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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
Yogi Anggun Saloko Yudo
Universitas Telkom
Computer Science & IT Education

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IMPROVED FACE DETECTION ACCURACY USING HAAR CASCADE CLASSIFIER METHOD AND ESP32-CAM FOR IOT-BASED HOME DOOR SECURITY Nauval Muhammad; Endro Ariyanto; Yogi Anggun Saloko Yudo
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 1 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i1.3365

Abstract

Some people are very easy to open the door lock with just a small wire. This causes the house to be vulnerable to burglary and theft. In previous studies, there were still shortcomings such as the accuracy of facial recognition was not good, the time for the facial recognition process was very long and no action was taken if the camera caught an unknown person. This raises the need for solutions related to security systems that can monitor homes when something suspicious happens so that it can be prevented immediately. This study aims to create a home door security system using ESP32-CAM as face recognition. This face recognition can unlock the door automatically and if someone is caught on camera who is not known, the system will send a notification to the owner to follow up on this. The results of the face detection test using the Haar Cascade Classifier method that can distinguish a known face and an unknown face. The results of facial accuracy at a distance of 30 cm, 40 cm, and 50 cm with a light intensity of 130 lux obtained an average accuracy of 96.6%. The result of the average time required from the face sampling process until the face is recognized by the system is 21,50 seconds.
IMPROVED REAL-TIME HOUSE FIRE DETECTION SYSTEM PERFORMANCE WITH IMAGE CLASSIFICATION USING MOBILENETV2 MODEL Muhammad Faris; Endro Ariyanto; Yogi Anggun Saloko Yudo
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 2 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i2.3803

Abstract

The problem with the Ardunio microcontroller-based fire detection system with fire and smoke sensors is the detection distance. For example, in another research, it was stated that the maximum distance for fire detection on two pieces of paper that were burned was 140 cm. This means that if the fire point is at a farther distance, the system cannot detect a fire early, of course, this will be problematic if used in a wider room. Based on these problems, a system is needed that can detect fires in large rooms. A method that can be used is detection using image classification. MobileNetV2 is a real-time model for classifying or detecting an object in an image. In this study, the model was built using real-time based on the TensorFlow and Keras libraries. The system will use a laptop with an Nvidia GeForce MX130 GPU, a 48MP resolution smartphone camera, and the OpenCV library for the image classification process, as well as Telegram for sending fire notifications via the Re-quests library. The test results obtained on burnt 80/90 motorcycle tires, the most optimal detection distance is 7 meters with an accuracy of 99.91%. While testing on two sheets of paper that are burned, the most optimal detection distance is 3 meters with an accuracy of 99.75%. The average response time obtained varies greatly from 74.5 ms to 117.1 ms, which depends on the internet network connection.
Co-Authors Endro Ariyanto Muhammad Faris Nauval Muhammad