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All Journal Journal of Soft Computing Exploration Journal of Electronics Technology Exploration (JOeTEx)
Ananda, Mohammad Nabiel Dwi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Application of Passive Infrared Sensor to Improve the Quality of CCTV in Maintaining Home Security Ananda, Mohammad Nabiel Dwi; Shabaha, Achmad Rozin; Sundari, Putri Susi
Journal of Electronics Technology Exploration Vol. 2 No. 1: June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v2i1.366

Abstract

Artificial intelligence, or AI, is a simulation technology that runs through human intelligence demonstrated by machines or tools. Artificial intelligence can overcome and provide a sense of comfort, especially in the application of CCTV devices that use this passive infrared sensor method. This method can detect thieves or people moving in the area of the house where CCTV is installed, by detecting human objects using IR filters. If it detects an object that has the minimum temperature possessed by humans, it will immediately direct the alarm indicator. With the existence of CCTV that applies AI, it is hoped that human life will be safe, and crime will be reduced in an area, especially quiet areas with high crime rates.  The application of Passive Infrared Sensor (PIR Sensor) in this anti-theft CCTV tool can detect and be able to work with a high level of accuracy.
Lung cancer classification using convolutional neural network and DenseNet Damayanti, Nabila Putri; Ananda, Mohammad Nabiel Dwi; Nugraha, Faizal Widya
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.177

Abstract

Lung cancer is a condition that has a major impact on public health. Convolutional Neural Network (CNN) and DenseNet approaches are suggested in this study to aid lung cancer detection and classification. In various fields of pattern recognition and medical imaging, CNN and DenseNet have demonstrated their efficacy. In this study, radiology images from individuals with lung cancer were used to create a set of medical lung images. The findings show that lung cancer can be accurately classified into malignant and benign from radiological images using CNN and DenseNet architectures, with a parameter accuracy of 99.48%. This research contributes to the creation of a deep learning-based system for detecting and classifying lung cancer. The findings can be the basis for creating a more accurate and productive lung cancer diagnostic system.
Co-Authors Damayanti, Nabila Putri Nugraha, Faizal Widya Shabaha, Achmad Rozin Sundari, Putri Susi