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All Journal Jurnal Mantik International Journal of Data Science, Engineering, and Analytics (IJDASEA)
Dhian Satria Yudha Kartika
UPN Veteran Jawa Timur
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Classification Of Sexually Transmitted Infectional Diseases Using Artificial Neural Networks Agung Mustika Rizki; Hendra Maulana; Dhian Satria Yudha Kartika; Gusti Eka Yuliastuti
Jurnal Mantik Vol. 5 No. 3 (2021): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Many diseases are caused by bacteria, some of which can be easily noticed by ordinary people for immediate treatment. However, not with this one disease, namely sexually transmitted infections (STIs). This STI disease can be spread mainly through sexual intercourse, both vaginal, anal and oral sex. Some STI diseases can also be transmitted in non-sexual ways, such as through needles, blood or other blood products. Indonesia is one of the countries whose handling can be said to be not optimal as in several other countries. This is the result of a lack of education on STI diseases in the community. Based on this background, it can be concluded that there is a need for an intelligent system to classify STI diseases based on their symptoms. Therefore, the authors propose this research topic by applying the Artificial Neural Network method. Based on the test results, the application of the Artificial Neural Network method shows that 80% of the predicted data is in accordance with the actual data.
A Fraud Detection Implementation Of Decision Tree C4.5 Algorithm For Fraud Detection On Anonymous Credit Card Transaction Ulfa Nur Ulfa Mauludina; Dhian Satria Yudha Kartika; Ananda Devi Muri Utomo
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 2 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i2.36

Abstract

The development of technology today makes credit cards seen as a solution to problems that are not difficult and practical in conducting transactions at a bank. Not only is it easy to use when making payments, but using a credit card also doesn't require many requirements. However, with the increase in the use of credit cards, there are several emergencies of criminal acts that can cause losses for customers and banks. This study uses a dataset from the Kaggle website, which amounts to 56,962 original data from a bank in Europe. Data Mining has been reviewed as the best solution to solving this problem, so in this study, the Decision Tree C4.5 method will be used in detecting fraud in credit card transactions. Keywords: Credit Card. Fraud, Data Mining
Co-Authors Agung Mustika Rizki Ananda Devi Muri Utomo Gusti Eka Yuliastuti Maulana, Hendra Ulfa Nur Ulfa Mauludina