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All Journal IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences
Nur Rachman Dzakiyullah
Universitas Alma Ata, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Implementation of the Convolutional Neural Network Method to Detect the Use of Masks Arbiati Faizah; Pujo Hari Saputro; Augusta Jannatul Firdaus; Raden Nur Rachman Dzakiyullah
International Journal of Informatics and Information Systems Vol 4, No 1: March 2021
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v4i1.75

Abstract

The planet has been taken seriously by Coronavirus disease since the end of 2019. Wearing a mask in public is one of the key means of security for people. Furthermore, certain public service vendors only require clients to use the service if they wear masks correctly. However, based on image processing, there is relatively little study into the discovery of face masks. Almost everybody appears to wear a mask in order to shield themselves from the COVID-19 Pandemic. Monitoring whether people in the crowd wear face masks at the most public place, such as malls, museums, parks, has become increasingly important. The development of an AI approach to deal with if the person wears a face mask and their entrance would significantly assist society. In this article, we will use a deep learning model that is then combined with Keras / TensorFlow & OpenCV, respectively CNN or Confusional Neural Network. The accuracy of the research results obtained from this model is more than 96%.
Semi-Supervised Classification on Credit Card Fraud Detection using AutoEncoders Nur Rachman Dzakiyullah; Andri Pramuntadi; Anni Karimatul Fauziyyah
Journal of Applied Data Sciences Vol 2, No 1: JANUARY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i1.16

Abstract

The use of credit cards for online purchases has increased dramatically and led to an explosion in credit card fraud. Credit card companies need to be able to identify fraudulent credit card transactions so that customers are not charged for items they do not buy. In this study, we will use semi-supervised learning and combine it with AutoEncoders to identify fraudulent credit card transactions. In this paper, we will implement the use of T-SNE to visualize fraud and non-fraud transactions, then improve the visualization using autoencoders. Classification report proved that it is possible to achieve very acceptable precision using semi-supervised classification to detect credit card fraud.
Co-Authors Andri Pramuntadi Anni Karimatul Fauziyyah Arbiati Faizah Augusta Jannatul Firdaus Pujo Hari Saputro