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All Journal International Journal of Electrical and Computer Engineering Journal of Soft Computing Exploration
Mohamad, Masurah
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Computer Science & IT Electrical & Electronics Engineering

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Optimizing Seq2Seq LSTM for Regional-to-National language translation on a web platform Af'idah, Dwi Intan; Susanto, Ardi; Mohamad, Masurah; Alfat, Lathifah
Journal of Soft Computing Exploration Vol. 6 No. 1 (2025): March 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i1.561

Abstract

Machine translation for low-resource languages remains a significant challenge due to the lack of parallel corpora and optimized model configurations. This study developed and optimized a Seq2Seq Long Short-Term Memory (LSTM) model for Tegalan-to-Indonesian translation. A manually curated parallel corpus was constructed to train and evaluate the model. Various hyperparameter configurations were systematically tested, with the best-performing model achieving a BLEU score of 11.7381 using a dropout rate of 0.5, batch size of 64, learning rate of 0.01, and 70 training epochs. The results demonstrated that higher dropout rates, smaller batch sizes, and longer training durations enhanced model generalization and translation accuracy. The optimized model was deployed into a web-based application using Streamlit, ensuring accessibility for real-time translation. The findings highlighted the importance of hyperparameter tuning in neural machine translation for low-resource languages. Future research should explore Transformer-based architectures, larger datasets, and reinforcement learning techniques to further enhance translation quality and generalization.
Development and evaluation of a smart home energy management system using internet of things and real-time monitoring Ariff, Mohamed Imran Mohamed; Halim, Nur Anim Abdul; Abdullah, Mohammad Nasir; Ahmad, Samsiah; Mohamad, Masurah; Azmi, Anis Zafirah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3977-3985

Abstract

This project presents the design and implementation of a smart home energy management system using internet of things (IoT) technology to optimize household energy consumption. The system integrates various sensors, including passive infrared (PIR), light dependent resistor (LDR), and DHT11, to collect real-time environmental data, which is processed by a NodeMCU microcontroller. The microcontroller controls home appliances using relays, while the Blynk mobile app and Streamlit web platform provide users with remote monitoring and control capabilities. Despite successfully optimizing energy usage, the system faces limitations such as high sensor sensitivity and potential hazards during high-load power demonstrations. To address these issues, future work proposes integrating additional sensors for improved accuracy and incorporating renewable energy sources for increased sustainability. This project aims to enhance energy efficiency, provide users with greater control over their energy consumption, and contribute to smart home automation by utilizing real-time data, IoT integration, and user-friendly interfaces.
Co-Authors Abdullah, Mohammad Nasir Af'idah, Dwi Intan Ahmad, Samsiah Ardi Susanto Ariff, Mohamed Imran Mohamed Azmi, Anis Zafirah Halim, Nur Anim Abdul Lathifah Alfat, Lathifah