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All Journal International Journal of Electrical and Computer Engineering Momentum: Physics Education Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Fredyan, Renaldy
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Physics

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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.
A rasch model approach to gender-based thermal concept analysis in East Java high schools Irsalina, Fian Rifqi; Aviyanti, Lina; Fredyan, Renaldy
Momentum: Physics Education Journal Vol. 9 No. 1 (2025)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/mpej.v9i1.11007

Abstract

This research analyzes high school students' understanding of thermal concepts in East Java, focusing on identifying gender-based differences and misconceptions. A descriptive survey with a non-experimental cross-sectional design was conducted using the Thermal Concept Evaluation (TCE) instrument, distributed via Google Forms to 171 students (94 females and 77 males). Data were analyzed using the Rasch model with Winsteps software, providing a detailed diagnosis of student ability levels and item difficulties. The findings revealed significant misconceptions about thermal conductivity and thermal equilibrium, which were challenging for both genders. Male students demonstrated better comprehension of heat transfer and temperature change, while female students showed a slightly higher understanding of boiling concepts. Practical implications include the need for targeted instructional strategies, such as inquiry-based and problem-based learning, incorporating hands-on experiments and technology-based tools like simulations to visualize abstract thermal phenomena. Policymakers and educators are encouraged to adopt conceptual-based curricula, improve laboratory facilities, and provide teacher training programs to address misconceptions and bridge gender gaps in thermal physics education. These strategies aim to enhance students' understanding and prepare them for advanced applications in science and technology.
Leveraging Support Vector Machines and Ensemble Learning for Early Diabetes Risk Assessment: A Comparative Study Shiddiqi, Hafizh Ash; Setiawan, Karli Eka; Fredyan, Renaldy
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 1 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i1.12846

Abstract

Currently, diabetes is a hidden, serious threat to human lifestyles through daily food and drink, which has become a formidable global health challenge. As a contribution, this study suggests a way to use machine learning to find people with diabetes by looking at certain health parameters. It does this by using different Support Vector Machine (SVM)-based models, such as different SVMs with different kernels, such as linear, polynomial, radial basis function, and sigmoid kernels; different ensemble bagging with SVM; and different ensemble stacking with various SVM models. The findings demonstrated that utilizing a single SVM model with a linear kernel, ensemble bagging with a linear SVM, and ensemble stacking with different SVM models yielded the most accurate results, achieving 95% accuracy in both diabetes presence and absence. This lends credence to the idea that the incorporation of a linear kernel has the potential to improve the accuracy of determining whether or not diabetic illness is present.
Enhancing spatiotemporal weather forecasting accuracy with 3D convolutional kernel through the sequence to sequence model Fredyan, Renaldy; Setiawan, Karli Eka; Minor, Kelvin Asclepius
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2022-2030

Abstract

Accurate weather forecasting is important when dealing with various sectors, such as retail, agriculture, and aviation, especially during extreme weather events like heat waves, droughts, and storms to prevent disaster impact. Traditional methods rely on complex, physics-based models to predict the Earth's stochastic systems. However, some technological advancements and the availability of extensive satellite data from beyond Earth have enhanced meteorological predictions and sent them to Earth's antennae. Deep learning models using this historical data show promise in improving forecast accuracy to enhance how models learn the data pattern. This study introduces a novel architecture, convolutional sequence to sequence (ConvSeq2Seq) network, which employs 3D convolutional neural networks (CNN) to address the challenges of spatiotemporal forecasting. Unlike recurrent neural network (RNN)--based models, which are time-consuming due to sequential processing, 3D CNNs capture spatial context more efficiently. ConvSeq2Seq overcomes the limitations of traditional CNN models by ensuring causal constraints and generating flexible length output sequences. Our experimental results demonstrate that ConvSeq2Seq outperforms traditional and modern RNN-based architectures in both prediction accuracy and time efficiency, leveraging historical meteorological data to provide a robust solution for weather forecasting applications. The proposed architecture outperforms the previous method, giving new insight when dealing with spatiotemporal with high density.
Antiviral Medication Prediction Using A Deep Learning Model of Drug-Target Interaction for The Coronavirus SARS-COV Fredyan, Renaldy
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 2 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i2.11290

Abstract

Graph convolutional neural networks (GCNs) have shown promising performance in modeling graph data, particularly for small-scale molecules. Message-passing neural networks (MPNNs) are an important form of GCN variant. They excel at gathering and integrating particular information about molecules via several repetitions of message transmission. This capability has resulted in major advances in molecular modeling and property prediction. By combining the self-attention mechanism with MPNNs, there is potential to improve molecular representation while using Transformers' proven efficacy in other artificial intelligence disciplines. This research introduces a transformer-based message-passing neural network (T-MPNN) that is intended to improve the process of embedding molecular representations for property prediction. Our technique incorporates attention processes into MPNNs' message-passing and readout phases, resulting in molecular representations that are seamlessly integrated. The experimental results from three datasets show that T-MPNN outperforms or equals cutting-edge baseline models in tasks involving quantitative structure-property connections. By studying case studies of SARS-COV growth inhibitors, we demonstrate our model's ability to graphically depict attention at the atomic level. This enables us to pinpoint individual chemical atoms or functional groups linked with desirable biological properties. The model we propose improves the interpretability of classic MPNNs and is a useful tool for investigating the impact of self-attention on chemical substructures and functional groups in molecular representation learning. This leads to a better understanding of medication modes of action.
Co-Authors Alam, Islam Nur Irsalina, Fian Rifqi Lina Aviyanti Minor, Kelvin Asclepius Setiawan, Karli Eka Shiddiqi, Hafizh Ash