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All Journal Bulletin of Electrical Engineering and Informatics Sinergi
Mohamad, Fatma Susilawati
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Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Offline signature verification using long short-term memory and histogram orientation gradient Alsuhimat, Fadi Mohammad; Mohamad, Fatma Susilawati
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4024

Abstract

The signing process is a critical step that organizations take to ensure the confidentiality of their data and to safeguard it against unauthorized penetration or access. Within the last decade, offline handwritten signature research has grown in popularity as a common method for human authentication via biometric features. It is not an easy task, despite the importance of this method; the struggle in such a system stem from the inability of any individual to sign the same signature each and every time. Additionally, we are indeed interested in the dataset’s features that could affect the model's performance; thus, from extracted features from the signature images using the histogram orientation gradient (HOG) technique. In this paper, we suggested a long short-term memory (LSTM) neural network model for signature verification, with input data from the USTig and CEDAR datasets. Our model’s predictive ability is quite outstanding: The classification accuracy efficiency LSTM for USTig was 92.4% with a run-time of 1.67 seconds and 87.7% for CEDAR with a run-time of 2.98 seconds. Our proposed method outperforms other offline signature verification approaches such as K-nearest neighbour (KNN), support vector machine (SVM), convolution neural network (CNN), speeded-up robust features (SURF), and Harris in terms of accuracy.
Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network Kurniawan, Rudi; Samsuryadi, Samsuryadi; Mohamad, Fatma Susilawati; Wijaya, Harma Oktafia Lingga; Santoso, Budi
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.019

Abstract

The palm oil industry faces significant challenges in accurately classifying fruit ripeness, which is crucial for optimizing yield, quality, and profitability. Manual methods are slow and prone to errors, leading to inefficiencies and increased costs. Deep Learning, particularly the AlexNet architecture, has succeeded in image classification tasks and offers a promising solution. This study explores the implementation of AlexNet to improve the efficiency and accuracy of palm oil fruit maturity classification, thereby reducing costs and production time. We employed a dataset of 1500 images of palm oil fruits, meticulously categorized into three classes: raw, ripe, and rotten. The experimental setup involved training AlexNet and comparing its performance with a conventional Convolutional Neural Network (CNN). The results demonstrated that AlexNet significantly outperforms the traditional CNN, achieving a validation loss of 0.0261 and an accuracy of 0.9962, compared to the CNN's validation loss of 0.0377 and accuracy of 0.9925. Furthermore, AlexNet achieved superior precision, recall, and F-1 scores, each reaching 0.99, while the CNN scores were 0.98. These findings suggest that adopting AlexNet can enhance the palm oil industry's operational efficiency and product quality. The improved classification accuracy ensures that fruits are harvested at optimal ripeness, leading to better oil yield and quality. Reducing classification errors and manual labor can also lead to substantial cost savings and increased profitability. This study underscores the potential of advanced deep learning models like AlexNet in revolutionizing agricultural practices and improving industrial outcomes.
Co-Authors Alsuhimat, Fadi Mohammad Dwi Budi Santoso Lingga Wijaya, Harma Oktafia Rudi Kurniawan Samsuryadi Samsuryadi