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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Abdi Ilmu International Journal of Advances in Data and Information Systems Indonesian Journal of Education, Social Sciences and Research (IJESSR) Jurnal Ilmiah MADIYA: Masyarakat Mandiri Berkarya International Journal of Research in Vocational Studies (IJRVOCAS) Jurnal Sains dan Teknologi Majalah Iptek Politeknik Negeri Medan Polimedia Internasional Journal of Data Science, Computer Science and Informatics Technology (InJODACSIT) Journal Global Technology Computer Jurnal Pengabdian Barelang Komposit : Jurnal Pengabdian Dan Pemberdayaan Masyarakat Journal of Computer Science Advancements
Husna, Meryatul
Politeknik Negeri Medan
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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
Utilization of Color Space Value Filter for Laser Pointer Spot Detection with a Single Board Computer julham -; Hikmah Adwin Adam; Meryatul Husna
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6119

Abstract

A color space is an abstract mathematical model that describes how a color can be represented as a row of numbers. One abstract mathematical model of the color space that represents the brightness of a color is the value color space or known as the value filter. For this reason, a gray scale is used with a value range of 0 to 255. This is tested in detecting a red laser pointer light spot that is directed into a field. The results obtained show that more inconsistent detection results are displayed than consistent detection results (can be seen in table 1 and table 2). This can happen because the area of the image captured by the camcorder has several pixels with a maximum gray level value or a gray level value that exceeds the specified limit value (maxBri variable).
OpenCV Using on a Single Board Computer for Incorrect Facemask-Wearing Detection and Capturing julham -; Meryatul Husna; Arif Ridho Lubis
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6118

Abstract

OpenCV (Open Source Computer Vision Library) is a software library intended for real-time dynamic image processing, created by Intel. In this study, the library will be used to detect the face, nose and mouth. Furthermore, the system is equipped with the knowledge that if the mouth and nose or one of them is detected, then the face has not used the mask correctly and the system records the face. The system is supported by an image capture device in the form of a camcorder, a processing device in the form of a single board computer, such as a Raspberry Pi and a display device in the form of a monitor. And the result is that the system is able to make a decision whether the face is wearing a mask correctly or not. By means of labeling around the face in the form of red angular lines, if not properly use the mask. The success rate is 88.4% using detector parameters, namely: scale factor = 1.1 for all face, nose and mouth object libraries.
Multi-Detection System Using Faster R-CNN for Fish Species Classification and Quality Assessment on Android Faza, Sharfina; Lubis, Arif Ridho; Meryatul Husna; Rina Anugrahwaty; Muhammad Rafif Rasyidi; Romi Fadillah Rahmat
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16374

Abstract

Species identification and quality assessment of fish in trade still rely on manual visual observation, which is subjective and requires specialized expertise. This method's limitations make it difficult for consumers to distinguish species with similar morphology and accurately assess fish quality, which can lead to inappropriate purchasing decisions. This research develops a multi-detection system based on Faster R-CNN with VGG16 backbone for fish species classification and quality assessment simultaneously on Android platform. The system uses convolutional layers to extract visual features from input images, Region Proposal Network for fish object detection and localization, and fully connected layers for simultaneous classification of species and quality levels. The research dataset consists of 3,000 images of five fish species (gourami, tilapia, nile tilapia, snapper, and pomfret) with four quality levels, divided into 2,400 training images and 600 testing images. The trained model is converted to TensorFlow Lite format for implementation on Android devices. Test results show the multi-detection system achieves 92% accuracy in fish species classification and quality assessment, demonstrating the effectiveness of the Faster R-CNN approach for multi-detection applications in the Android-based fisheries sector.
Co-Authors Adam, Hikmah Adwin Ajulio Padly Sembiring Ajulio Padly Sembiring Andam Lukcyhasnita Andi Supriadi Chan, Andi Supriadi Aprilza Aswani Dani Gunawan Demora Ari Destiadi, Rezha Fawwaz, Insidini Faza, Sharfina Hasibuan, Annalisa Sonaria Hikmah Adwin Adam Janferson Panggabean Julham Julham Julham Julham Kinarta Ketaren Lubis, Arif Ridho Lukcyhasnita, Andam Marliana Sari Muhammad Rafif Rasyidi Pandu Pratama Putra, Pandu Pratama Poppy Andriyani Prayudani, Santi Purba, Lampson Pindahaman Putri, Maharani Rina Anugrahwaty Rina Anugrahwaty, Rina Rinaldy, Muhammad Eri Rizka, Ade Rizki Syahputra Romi Fadillah Rahmat Sembiring, Ajulio Padly Silmi Silmi Silmi Simarmata, Anggi C Simarmata Sinambela, Lamtiur Siregar, Mhd. Ikhsan P. Tumanggor, Orli Binta Winda Syafitri Wiwin Sry Adinda Banjarnahor Yusnida, Yuyun Yusuf, Kadri Yuyun Yusnida Lase