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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat
Suharmanto, Eko Taufiq
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Computer Science & IT

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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.
PENERAPAN PEMBELAJARAN BERBASIS KONVENSIONAL DENGAN TEKNOLOGI INFORMASI PADA TPQ RAUDHATUL ‘ULUM MANYARAN KOTA SEMARANG Razaq, Jeffri Alfa; Supriyanto, Aji; Budiarso, Zuly; Suharmanto, Eko Taufiq; Kasprabowo, Teguh
Intimas Vol 5 No 1 (2025)
Publisher : Fakultas Teknologi Informasi dan Industri Unisbank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/intimas.v5i1.9931

Abstract

One of the places for community-based Islamic Religious Education (PAI) is through the Quran Education Park (TPQ). Not all TPQs implement qualified and modern learning methods and media according to the needs of today's education to realize the goals of PAI. The purpose of this service is to realize the goals of PAI at TPQ Raudahtul 'Ulum with conventional-based learning assistance and training methods with Information and Communication Technology (ICT) and digital. Assistance is carried out by installing ICT and digital devices with TPQ teachers. Meanwhile, training is carried out by combining ICT and digital-based learning media with Reading and Writing the Quran (BTA), books and Educational Game Tools (APE) with Islamic themes. The use of a combination of learning media aims to make learning easy, complete, interesting, creative and innovative, and can be done online and students can learn through their own gadgets. As a result, TPQ teachers have become proficient in installing ICT and digital devices and are able to understand and teach combination learning.
Assessment Of IDW And ANN On Daily Rainfall Data Imputation in Semarang Central Java Suharmanto, Eko Taufiq; Supriyanto, Aji
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14452

Abstract

Rainfall plays a critical role in the global water and energy cycle, influencing surface water availability and recharge processes both spatially and temporally. Traditional rainfall data collection using ombrometers provides accurate live data, but often faces the challenge of missing data due to equipment failure or transmission, especially in agencies such as BMKG. This problem of missing data greatly impacts hydrological analysis and requires an effective data recovery process through imputation. This study aims to assess the accuracy of rainfall data imputation techniques using the Inverse Distance Weighting (IDW) and Artificial Neural Network (ANN) methods. In this study, we utilize data from 31 observation stations in Semarang City for more than three decades. The findings show that the spatial distribution of rainfall is variable and exhibits a cyclic pattern despite fluctuations. The ANN model performed very well in overcoming missing data, especially in the dry season with an RMSE of 0.9489 and a coefficient of determination (R2) of 0.9926. By demonstrating the superiority of the ANN model in accurately predicting rainfall, this study offers an effective approach to improve the quality of BMKG climate data. This is expected to support disaster mitigation decisions and sustainable development planning. This approach demonstrates that the selection of an appropriate method is critical for accurate and reliable analysis of rainfall time series data. In addition to making an academic contribution, these results also provide an alternative imputation method for various time series.
Co-Authors Aji Supriyanto Amalina, Hana Damaryanti, Fitri Jeffri Alfa Razaq Kasprabowo, Teguh Zuly Budiarso