Publication Search

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.

The advent of the Internet as an effective means for resource sharing has consequently, led to proliferation of adversaries, with unauthorized access to network resources. Adversaries achieved fraudulent activities via carefully crafted attacks of large magnitude targeted at personal gains and rewards. With the cost of over $1.3Trillion lost globally to financial crimes and the rise in such fraudulent activities vis the use of credit-cards, financial institutions and major stakeholders must begin to explore and exploit better and improved means to secure client data and funds. Banks and financial services must harness the creative mode rendered by machine learning schemes to help effectively manage such fraud attacks and threats. We propose HyGAMoNNE – a hybrid modular genetic algorithm trained neural network ensemble to detect fraud activities. The hybrid, equipped with knowledge to altruistically detect fraud on credit card transactions. Results show that the hybrid effectively differentiates, the benign class attacks/threats from genuine credit card transaction(s) with model accuracy of 92%.