Traffic accidents often result in loss of life and significant economic losses. Indonesia's high number of traffic accidents indicates the need for effective solutions to overcome this problem. Developing a drowsiness detection device is one effort that can be made to reduce accidents caused by drowsy drivers. The data obtained in this study used driver heart rate data. The drowsiness detection tool was developed using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor. Testing was carried out on 25 subjects under two conditions: 'Drowsy' and 'Normal.' The driver's level of drowsiness is determined based on the heart rate measured by the detection device. The Blynk application is used as a visual interface to provide notifications via smartphone if the driver is drowsy. The accuracy of the drowsiness detection tool was compared with the results obtained from the Pulse Oximeter. This research shows that the drowsiness detection tool using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor has an accuracy of around 98% when compared with the pulse oximeter. The Blynk application successfully sends notifications precisely when the driver is drowsy. This study highlights the potential of drowsiness detection devices to improve traffic safety and reduce accidents caused by drowsy drivers.

This study discusses the face recognition of children with special needs, especially those with autism. Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that affects social skills, ways of interacting, and communication disorders. Facial recognition in autistic children is needed to help detect autism quickly to minimize the risk of further complications. There is extraordinarily little research on facial recognition of autistic children, and the resulting system is not fully accurate. This research proposes using the Convolution Neural Network (CNN) model using two architectures: ShuffleNet, which uses randomization channels, and Visual Geometry Group (VGG)-19, which has 19 layers for the classification process. The research object used in the face recognition system is secondary data obtained through the Kaggle site with a total of 2,940 image data consisting of images of autism and non-autism. The faces of autistic children are visually difficult to distinguish from those of normal children. Therefore, this system was built to recognize the faces of people with autism. The method used in this research is applying the CNN model to autism face recognition through images by comparing two architectures to see their best performance. Autism and non-autism data are grouped into training data, 2,540, and test data, as much as 300. In the training stage, the data was validated using validation data consisting of 50 autism image data and 50 non-autism image data. The experimental results show that the VGG-19 has high accuracy at 98%, while ShuffleNet is 88%.

Autism spectrum disorder is a serious mental disorder affecting social behavior. Some children also faceintellectual delay. In people with autism spectrum disorder, the signals detected have abnormalities compared to normalpeople. This can be a reference in diagnosing the disorder with electroencephalography. This study will analyze the effect ofpower spectral density on the electroencephalography of autistic children and also compare it with the power spectral densityvalue on the electroencephalography of normal children using the Welch periodogram method approach. In the preprocessingstage, the independent component analysis method will be applied to remove artifacts, and a finite impulse response filter toreduce noise in the electroencephalography signal. The study results indicate differences in the power spectral density valuesobtained in the autistic and normal electroencephalography signals. The power spectral density value obtained in the autisticelectroencephalography signal is higher than the normal electroencephalography signal in all frequency sub-bands. From thestudy results, the highest power spectral density value obtained by the autistic electroencephalography signal is in the deltasub-band, which is 54.06 dB/Hz, while the normal electroencephalography signal is only 33.14 dB/Hz at the same frequencysub-band. And in the Alpha and Beta sub-bands, the normal electroencephalography signal increases the power spectraldensity value, while in the autistic electroencephalography signal, the power spectral density value decreases in the Alphaand Beta sub-bands. In addition, finite impulse response and independent component analysis methods can also reduce noiseand artifacts contained in autistic and normal electroencephalography signals