Muhammad Salman

Improving Security Level through Obfuscation Technique for Source Code Protection using AES Algorithm

PHP is a server-side scripting programming language that is widely used to develop website services. However, web-based PHP applications are distributed in source code so that the security is vulnerable and weak because the lines of source code can be easily copied, modified, or used in other applications. These research aims to implement obfuscation techniques design in PHP extension code using AES algorithm. The AES algorithm recommended by NIST (National Institute of Standards and Technology) to protect the US governments national information security system. Through obfuscation technique using encryption, it is expected that programmers have an option to protect the PHP source code so that the copyright or intellectual property of the program can be protected

Cross-Language Plagiarism Detection System Using Latent Semantic Analysis and Learning Vector Quantization

Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising). Due to the syntax disparity between Bahasa Indonesia and English, most of the existing methods for automated cross-language plagiarism detection do not provide satisfactory results. This paper analyses the probability of developing Latent Semantic Analysis (LSA) for a computerized cross-language plagiarism detector for two languages with different syntax. To improve performance, various alterations in LSA are suggested. By using a linear vector quantization (LVQ) classifier in the LSA and taking into account the Frobenius norm, output has reached up to 65.98% in accuracy. The results of the experiments showed that the best accuracy achieved is 87% with a document size of 6 words, and the document definition size must be kept below 10 words in order to maintain high accuracy. Additionally, based on experimental results, this paper suggests utilizing the frequency occurrence method as opposed to the binary method for the term–document matrix construction.

Analysis on the Effect of Term-Document's Matrix to the Accuracy of Latent-Semantic-Analysis-Based Cross-Language Plagiarism Detection

This paper presents the results of experimental investigation on the impact of term-document matrix variations to the accuracy of cross-language LSA-based plagiarism detection. The experiment was focusing in comparing Indonesian and English papers. The increase of document definition size as the source of matrix construction significantly caused negative impact to the detection accuracy in all scenarios. The results of the experiments showed that the document definition size must be kept below 10 in order to maintain high accuracy, and reached its worst performance at 25. Additionally, the implementation of term-document matrix using the frequency of words occurrence was found beneficial to the improvement of detection accuracy compared to the binary implementation using simply the existence/absence of words.