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All Journal Jurnal Teknik Informatika (JUTIF) ITIJ
Damaryanti, Fitri
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Computer Science & IT Electrical & Electronics Engineering

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DROUGHT PREDICTION USING LSTM MODEL WITH STANDARDIZED PRECIPITATION INDEX ON THE NORTH COAST OF CENTRAL JAVA Supriyanto, Aji; Zuliarso, Eri; Suharmanto, Eko Taufiq; Amalina, Hana; Damaryanti, Fitri
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.4159

Abstract

Fluctuating weather can trigger hydrometeorological disasters, especially affecting farmers and fishermen on the north coast of Central Java. Weather predictions including drought are very important to anticipate drought disasters. Deep learning-based prediction models such as Long Short Term Memory (LSTM) are used in an effort to reduce the impact of drought. The purpose of this study is to prove the level of accuracy of the LSTM model and determine the drought index with the Standardized Precipitation Index (SPI). The LSTM model is used to predict drought based on the SPI, while the SPI acts as a drought index that considers precipitation (rainfall) for a period of 1, 3, and 6 months. Predictions use rainfall data obtained from online data from the Central Java BMKG UPT Indonesia for the period 2010-2023 in the Tegal City and Semarang City station areas. The results of data treatment with LSTM can effectively analyze and capture complex patterns in meteorological data to predict drought events accurately. The effectiveness of the model is shown by the relatively small MAE and RMSE results, namely MAE 0.163 - 0.352 and RMSE 0.247-0.515. The best prediction result is the 3-month SPI in the Semarang area with MAE 0.163 and RMSE 0.274. While the prediction result with the largest error is the 1-month SPI in the Tegal area. Drought modeling using LSTM has been successfully implemented for the northern coast of Central Java using the Streamlit Framework and can process and visualize the drought prediction system well.
Optimization of the SRIKANDI E-Government System Using XGBoost-Based Classification and One-Class SVM Anomaly DetectionType Damaryanti, Fitri; Aji Supriyanto
Information Technology International Journal Vol. 3 No. 1 (2025): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v3i1.50

Abstract

Accurate and efficient digital archive management is a crucial component of Electronic-Based Government Systems (SPBE) in Indonesia. The Integrated Dynamic Archival Information System (SRIKANDI), widely used by government agencies, continues to face various challenges such as incomplete metadata, inconsistent classification, and difficulties in archive retrieval and retention scheduling. This study aims to optimize the SRIKANDI system by implementing machine learning algorithms XGBoost for document classification and One-Class SVM (OCSVM) for automatic anomaly detection in metadata. The methodology involves data preprocessing, feature selection, label generation, and the application of classification and anomaly detection models on archival data from the Meteorological, Climatological, and Geophysical Agency (BMKG), Central Java. The XGBoost model achieved a classification accuracy of 77%, showing strong performance in identifying "Destructible" archives but limited ability in detecting the "Permanent" category due to data imbalance. Meanwhile, the OCSVM model successfully identified 16 anomalous entries (9.14%) out of 175 archives, with key indicators including extreme item counts and illogical retention periods. The results demonstrate that integrating machine learning into digital archival systems significantly improves classification accuracy, operational efficiency, and metadata integrity. Furthermore, this approach supports proactive auditing and validation of archival metadata. The findings offer valuable insights for developing AI-powered archival classification and anomaly detection systems to enhance accountability, transparency, and data governance in the public sector.
Co-Authors Aji Supriyanto Amalina, Hana Suharmanto, Eko Taufiq