Enhancing Weather Monitoring for Agriculture with Deep Learning: Anomaly Detection in East Java Using LSTM Autoencoder and OCSVM

Enhancing Weather Monitoring for Agriculture with Deep Learning: Anomaly Detection in East Java Using LSTM Autoencoder and OCSVM

Agricultural productivity in East Java is under threat from unpredictable and harsh weather patterns, particularly rapid variations in sunlight length and rainfall intensity. These abnormalities can interrupt agricultural cycles, lower yields, and make farming communities more vulnerable to climati...

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Bibliographic Details
Main Authors:	Maulana Ahsan Fadillah, Yenni Angraini, Rahma Anisa
Format:	Article
Language:	English
Published:	Department of Informatics, UIN Sunan Gunung Djati Bandung 2025-06-01
Series:	JOIN: Jurnal Online Informatika
Subjects:	anomaly detection lstm autoencoder lstm ocsvm weather
Online Access:	https://join.if.uinsgd.ac.id/index.php/join/article/view/1571
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