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September 10, 2025Jurnal NaturalOpen Access

Analysis of VAE-LSTM Performance in Detecting Anomalies in Average Daily Temperature Data in Jakarta 2000-2023

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Authors

IRINDRI RAMDANIYAYenni AngrainiIIINDAHWATI INDAHWATI

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Overview

Analysis reveals superior detection of temperature anomalies using VAE and LSTM models, indicating effective urban planning tools.

Key Points

  • The VAE-LSTM model achieved an F1-Score of 0.985, demonstrating high accuracy in detecting temperature anomalies.
  • Data analysis covered daily air temperature from Jakara, spanning from April 2000 to December 2023, emphasizing significant years for climate change.
  • This study integrated generative methodologies and temporal coding to detect anomalies through advanced deep learning techniques.
  • The results underscore the necessity for improved urban planning strategies in response to climate-related temperature variations.

Cite This Study

RAMDANI et al. (2025) studied this question.

synapsesocial.com/papers/68c1a40f54b1d3bfb60de9efhttps://doi.org/10.24815/jn.v25i2.41856
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