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All Journal Bulletin of Computer Science Research Journal of Computing Theories and Applications
Octara Pribadi
STMIK TIME, Medan
Computer Science & IT Decision Sciences, Operations Research & Management

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Aplikasi Pembeda Daging Sapi dan Babi dengan Metode Color Moment dan Local Binary Pattern Histogram Edi; Octara Pribadi
Bulletin of Computer Science Research Vol. 3 No. 5 (2023): Agustus 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v3i5.260

Abstract

Meat is one of the main food ingredients consumed by humans because it contains a lot of high protein, so it can increase intelligence and increase the stamina that humans need to carry out activities of daily life. Due to the very high level of meat consumption, these meats are often found in the market. The price of pork is cheaper than beef. The price difference between the two meats has led to the emergence of fraudulent practices in the beef trade. To solve this problem, an application to distinguish beef and pork can be designed to help socialize to the public about how to distinguish beef and pork. In this study, color characteristics will be used to distinguish pork and beef, because from previous studies the color characteristics have a higher accuracy. The method that can be used to extract features from meat images is the color moment method. Color feature extraction consists of Mean, Standard Deviation, and Skewness features. Meanwhile, to carry out the process of detecting the type of meat, the Local Binary Pattern Histogram (LBPH) method will be used. LBPH is a technique of the Local Binary Pattern (LBP) method to change the performance of object recognition results. The result of this research is an application that can be used to distinguish beef and pork. The process of recognizing the type of meat using the Color Moment and LBPH methods has a high success rate so that the Color Moment and LBPH methods can be applied to detect the type of meat, with a success rate of 99.33%.
Image Encryption using Half-Inverted Cascading Chaos Cipheration De Rosal Ignatius Moses Setiadi; Robet Robet; Octara Pribadi; Suyud Widiono; Md Kamruzzaman Sarker
Journal of Computing Theories and Applications Vol 1, No 2 (2023): (in Process)
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9388

Abstract

This research introduces an image encryption scheme combining several permutations and substitution-based chaotic techniques, such as Arnold Chaotic Map, 2D-SLMM, 2D-LICM, and 1D-MLM. The proposed method is called Half-Inverted Cascading Chaos Cipheration (HIC3), designed to increase digital image security and confidentiality. The main problem solved is the image's degree of confusion and diffusion. Extensive testing included chi-square analysis, information entropy, NCPCR, UACI, adjacent pixel correlation, key sensitivity and space analysis, NIST randomness testing, robustness testing, and visual analysis. The results show that HIC3 effectively protects digital images from various attacks and maintains their integrity. Thus, this method successfully achieves its goal of increasing security in digital image encryption
Co-Authors De Rosal Ignatius Moses Setiadi Edi Md Kamruzzaman Sarker Robet Robet Suyud Widiono