Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Applied Sciences and Smart Technologies
Erna Alimudin
Polytechnic State of Cilacap
Computer Science & IT Energy Engineering Industrial & Manufacturing Engineering

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Employee Presence Using Body Temperature Detection And Face Recognition Arif Ainur Rafiq; Erna Alimudin; Della Puspa Rani
International Journal of Applied Sciences and Smart Technologies Volume 04, Issue 02, December 2022
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v4i2.5066

Abstract

Employee performance can be measured by their presence or attendance, which applies to both civil servants and non-civil servants. Because the attendance system still uses the manual technique, it is considered inefficient due to the potential for data fraud and attendance problems. In addition, the government is adopting precautions against viruses in office buildings to maintain business continuity while the pandemic is being addressed. This study aimed to employ a facial recognition system and temperature measurement to lower the danger of COVID 19 transmission while minimizing paper use by using a facial recognition system as a substitute for presence. It has so far permitted the digitization of formerly manual sights. The OpenCV library allows computers to detect faces using the haar cascade classifier approach and Python as a programming language. A Logitech C930e webcam with a resolution of 1080p at 30fps was used to capture facial data, which was then processed on a Raspberry Pi 4 microprocessor. It uses an MLX90614 sensor to monitor body temperature, which is controlled by an Arduino Uno microcontroller. It is well integrated into the database based on body temperature testing and facial recognition. The development of a more accurate temperature sensor reading method for distance and employee body temperature is a priority for future research.
Similarity measurement on digital mammogram classification Erna Alimudin; Hanung Adi Nugroho; Teguh Bharata Adji
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 4: August 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i4.10698

Abstract

Breast cancer is one of the dominant causes of death in the world. Mammography is the standard for early detection of breast cancer. In examining mammograms, the overall parenchyma pattern of the left and right breast was placed side by side for symmetry assessed of left and right breast tissue by radiologist. Thus, in building computer-aided diagnosis (CAD) system for screening mammography, it is necessary to adapt the working procedure of the radiologist. In this study, 30 training images and 30 testing images from Kotabaru Oncology Clinic in Yogyakarta were used. The first step was to enhance the image quality with median filter and contrast limited adaptive histogram equalization (CLAHE). Then, feature extraction was processed by histogram-based and by gray level co-occurrence matrix (GLCM) based. Furthermore, the similarity measurement process was used to measure the difference value between selected features, i.e. angular second moment (ASM), inverse difference moment (IDM), contrast, entropy based GLCM, and energy, on the left and right mammograms. This process was intended to assess the symmetry of left and right mammograms as radiologists do in mammography screening. The obtained results of the classification between normal and abnormal images with backpropagation algorithm were accuracy of 0.933, sensitivity of 0.833, and specificity of 1.000.
Co-Authors Della Puspa Rani Hanung Adi Nugroho Rafiq, Arif Ainur Teguh Bharata Adji