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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Department of Informatics, UIN Sunan Gunung Djati Bandung
2025-06-01
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| author | Maulana Ahsan Fadillah Yenni Angraini Rahma Anisa |
| author_facet | Maulana Ahsan Fadillah Yenni Angraini Rahma Anisa |
| author_sort | Maulana Ahsan Fadillah |
| collection | DOAJ |
| description | 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 climatic calamities. However, current weather monitoring systems frequently fall short of detecting small anomalies in time series weather data that could serve as early warning signs of such disasters. This study seeks to close this gap by creating a robust anomaly detection methodology adapted to time-dependent weather variables important to agriculture. In this study, a hybrid model combining Long Short-Term Memory (LSTM) autoencoder and One-Class Support Vector Machine (OCSVM) is proposed. The LSTM autoencoder's structure reconstructs time series data and signifies anomalies through reconstruction errors (MSE), while OCSVM validates these anomalies to reduce false positives. The model was applied to daily weather data from East Java spanning 2015–2024. The results showed that the model effectively detected 11 anomalies in sunlight duration and 7 in rainfall, with F1-scores of 0.71 and 0.82, respectively. Several of these anomalies corresponded to actual disaster events such as floods, landslides, and droughts. This research contributed to the field by demonstrating the effectiveness of combining deep learning and machine learning for weather anomaly detection. The proposed framework offers valuable insights for early warning systems and can support local governments and farmers in improving disaster preparedness and enhancing agricultural resilience in East Java. |
| format | Article |
| id | doaj-art-40bb9600e82a47f0a6decd94607ceb8a |
| institution | Kabale University |
| issn | 2528-1682 2527-9165 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Department of Informatics, UIN Sunan Gunung Djati Bandung |
| record_format | Article |
| series | JOIN: Jurnal Online Informatika |
| spelling | doaj-art-40bb9600e82a47f0a6decd94607ceb8a2025-08-20T03:25:55ZengDepartment of Informatics, UIN Sunan Gunung Djati BandungJOIN: Jurnal Online Informatika2528-16822527-91652025-06-0110122723810.15575/join.v10i1.15711576Enhancing Weather Monitoring for Agriculture with Deep Learning: Anomaly Detection in East Java Using LSTM Autoencoder and OCSVMMaulana Ahsan Fadillah0Yenni Angraini1Rahma Anisa2Study Program of Statistics and Data Science, IPB UniversityStudy Program of Statistics and Data Science, IPB UniversityStudy Program of Statistics and Data Science, IPB UniversityAgricultural 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 climatic calamities. However, current weather monitoring systems frequently fall short of detecting small anomalies in time series weather data that could serve as early warning signs of such disasters. This study seeks to close this gap by creating a robust anomaly detection methodology adapted to time-dependent weather variables important to agriculture. In this study, a hybrid model combining Long Short-Term Memory (LSTM) autoencoder and One-Class Support Vector Machine (OCSVM) is proposed. The LSTM autoencoder's structure reconstructs time series data and signifies anomalies through reconstruction errors (MSE), while OCSVM validates these anomalies to reduce false positives. The model was applied to daily weather data from East Java spanning 2015–2024. The results showed that the model effectively detected 11 anomalies in sunlight duration and 7 in rainfall, with F1-scores of 0.71 and 0.82, respectively. Several of these anomalies corresponded to actual disaster events such as floods, landslides, and droughts. This research contributed to the field by demonstrating the effectiveness of combining deep learning and machine learning for weather anomaly detection. The proposed framework offers valuable insights for early warning systems and can support local governments and farmers in improving disaster preparedness and enhancing agricultural resilience in East Java.https://join.if.uinsgd.ac.id/index.php/join/article/view/1571anomaly detectionlstm autoencoderlstmocsvmweather |
| spellingShingle | Maulana Ahsan Fadillah Yenni Angraini Rahma Anisa Enhancing Weather Monitoring for Agriculture with Deep Learning: Anomaly Detection in East Java Using LSTM Autoencoder and OCSVM JOIN: Jurnal Online Informatika anomaly detection lstm autoencoder lstm ocsvm weather |
| title | Enhancing Weather Monitoring for Agriculture with Deep Learning: Anomaly Detection in East Java Using LSTM Autoencoder and OCSVM |
| title_full | Enhancing Weather Monitoring for Agriculture with Deep Learning: Anomaly Detection in East Java Using LSTM Autoencoder and OCSVM |
| title_fullStr | Enhancing Weather Monitoring for Agriculture with Deep Learning: Anomaly Detection in East Java Using LSTM Autoencoder and OCSVM |
| title_full_unstemmed | Enhancing Weather Monitoring for Agriculture with Deep Learning: Anomaly Detection in East Java Using LSTM Autoencoder and OCSVM |
| title_short | Enhancing Weather Monitoring for Agriculture with Deep Learning: Anomaly Detection in East Java Using LSTM Autoencoder and OCSVM |
| title_sort | enhancing weather monitoring for agriculture with deep learning anomaly detection in east java using lstm autoencoder and ocsvm |
| topic | anomaly detection lstm autoencoder lstm ocsvm weather |
| url | https://join.if.uinsgd.ac.id/index.php/join/article/view/1571 |
| work_keys_str_mv | AT maulanaahsanfadillah enhancingweathermonitoringforagriculturewithdeeplearninganomalydetectionineastjavausinglstmautoencoderandocsvm AT yenniangraini enhancingweathermonitoringforagriculturewithdeeplearninganomalydetectionineastjavausinglstmautoencoderandocsvm AT rahmaanisa enhancingweathermonitoringforagriculturewithdeeplearninganomalydetectionineastjavausinglstmautoencoderandocsvm |