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All Journal Knowledge Engineering and Data Science Jurnal Penelitian Teknologi Informasi dan Sains JECCOM
Hendrick Hendrick
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Segmentation Method for Face Modelling in Thermal Images Albar Albar; Hendrick Hendrick; Rahmad Hidayat
Knowledge Engineering and Data Science Vol 3, No 2 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v3i22020p99-105

Abstract

Face detection is mostly applied in RGB images. The object detection usually applied the Deep Learning method for model creation. One method face spoofing is by using a thermal camera. The famous object detection methods are Yolo, Fast RCNN, Faster RCNN, SSD, and Mask RCNN. We proposed a segmentation Mask RCNN method to create a face model from thermal images. This model was able to locate the face area in images. The dataset was established using 1600 images. The images were created from direct capturing and collecting from the online dataset. The Mask RCNN was configured to train with 5 epochs and 131 iterations. The final model predicted and located the face correctly using the test image.
Design of Basic Vital Signs Measurement Tool And Dehydration Early Detection in Human Body Efrizon Efrizon; Gwo Jia Jong; Hendrick Hendrick; fadhlan; Yulastri Yulastri
JECCOM: International Journal of Electronics Engineering and Applied Science Vol. 1 No. 1 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30642/jeccom.1.1.1-8.2023

Abstract

A tool has been made to monitor vital signs such as heart rate, oxygen saturation in the blood, and body temperature based on a microcontroller which is based on dehydration conditions where the body loses more fluid than the amount of fluid it enters. Parameters for carrying out this detection include heart rate, blood oxygen saturation (SpO2), body temperature and urine color. The targets of this research are (a) making a prototype, (b) programming the system with the help of the Arduino IDE, and (c) measuring system performance. The research method starts from making a prototype and measuring system performance. The results of measuring the performance of the tool show that the error for measuring heart rate is 1.28%, measuring blood oxygen saturation (SpO2) is 0.51%, and measuring body temperature is 1.729%. However, for the dehydration detection test from 5 test samples, the results showed a success percentage of 60% with an average error of 40%. Overall the tool can function well
Mengukur Pengalaman Pengguna Aplikasi MochiMochi Menggunakan User Experiment Quisioner Novi Novi; Hendrick Hendrick
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 4 No. 1 (2026): : JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS (JPTIS)
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v4i1.3856

Abstract

Automatic facial expression recognition is a significant challenge in human-computer interaction with broad relevance in mental health, security, and behavioral analysis. This study proposes the implementation of Deep Learning using a custom Convolutional Neural Network (CNN) architecture to classify seven basic emotion categories: angry, disgust, fear, happy, sad, surprise, and neutral. Key challenges such as lighting variations and visual feature ambiguity in the FER2013 dataset are addressed through image pre-processing techniques, data augmentation, and the use of Batch Normalization and Dropout layers to prevent overfitting. The research methodology involves a systematic architectural design with three main convolution blocks optimized for computational efficiency. Experimental results show that the proposed model achieved a validation accuracy of 68.2%. Performance analysis based on F1-Score reveals that the "Happy" emotion has the highest detection rate (0.85) due to contrasting facial geometric features, while the "Fear" emotion is the most difficult class to identify (0.41). This study concludes that the use of an optimized standalone CNN architecture provides competitive and efficient performance compared to heavier transfer learning models, making it feasible for implementation on devices with mid-range hardware specifications.
Co-Authors Albar Albar fadhlan Gwo Jia Jong Novi Novi Rahmat Hidayat Yulastri Yulastri