Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal International Journal Of Computer, Network Security and Information System (IJCONSIST)
Hendra Maulana
UPN "Veteran" Jawa Timur
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Traffic Sign Detection Using Region And Corner Feature Extraction Method Hendra Maulana; Dhian Satria Yudha Kartika; Agung Mustika Riski; Afina Lina Nurlaili
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 (3139.835 KB) | DOI: 10.33005/ijconsist.v3i1.54

Abstract

Traffic signs are an important feature in providing safety information for drivers about road conditions. Recognition of traffic signs can reduce the burden on drivers remembering signs and improve safety. One solution that can reduce these violations is by building a system that can recognize traffic signs as reminders to motorists. The process applied to traffic sign detection is image processing. Image processing is an image processing and analysis process that involves a lot of visual perception. Traffic signs can be detected and recognized visually by using a camera as a medium for retrieving information from a traffic sign. The layout of different traffic signs can affect the identification process. Several studies related to the detection and recognition of traffic signs have been carried out before, one of the problems that arises is the difficulty in knowing the kinds of traffic signs. This study proposes a combination of region and corner point feature extraction methods. Based on the test results obtained an accuracy value of 76.2%, a precision of 67.3 and a recall value of 78.6.
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
Co-Authors Afina Lina Nurlaili Agung Mustika Riski Anita Wulansari Dhian Satria Yudha Kartika Dhian Satria Yudha Kartika Eristya Maya Safitri Faisal Muttaqin