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All Journal JOIV : International Journal on Informatics Visualization
Mohmad Hassim, Yana Mazwin
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Computer Science & IT

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Improved Image Classification Task Using Enhanced Visual Geometry Group of Convolution Neural Networks Zakaria, Nurzarinah; Mohmad Hassim, Yana Mazwin
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1752

Abstract

Convolutional Neural Networks (CNNs) have become essential to solving image classification tasks. One of the most frequent models of CNNs for image classification is the Visual Geometry Group (VGG). The VGG architecture is made up of multiple layers of convolution and pooling processes followed by fully connected layers. Among the various VGG models, the VGG16 architecture has gained great attention due to its remarkable performance and simplicity. However, the VGG16 architecture is still prone to have many parameters contributing to its complexity. Moreover, the complexity of VGG16 may cause a longer execution time. The complexity of VGG16 architecture is also more highly prone to overfitting and may affect the classification accuracy. This study proposes an enhancement of VGG16 architecture to overcome such drawbacks. The enhancement involved the reduction of the convolution blocks, implementing batch normalization (B.N.) layers, and integrating global average pooling (GAP) layers with the addition of dense and dropout layers in the architecture. The experiment was carried out with six benchmark datasets for image classification tasks. The results from the experiment show that the network parameters are 79% less complex than the standard VGG16. The proposed model also yields better classification accuracy and shorter execution time. Reducing the parameters in the proposed improved VGG architecture allows for more efficient computation and memory usage. Overall, the proposed improved VGG architecture offers a promising solution to the challenges of long execution times and excessive memory usage in VGG16 architecture. 
Customer Loyalty Prediction for Hotel Industry Using Machine Learning Approach Hamdan, Iskandar Zul Putera; Othman, Muhaini; Mohmad Hassim, Yana Mazwin; Marjudi, Suziyanti; Mohd Yusof, Munirah
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1335

Abstract

Today, machine learning is utilized in several industries, including tourism, hospitality, and the hotel industry. This project uses machine learning approaches such as classification to predict hotel customers’ loyalty and develop viable strategies for managing and structuring customer relationships. The research is conducted using the CRISP-DM technique, and the three chosen classification algorithms are random forest, logistic regression, and decision tree. This study investigated key characteristics of merchants’ customers’ behavior, interest, and preference using a real-world case study with a hotel booking dataset from the C3 Rewards and C3 Merchant systems. Following a comprehensive investigation of prospective preferences in the pre-processing phase, the best machine learning algorithms are identified and assessed for forecasting customer loyalty in the hotel business. The study's outcome was recorded and examined further before hotel operators utilized it as a reference. The chosen algorithms are developed utilizing Python programming language, and the analysis result is evaluated using the Confusion Matrix, specifically in terms of precision, recall, and F1-score. At the end of the experiment, the accuracy values generated by the logistic regression, decision tree, and random forest algorithms were 57.83%, 71.44%, and 69.91%, respectively. To overcome the limits of this study method, additional datasets or upgraded algorithms might be utilized better to understand each algorithm's benefits and limitations and achieve further advancement. 
Co-Authors Hamdan, Iskandar Zul Putera Marjudi, Suziyanti Muhaini Othman, Muhaini Munirah Mohd Yusof, Munirah Zakaria, Nurzarinah