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INDONESIA
International Journal Of Computer, Network Security and Information System (IJCONSIST)
ISSN : -     EISSN : 26863480     DOI : https://doi.org/10.33005/ijconsist.v3i1
Core Subject : Science,
Focus and Scope The Journal covers the whole spectrum of intelligent informatics, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Autonomous Agents and Multi-Agent Systems • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Cognitive systems • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, fault analysis and diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High Performance Computing • Information storage, security, integrity, privacy and trust • Image and Speech Signal Processing • Knowledge Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Memetic Computing • Multimedia and Applications • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Stochastic systems • Support Vector Machines • Ubiquitous, grid and high performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data
Articles 91 Documents
Implementation of the Naive Bayes Method for Stunting Classification in Children Under Five Years Old (Balita). Sulthan Ahmad, Ferdiansyah; Muhammad Farhan , Maulana; Akmal Aliffandhi , Anwar; Muhammad Rifki Bahrul , Ulum; I Gede Susrama , Diyasa; Vinza Hedi, Satria
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.157

Abstract

The issue of stunting in Indonesia has become a serious concern, drawing significant attention from the government. To address this problem, the government has set a target to reduce the stunting prevalence rate to 14% by 2024. As an initial step in supporting this goal, the present study aims to classify the nutritional status of children under five years old using the Naive Bayes method. The objective of this research is to evaluate the performance of the Naive Bayes algorithm in classifying the nutritional status of children under five, with a focus on body weight, height, and exclusive breastfeeding intake as predictors of stunting. The research process includes several stages, namely problem formulation, data collection, data preprocessing, data splitting, model construction, model training, model evaluation, and result analysis. The findings of this study indicate that the Naive Bayes method achieved an accuracy of 70% in classifying stunting among children under the age of five, with an F1-score evaluation of 70%.

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