PreprintArticleVersion 1This version is not peer-reviewed
Hybrid Machine Learning for Stunting Prevalence: A Novel Comprehensive Approach to Classification, Prediction, and Clustering Optimization in Aceh, Indonesia
Version 1
: Received: 5 September 2024 / Approved: 5 September 2024 / Online: 6 September 2024 (09:33:32 CEST)
How to cite:
Hasdyna, N.; Dinata, R. K.; Rahmi, R.; Fajri, T. I. Hybrid Machine Learning for Stunting Prevalence: A Novel Comprehensive Approach to Classification, Prediction, and Clustering Optimization in Aceh, Indonesia. Preprints2024, 2024090485. https://doi.org/10.20944/preprints202409.0485.v1
Hasdyna, N.; Dinata, R. K.; Rahmi, R.; Fajri, T. I. Hybrid Machine Learning for Stunting Prevalence: A Novel Comprehensive Approach to Classification, Prediction, and Clustering Optimization in Aceh, Indonesia. Preprints 2024, 2024090485. https://doi.org/10.20944/preprints202409.0485.v1
Hasdyna, N.; Dinata, R. K.; Rahmi, R.; Fajri, T. I. Hybrid Machine Learning for Stunting Prevalence: A Novel Comprehensive Approach to Classification, Prediction, and Clustering Optimization in Aceh, Indonesia. Preprints2024, 2024090485. https://doi.org/10.20944/preprints202409.0485.v1
APA Style
Hasdyna, N., Dinata, R. K., Rahmi, R., & Fajri, T. I. (2024). Hybrid Machine Learning for Stunting Prevalence: A Novel Comprehensive Approach to Classification, Prediction, and Clustering Optimization in Aceh, Indonesia. Preprints. https://doi.org/10.20944/preprints202409.0485.v1
Chicago/Turabian Style
Hasdyna, N., Rahmi Rahmi and T. Irfan Fajri. 2024 "Hybrid Machine Learning for Stunting Prevalence: A Novel Comprehensive Approach to Classification, Prediction, and Clustering Optimization in Aceh, Indonesia" Preprints. https://doi.org/10.20944/preprints202409.0485.v1
Abstract
Stunting is a major public health issue in Aceh, Indonesia, requiring advanced analytical techniques for effective interventions. This study presents a novel hybrid machine learning framework designed to enhance the analysis of stunting through improved classification, predic-tive modeling, and clustering optimization. The framework utilizes Support Vector Machines (SVM) with Radial Basis Function (RBF) and Sigmoid kernels for classification. The RBF kernel achieved an accuracy of 91.3%, significantly outperforming the Sigmoid kernel's 85.6%. Linear Regression was employed for predictive modeling, yielding a Mean Squared Error (MSE) of 0.137, which indicates strong predictive accuracy. In clustering, the optimized K-Medoids method, in-corporating a weight product approach, demonstrated superior efficiency by requiring only 3 iterations for convergence, compared to 7 iterations for the conventional K-Medoids method. Additionally, it achieved a higher Calinski Harabasz Index of 93.7, compared to 85.2 for the conventional method. This comprehensive approach enhances accuracy and efficiency across classification, prediction, and clustering tasks, providing valuable insights for targeted interventions and policy development to address stunting in Aceh.
Keywords
stunting; machine learning; support vector machines; linear regression; k-medoids; clustering optimization; weight product; Aceh
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
