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All Journal Populer: Jurnal Penelitian Mahasiswa Journal of Information Technology
Dechy Deswita Indriani.S
Universitas Negeri Medan
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Identifikasi Tanda Tangan Dengan Menggunakan Metode Convolution Neural Network (CNN) Dechy Deswita Indriani.S; Elya Juni Arta Sinaga; Grace Oktavia; Hermawan Syahputra; Fanny Ramadhani
J-INTECH ( Journal of Information and Technology) Vol 12 No 1 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i1.1273

Abstract

This research aims to develop and evaluate a Convolutional Neural Network (CNN) model for signature identification. The CNN method is chosen for its capability to extract and analyze complex visual features from signature images. The data used in this study consists of a collection of signature images divided into training and testing sets. The proposed CNN model comprises several convolutional, pooling, and fully connected layers optimized for classification tasks. Evaluation results indicate that the CNN model achieves excellent performance with an accuracy of 0.97, demonstrating high accuracy and precision in signature recognition. With these results, CNN proves to be an effective and reliable method for signature identification, making a significant contribution to the field of biometric identity verification. These findings open opportunities for further applications in security and authentication systems requiring automatic signature recognition.
Klasifikasi Batik Parang Menggunakan Convolutional Neural Network (CNN) Angginy Akhirunnisa Siregar; Citra Citra; Dechy Deswita Indriani.S; Gifari Dhaffa Prawira Sianturi
Populer: Jurnal Penelitian Mahasiswa Vol. 3 No. 1 (2024): Maret : Jurnal Penelitian Mahasiswa
Publisher : Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/populer.v3i1.1666

Abstract

Batik culture is very strong in Indonesia, this is the reason that batik can be found throughout the archipelago, with unique characteristics that distinguish it in each region. However, people are often confused and find it difficult to recognize one type of batik from another. One of the famous types of batik motif is Batik Parang. This research aims to establish a Convolutional Neural Network (CNN) model to classify Batik Parang and help people distinguish it from other batik motifs. Deep learning, particularly CNN, was chosen because it has a high accuracy rate in image classification. A quantitative Experimental design is used, using a dataset of 100 batik images evenly divided into two classes, namely Batik Parang and not Batik Parang. The dataset is divided into two categories, namely training data and testing data, with a data ratio of 80:20. Thus, by using Convolutional Neural Network (CNN), the classification between Batik Parang and not Batik Parang produces an accuracy of 95%, with the use of epoch = 118 and batch_size = 100.
Co-Authors Angginy Akhirunnisa Siregar Citra Citra Elya Juni Arta Sinaga Gifari Dhaffa Prawira Sianturi Grace Oktavia Hermawan Syahputra Ramadhani, Fanny