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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Buana Informatika Jurnal Transformatika Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi Jurnal CoreIT IT JOURNAL RESEARCH AND DEVELOPMENT Indonesian Journal of Artificial Intelligence and Data Mining JRST (Jurnal Riset Sains dan Teknologi) Techne : Jurnal Ilmiah Elektroteknika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Sistem Cerdas Applied Technology and Computing Science Journal JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknologi Informasi dan Terapan (J-TIT) International Journal of Informatics and Computation Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Jurnal Informatika dan Rekayasa Perangkat Lunak Respati Letters in Information Technology Education (LITE) Jurnal Teknik Informatika (JUTIF) Teknika Jurnal Computer Science and Information Technology (CoSciTech) Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) International Journal of Informatics Engineering and Computing
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Journal : International Journal of Informatics Engineering and Computing

Optimizing Sunspot Forecasts: An In-Depth Analysis of the ConcaveLSTM Model Ordiyasa, I Wayan; Diqi, Mohammad; Hiswati, Marselina Endah; Wandani, Aulia Fadillah Wani
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.103

Abstract

This work examines how effectively the ConcaveLSTM model can forecast sunspot numbers, recognizing their importance in space weather. The model addresses the complex and changing sunspot characteristics to improve forecasting accuracy. By comparing different model variations, this research identifies optimal combinations of input steps and LSTM units that enhance forecast performance while avoiding overfitting. The study showcases the capability of specific architectures concerning detail versus computational cost, using evaluation metrics such as RMSE, MAE, MAPE, and R2. Considering factors like limited data availability and the complexity of solar phenomena, the ConcaveLSTM model could be a valuable tool for predicting solar activity. This research advances understanding of space weather forecasting through machine learning and offers guidance for further model development and future investigations.