COMPARISON OF PERFORMANCE OF DEEP LEARNING AND RANDOM FOREST ALGORITHMS IN CLASSIFICATION OF STUNTING CASES IN TULUNGAGUNG REGION

  • Rahmad Syaifudin Universitas Tulungagung
Keywords: Classification, Deep Learning, Random Forest Stunting, Tulungagung Region

Abstract

Stunting is a significant public health problem, especially in Tulungagung. This phenomenon reflects nutritional imbalances and environmental conditions that affect children's growth. In order to improve understanding of the factors that contribute to stunting, this research focuses on comparing the performance of two main algorithms, namely Deep Learning and Random Forest, in the classification of stunting cases in the region. The evaluation is based on several metrics such as Relative Error, Standard Deviation, Gains, Total Time, Training Time (1,000 Rows), and Scoring Time (1,000 Rows). The results show that the Deep Learning model has a Relative Error of 0.3 with a Standard Deviation of 0.0, while the Random Forest model has a Relative Error of 0.4 with a Standard Deviation of 0.0. The Gains obtained by the Deep Learning model reached 1454.0, while the Random Forest model reached 622.0. The total time required by the Deep Learning model is 786.0, with Training Time (1,000 Rows) of 55.6 and Scoring Time (1,000 Rows) of 361.1. In contrast, the Random Forest model has a Total Time of 55.4, Training Time (1,000 Rows) of 361.1, and Scoring Time (1,000 Rows) of 55.4. This research provides an in-depth understanding of the performance comparison between Deep Learning and Random Forest algorithms in classifying stunting cases in the Tulungagung area, with consideration of time efficiency and prediction accuracy as determining factors for the success of model implementation.

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Published
2023-12-30
How to Cite
Rahmad Syaifudin. (2023). COMPARISON OF PERFORMANCE OF DEEP LEARNING AND RANDOM FOREST ALGORITHMS IN CLASSIFICATION OF STUNTING CASES IN TULUNGAGUNG REGION. INTERNATIONAL SEMINAR, 5, 390-401. https://doi.org/10.36563/proceeding.v5i0.145