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All Journal Jurnal Mantik International Journal of Data Science, Engineering, and Analytics (IJDASEA) International Journal Of Computer, Network Security and Information System (IJCONSIST)
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.
Classification of color features in butterflies using the Support Vector Machine (SVM) Dhian Satria Yudha Kartika; Hendra Maulana
IJCONSIST JOURNALS Vol 2 No 02 (2021): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (381.57 KB) | DOI: 10.33005/ijconsist.v2i02.50

Abstract

Research in digital images is expanding widely and includes several sectors. One sector currently being carried out research is in insects; specifically, butterflies are used as a dataset. A total of 890 types of butterflies divided into ten classes were used as a dataset and classified based on color. Ten types of butterflies include Danaus plexippus, Heliconius charitonius, Heliconius erato, Junonia coenia, Lycaena phlaeas, Nymphalis antiopa, Papilio cresphontes, Pieris rapae, Vanessa atalanta, Vanessa cardui. The process of extracting color features on butterfly wings uses the RGB method to become HSV color space with color quantization (CQ). The purpose of adding CQ is that the computation process is carried out faster without reducing the image's information. In the color feature extraction process, the image is converted into 3-pixel sizes and normalized. The process of normalizing the dataset has the aim that the value ranges in the dataset have the same value. The 890 butterfly dataset was classified using the Support Vector Machine (SVM) method. Based on this research process, the accuracy of the 256x160 pixel size is 72%, the 420x315 pixel is 75%, and the 768x576 pixel is 75%. The test results on a system with a 768x576 pixel get the highest results with a precision value of 74.6%, a recall of 72%, and an f-measure of 73.2% Keywords—image processing; classification; butterflies; color features; features extraction
Digital Image Segmentation Resulting from X-Rays of Covid Patients using K-Means and Extraction Features Method Dhian Satria Yudha Kartika; Anita Wulansari; Hendra Maulana; Eristya Maya Safitri; Faisal Muttaqin
IJCONSIST JOURNALS Vol 3 No 1 (2021): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (970.953 KB) | DOI: 10.33005/ijconsist.v3i1.55

Abstract

The COVID-19 pandemic has significant impact on people's lives such as economic, social, psychological and health conditions. The health sector, which is spearheading the handling of the outbreak, has conducted a lot of research and trials related to COVID-19. Coughing is a common symptoms among humans affected by COVID-19 in earlier stage. The first step when a patient shows symptoms of COVID-19 was to conduct a chest x-ray imaging. The chest x-rayss can be used as a digital image dataset for analysing the spread of the virus that enters the lungs or respiratory tract. In this study, 864 x-rays were used as datasets. The images were still raw, taken directly from Covid-19 patients, so there were still a lot of noise. The process to remove unnecessary images would be carried out in the pre-processing stage. The images used as datasets were not mixed with the background which can reduce the value at the next stage. All datasets were made to have a uniform size and pixels to obtain a standard quality and size in order to support the next stage, namely segmentation. The segmentation stage of the x-ray datasets of Covid-19 patients was carried out using the k-means method and feature extraction. The Confusion Matrix method used as testing process. The accuracy value was 78.5%. The results of this testing process were 78.5% of precision value, 78% of recall and 79% for f-measure
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 Anita Wulansari Eristya Maya Safitri Faisal Muttaqin Gusti Eka Yuliastuti Hendra Maulana Hendra Maulana Hendra Maulana Ulfa Nur Ulfa Mauludina