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All Journal International Journal of Electrical and Computer Engineering JIPTEK : Jurnal Ilmiah Pendidikan Teknik dan Kejuruan Jurnal Sains dan Teknologi IJIE (Indonesian Journal of Informatics Education) Jurnal ULTIMATICS JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Media Informasi Teknologi Media of Computer Science
Prakisya, Nurcahya Pradana Taufik
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering

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Rekayasa Perangkat Lunak Aplikasi Presensi Mobile Menggunakan Metode Deep Learning Setiawan, Ragil; Prakisya, Nurcahya Pradana Taufik; Ariyuana, Rosihan
JIPTEK: Jurnal Ilmiah Pendidikan Teknik dan Kejuruan Vol 17, No 1 (2024): January
Publisher : Faculty of Teacher Training and Education Universitas Sebelas Maret Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jiptek.v17i1.76556

Abstract

Facial recognition research has its challenges due to faces complexity, ranging from facial expressions and certain conditions that make facial recognition an exciting research experiment. Moreover, many-oriented applications of machine learning have moved to devices edge, and-based facial recognition is no exception mobile. Seeing the ongoing development of facial pattern recognition algorithms such as Viola Jones, Backpropagation, this research uses the MobileFaceNet  mobile CNN model which is currently popular to be implemented in the mobile-based facial recognition presence application at the Information and Computer Engineering Education (PTIK) FKIP UNS. The deep learning method is a method for understanding and classifying objects. In the developed application, a face is captured in an image. This research uses the help of the flutter framework and the Tensorflow Lite library to develop a presence application mobile facial recognition in real-time. This paper aims to determine the value of the memorization and generalization algorithms model of CNN MobileFaceNet  on the application.  A trial of the system has been carried out by involving 30 volunteers in the testing from 2016-2019 PTIK students by random sampling. Each test was carried out for 10 iterations. From the test results, the system memorization value is 84.5%. On the other hand, the generalization results get 70% in recognizing identical but not similar images correctly. In terms of memorization and generalization, these results are better than similar studies using backpropagation
A Comparative Study of Digital Image Segmentation Algorithms for Acute Myeloid Leukemia M1 White Blood Cells Images Prakisya, Nurcahya Pradana Taufik; Setiawan, Andika
IJIE (Indonesian Journal of Informatics Education) Vol 4, No 2 (2020): IJIE (Indonesian Journal of Informatics Education)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v4i2.48448

Abstract

Various types of algorithms have been widely used for image segmentation in digital image processing. Every algorithm has features that make it unique to be applied to specific cases. One of the applications of image segmentation is to detect white blood cells. Certain objects such as blood cells must be able to be well segmented because their existence is very crucial to support the accuracy of disease detection related to haematology or the branch of medical science that studies the morphology of blood and blood-forming tissues. Three image segmentation algorithms were compared through this study: Seed Region Growing, Otsu Thresholding and Active Contour Without Edge. Comparative analysis of the three algorithms was done by counting the number of white blood cell objects that were successfully segmented with the actual number of cells that were counted manually. A total of 30 images of blood smears were taken from people suffering from acute myeloid leukemia M1. The average accuracy values from each algorithm were used to determine which image segmentation algorithm is the most suitable for application in the case of white blood cells segmentation. The results showed that Active Contour Without Edge is the most appropriate among the other algorithms
Hand Detection on HSV Color Space Model and Syntactic Extraction of Fingertip by Thinning Method for Hand Gesture Recognition Aristyagama, Yusfia Hafid; Liantoni, Febri; Prakisya, Nurcahya Pradana Taufik
IJIE (Indonesian Journal of Informatics Education) Vol 5, No 2 (2021): IJIE (Indonesian Journal of Informatics Education)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v5i2.51693

Abstract

In the discussion of computer vision, detection and recognition are an interesting topic to discuss. Basically, advanced computer vision technology requires a high-level interaction method above the text-based console interaction. Hand detection and gesture recognition is one of the interaction cases in computer vision. In this study, an experiment of hand detection and syntactic hand gesture recognition method are discussed. HSV (Hue Saturation Value) space color model is used as the basis of hand detection and segmentation. Then, the thinning method is used to get endpoint features of each fingertip.The proposed design is designed to meet with real-time video processing. The experiment intended to find some issues usually happened when the ZS thinning method is used to gain the detection and recognition. The result shows that the proposed design able to detect and recognize some gesture, but unstable hand movement may lead into a fault called by extra endpoint. In this research, extra endpoints are considered as a challenge that must be anticipated when using thinning method especially ZS algorithm to perform syntactic hand gesture recognition.
Co-Authors Adhiatma, Paulus Lestyo Andika Setiawan, Andika Aris Budianto, Aris Cucuk Wawan Budiyanto Fadliansah, Arafik Nur Fawaidil Wafa, Alfian Febri Liantoni, Febri Herianto, Tulus Joseph Jimsan Jimsan Kusmanto, Bintang Timur Lestari Rahayu Puspanda Hatta Ramadhan, Firdaus Ditio Ramadhan, Raqael Fisabillah Rosihan Ari Yuana, Rosihan Ari Rosihan Ariyuana, Rosihan Rustanto, Diki Wahyudi Salsabila, Zahra Khalila Setiawan, Ragil Sidauruk, Deardo Satria Ristiawan Yusfia Hafid Aristyagama Zaqli, Abi Khoir Naufal