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Nur Hidayat, M. Ali
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Pattern Recognition of Chinese Characters Using the Sokal Sneath Four Method Rasna, Rasna; Sah, Andrian; Nur Hidayat, M. Ali; Jusmawati, Jusmawati; Tonggiroh, Mursalim
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.668

Abstract

Pattern recognition is a discipline that aims to classify or describe objects based on their characteristics, quantitative measurements, or critical properties. Where a pattern is defined as an entity that is initially undefined, it can be identified and named through quantitative analysis. Pattern recognition can be applied to various fields, such as handwriting recognition, face recognition, eye recognition, skin, and image processing. One example of the application of pattern recognition is character recognition in letters used in learning. In this research, the digital image used as input comes from a two-dimensional image obtained through a digital camera. The digital image describes the light intensity in light and dark areas in the form of pixels and provides information about the object's color. To support the process of recognizing alphabet letters, which in this case are specifically Chinese alphabet letters, it will be assisted by using the Sokal Sneath Four Method. This significant mathematical approach helps create a compatible and accurate system for recognizing letter patterns through intensive data training. This method involves a series of steps, including data preprocessing, feature extraction, and classification, to train the system to recognize Chinese characters. The more training given to the system, the higher its accuracy in recognizing letter patterns, especially Chinese alphabet letters. The test results show that this Chinese alphabet letter pattern recognition system has a success rate of 65%, with a failure rate of 35%. Nevertheless, these results show room for further improvement in the algorithms used and the addition of training data to improve system performance and accuracy.
Heigh Detection System Using Russel and Rao Method Hakim, Jamaludin; Tonggiroh, Mursalim; Nurhayati, Siti; Nur Hidayat, M. Ali; Sah, Andrian
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.671

Abstract

Height detection is an exciting area of research with broad applications in fields such as construction, healthcare, and robotics, where measurements are still often done manually. This research aims to automate the height calculation process by developing a height detection system using image processing techniques, which offers improved accuracy and efficiency. The system that will be built works by capturing images of objects through a webcam and using the Russel Rao cluster analysis method to calculate height later. Borland Delphi 07 was chosen as the programming language because of its ability to handle image-processing tasks. This research draws on a thorough literature review of various books and articles, with the system operating in stages, starting with converting images to grayscale to simplify the data for more accessible analysis and then followed by applying Russel Rao's method for height measurement. However, the system is sensitive to environmental factors around the object. The system will perform best when there are no other objects near the target because when there are other objects nearby, it can cause the measurement line to shift and interfere with the results. The detection system requires a controlled environment with no foreign objects nearby for optimal performance. Despite these limitations, Russel Rao's analysis method achieved an accurate detection accuracy of approximately 65%, with three out of eight sample tests yielding correct measurements. While this shows room for improvement if more relevant research is to be done in the future, this system will build a strong foundation for further development in this field. Future enhancements could focus on refining the algorithm to increase detection accuracy, make the system more resilient in dynamic or cluttered environments, and expand its potential applications in various fields.
Co-Authors Hakim, Jamaludin Jusmawati Jusmawati, Jusmawati Rasna, Rasna Sah, Andrian Siti Nurhayati Tonggiroh, Mursalim