, either tabular or image data alone, li