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All Journal International Journal of Electrical and Computer Engineering Jurnal Informatika Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Alam, Islam Nur
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Enhancing El Niño-Southern oscillation prediction using an attention-based sequence-to-sequence architecture Setiawan, Karli Eka; Fredyan, Renaldy; Alam, Islam Nur
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7057-7066

Abstract

The ability to accurately predict the EI Nino-Southern oscillation (ENSO) is essential for seasonal climate forecasting. Monitoring the Pacific Ocean's surface temperature has many benefits for human life, including a better understanding of climate and weather, the ability to predict summer and winter, the ability to manage natural resources, serving as a reference for maritime transportation and navigation needs, serving as a reference for climate change monitoring needs, and even serving as a renewable energy source by utilizing high sea surface temperatures. This study introduces a deep learning (DL) model with AttentionSeq2Luong model as our proposed model to the ENSO research community. The present study showcases the capability of our proposed model to effectively forecast the forthcoming monthly average Nino index compared to the baseline seq2seq architecture model. For the dataset, this study utilized monthly observations of Nino 12, Nino 3, Nino 34, and Nino 4 between January 1870 and August 2022. The brief result of our experiment was that applying Luong Attention in the seq2seq model reduced the RMSE error by around 0.03494, 0.04635, 0.03853, and 0.03892 for forecasting Nino 12, Nino 3, Nino 34, and Nino 4, respectively.
Indonesian multilabel classification using IndoBERT embedding and MBERT classification Nabiilah, Ghinaa Zain; Alam, Islam Nur; Purwanto, Eko Setyo; Hidayat, Muhammad Fadlan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1071-1078

Abstract

The rapid increase in social media activity has triggered various discussion spaces and information exchanges on social media. Social media users can easily tell stories or comment on many things without limits. However, this often triggers open debates that lead to fights on social media. This is because many social media users use toxic comments that contain elements of racism, radicalism, pornography, or slander to argue and corner individuals or groups. These comments can easily spread and trigger users vulnerable to mental disorders due to unhealthy and unfair debates on social media. Thus, a model is needed to classify comments, especially toxic ones, in Indonesian. Transformer-based model development and natural language processing approaches can be applied to create classification models. Some previous research related to the classification of toxic comments has been done, but the classification results of the model still require exploration to get optimal results. So, this research uses the proposed model by using different pre-trained models at the embedding and classification stages, in the embedding stage using Indonesia bidirectional encoder representations from transformers (IndoBERT), and classification using multilingual bidirectional encoder representations from transformers (MBERT). The proposed model provides optimal results with an F1 value of 0.9032.
Co-Authors Alauddin, Muhammad Fahmi Fredyan, Renaldy Hidayat, Muhammad Fadlan Ida Ayu Putu Sri Widnyani Nabiilah, Ghinaa Zain Purwanto, Eko Setyo Putra, Rizqi Ari Setiawan, Karli Eka Sihotang, Erna Fransisca Angela