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All Journal Jurnal ULTIMA Computing TESLA: Jurnal Teknik Elektro Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan Engineering, Mathematics and Computer Science Journal (EMACS) Green Intelligent Systems and Applications
Meirista Wulandari, Meirista
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : Engineering, Mathematics and Computer Science Journal (EMACS)

 1 
Optimizer Comparison In Convolutional Neural Network For Real Time Face Recognition Elbert, Elbert; Wulandari, Meirista; Fat, Joni
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.v7i1.12058

Abstract

Face recognition is one of the computer vision technologies that's used in many industries. Face recognition always used in various sector that require the verification of an individual identity. There are many ways that can be used to develop face recognition, one of them is convolutional neural network. Convolutional neural network (CNN) is a deep learning neural network that is created specifically to process and analyze visual data, such as images and videos. CNN have the ability to learn many features from visual data, making them highly effective for tasks like face recognition. There are many factors that can affect CNN performance including the optimizers that are used in the neural network. Optimizers are the algorithm that adjust weights of the neural network to minimize error between the predicted output and actual target. This study used 10 different subjects for face recognition. In this study, the CNN model uses a training algorithm called backpropagation then will compare 3 different types of optimizers. The optimizers that used in this study are Adaptive Momentum (Adam), Root Mean Square Propagation (RMSProp), and Stochastic Gradient Descent (SGD). The results of the comparison will be shown in the form of performance metrics. The performance metrics include correct classification rate (CCR) as well as the confusion matrix of each model. CNN model with SGD optimizers has the highest CCR of 97.07%.
Computer Resource Utilization Analysis for Microsoft Excel and Python in Data Processing Kelvin, Kelvin; Wahab, Wahidin; Wulandari, Meirista
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.v6i2.11736

Abstract

Data analysis is essential for gaining insights and making informed decisions. A crutial step in data analysis is data processing, which involves preparing and filtering raw data to ensure accuracy, consistency, and structure. While Microsoft Excel is commonly used for data processing, it is susceptible to human errors and has limitations in handling large datasets. Python provides an alternative by automating data processing through scripts executed by the interpreter. The superior software for data processing is obtained by comparing the computer resource utilization based on statistical theory approach, Wilcoxon signed-rank test. This test is appropriate because it does not require the assumption of a normal distribution, providing flexibility in comparing computer resource utilization between Microsoft Excel and Python. Microsoft Excel and Python proceed *.csv and *.xlsx files, then Task Manager recorded the data of computer resource utilization for each processing step. The Wilcoxon signed-rank test analyzes the data and evaluating two hypotheses. H0 (there is no any significant differences in computer resource utilization between Microsoft Excel and Python are calculated for each data processing) and H1 (there is significant differences in computer resource utilization between Microsoft Excel and Python are calculated for each data processing). The sum of ranks in Wilcoxon test are compared to the critical value from the Wilcoxon distribution table to determine the accepted hypothesis. Based on the Wilcoxon test results, hypothesis H1 is accepted, indicating a significant difference in computer resource utilization between Microsoft Excel and Python.
Co-Authors Adrianto, Christopher Calvin Beatrix, Maria Elbert, Elbert Fat, Joni Fawzi, Ahmad Hugeng Kelvin Kelvin Pranoto, Wati Asriningsih Redja, Christie Suraidi Wahidin Wahab