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All Journal Electronic Journal of Education, Social Economics and Technology
Ida Nurhaida
Universitas Pembangunan Jaya, South Tangerang, 15413
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Veterinary Other

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Signature Originality Verification Using A Deep Learning Approach Muhammad Azi Saputra; Ida Nurhaida
Electronic Journal of Education, Social Economics and Technology Vol 5, No 1 (2024)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v5i1.310

Abstract

The rapid advancement of digital technology has heightened the need for reliable methods to verify signature authenticity, a critical aspect of document and transaction security. This study uses a deep learning approach to develop a mobile application to verify the originality of paper and digital media signatures. The dataset comprises 1,060 signature images, including authentic and forged categories for both media types. The system employs the EfficientNetV2M model, trained with augmented data, to enhance robustness. Model evaluation demonstrates strong performance with an accuracy of 82.07%, a global precision of 81.31%, a global recall of 83.25%, and a global F1-score of 82.18%. The model is implemented in an Android-based mobile application, providing an intuitive interface for users to upload and verify signatures in real time. These results underscore the potential of EfficientNetV2M for mitigating signature fraud across various domains while highlighting areas for improvement, particularly in classifying paper-based signatures. Future work will focus on expanding the dataset and refining feature extraction techniques to enhance classification performance.
LSTM-Based NLP Approach for Spelling Error Detection and Correction in Scientific Writing Indonesian Language Yeru Dwi Pratama Halim; Ida Nurhaida
Electronic Journal of Education, Social Economics and Technology Vol 5, No 1 (2024)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v5i1.309

Abstract

Scientific writing requires precision and clarity to uphold credibility and effective communication. Errors such as spelling mistakes and typos can compromise the quality and reliability of scientific texts. This study proposes a Long Short-Term Memory (LSTM)-based approach to detect and correct spelling errors, enhancing text accuracy and readability. The dataset comprises 45,698 standard words, supplemented with typo variations to improve model performance. Data is sourced from the Indonesian Dictionary (KBBI) and undergoes normalization and preprocessing to capture diverse error patterns. The model’s performance is evaluated using a confusion matrix, achieving 93% accuracy and high precision, recall, and F1-score metrics. These results demonstrate that the proposed NLP-based LSTM model offers an effective and reliable solution for identifying and correcting spelling errors. This approach significantly enhances the quality of scientific writing, ensuring more transparent and credible communication.
Co-Authors Muhammad Azi Saputra Yeru Dwi Pratama Halim