Rabin-Karp Research Articles

Application of the KARP RABIN Algorithm for Plagiarism Detection System in Thesis Proposal Submission in the Department of Informatics Engineering STMIK Kaputama

Plagiarism is a serious threat, especially to academic honesty, so a detection system that can analyze various types of documents is needed. This research develops a plagiarism detection system using Optical Character Recognition (OCR) to convert image text into digital text. Rabin – Karp algorithm with rolling hash and Dice Coefficient Similarity is applied to measure similarities between documents. Testing is carried out on .doc, .txt, .jpg files. As a result, the system can detect plagiarism well in clear text and image documents, but accuracy can decrease in low-quality images. In conclusion, the similarity of content, sentence structure, and format affects the degree of similarity, while OCR techniques work effectively even though they are limited to low-quality images.

Analysis and Comparison of Full-Text Search Algorithms

The exponential growth of electronically stored textual data poses a significant challenge for search engine developers. This paper is dedicated to a detailed study and comparison of three classical full-text search algorithms: Knuth-Morris-Pratt (KMP), Boyer-Moore (BM), and Rabin-Karp (RK). These algorithms are widely used in computer science for efficient substring searching in textual data. The research results allowed us to identify the strengths and weaknesses of each algorithm and to determine the conditions under which each algorithm is most efficient.

S-box design based on logistic skewed chaotic map and modified Rabin-Karp algorithm: applications to multimedia security

A substitution box (S-box) serves as the nonlinearity component in a symmetric key encryption scheme; it directly determines the performance and security level of ciphers. Thus, the construction of an S-box with superior performance and efficiency, specifically in terms of high cryptographic properties, is critical. This research proposes a novel method to construct the S-box using the skewed logistic chaotic map and modified Rabin-Karp rolling hash function without disturbing its mathematical structure. First, iterate the skewed logistic map by selecting a seed value and specifying the parameters to generate the chaotic values. The map’s sensitivity to initial conditions and parameters results in a seemingly random and unpredictable chaotic values. Second, hash the chaotic values using Rabin-Karp for generating sequences of numbers within a specific range (0–255 for an 8-bit S-box). Then an S-box is constructed using the hash values. Performance evaluations indicate that the S-box produced through our suggested approach exhibits superior performance, demonstrating robust resistance against various security threats, including but not limited to linear attacks, differential attacks, and others. To demonstrate the effectiveness of the constructed S-box, this paper goes on to employ it in an image encryption application. Security analyses reveal that the developed image encryption algorithm successfully encrypts diverse types of images, producing cipher images characterized by uniformly distributed histograms. Performance assessments illustrate its high-security level, surpassing several state-of-the-art encryption algorithms.

Multi-Pattern GPU Accelerated Collision-Less Rabin-Karp for NIDS

In the domain of network communication, network intrusion detection systems (NIDS) play a crucial role in maintaining security by identifying potential threats. NIDS relies on packet inspection, often using rule-based databases to scan for malicious patterns. However, the expanding scale of internet connections hampers the rate of packet inspection. To address this, some systems employ GPU accelerated pattern matching algorithms. Yet, this approach is susceptible to denial of service (DOS) attacks, inducing hashing collisions and slowing inspection. This research introduces a GPU-optimized variation of the Rabin-Karp algorithm, achieving scalability on GPUs while resisting DOS attacks. Our open-source solution (https://github.com/AnasAbbas1/NIDS) combines six polynomial hashing functions, eliminating the need for false-positive validation. This leads to a substantial improvement in inspection speed and accuracy. The proposed system ensures minimal packet misclassification rates, solidifying its role as a robust tool for real-time network security.

Penerapan Metode Rabin-Karp untuk Mengukur Kemiripan Kata Dua Dokumen Berbasis Web

Scientific literature plays a significant role in the academic requirements of colleges, encompassing various types such as papers, reports, journals, and scripts. However, the issue of plagiarism, including the copying and plagiarizing of others' work, remains prevalent in the creation of scientific papers. In particular, digital content plagiarism often involves copy-pasting and quoting from original documents. To address this, measuring the similarity of words between documents becomes essential. In Dhamayanti's research, the recommendation is to enhance the Rabin-Karp algorithm by utilizing a distinct method [1]. This study builds upon previous research employing a string-matching method. Instead of the conventional cosine method, the substitution method employed string-Karp techniques within the Rabin-Karp algorithm, resulting in improved similarity percentages. The manufacturing of the application adopts the string-matching method using the Rabin-Karp algorithm. The algorithm matches 5-gram word sequences converted into hash values, and the similarity percentage is determined based on matching hash values. The presence of identical words indicates similarity. The application is tested using six scientific writing documents from diverse sources with related titles. Through 15 test runs, the accuracy level reached 90%.

Data preprocessing approach for machine learning-based sentiment classification

Public sentiment regarding a particular issue, product, activity, or organization can be measured and monitored with an application based on artificial intelligence. The data come from comments circulating on social media. However, the rules for writing comments on social media have yet to be standardized, so non-standard words often appear in these comments. Non-standard words affect the determination of sentiment into positive, negative, and neutral categories. Therefore, this study proposes a data preprocessing approach by inserting the Rabin-Karp algorithm to improve non-standard words. This research consists of several stages, namely crawling data, data preprocessing, feature extraction, model development (based on Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) methods), and analysis of the results. The experimental results showed that the proposed approach influences the determination of the sentiment category composition. Then, model testing results showed that all models obtain the highest value in the Positive category for the precision parameter with a value 1. All models in the Neutral category obtain the highest value for the recall parameter, almost reaching 1. All models in the Neutral category achieve the highest value of the f1-score parameter, with an average value of 0.95. In general, the results of the performance analysis of the classification model showed that the NB and SVM-based models have better performance than the DT method.

Fake Article Detection Application With Rabin Karp Algorithm

Currently, fake (fake) information/articles are increasingly widespread. Surveys show that people receive fakes more than once a day. The most used channel for spreading fakes is social media. The fake phenomenon in Indonesia raises doubts about the information received and confuses the public. Artificial intelligence is an intelligent machine (computer), where a machine is trained in advance (machine learning) to solve certain problems. This artificial intelligence is expected to work faster and more accurately. There are several methods that can be implemented, one of which is using the Rabin-Karp algorithm. Where the reason for choosing the Rabin karp algorithm is that there is a process that can filter existing writing so that it is suitable for the problems discussed, namely article writing and an algorithm that can work at high speed so that users do not need a long time to receive predictive results for articles.

Reversible Programming: A Case Study of Two String-Matching Algorithms

String matching is a fundamental problem in algorithm. This study examines the development and construction of two reversible string-matching algorithms: a naive string-matching algorithm and the Rabin-Karp algorithm. The algorithms are used to introduce reversible computing concepts, beginning from basic reversible programming techniques to more advanced considerations about the injectivization of the polynomial hash-update function employed by the Rabin-Karp algorithm. The results are two clean input-preserving reversible algorithms that require no additional space and have the same asymptotic time complexity as their classic irreversible originals. This study aims to contribute to the body of reversible algorithms and to the discipline of reversible programming.

Razy: A String Matching Algorithm for Automatic Analysis of Pathological Reports

Pathology reports are necessary for specialists to make an appropriate diagnosis of diseases in general and blood diseases in particular. Therefore, specialists check blood cells and other blood details. Thus, to diagnose a disease, specialists must analyze the factors of the patient’s blood and medical history. Generally, doctors have tended to use intelligent agents to help them with CBC analysis. However, these agents need analytical tools to extract the parameters (CBC parameters) employed in the prediction of the development of life-threatening bacteremia and offer prognostic data. Therefore, this paper proposes an enhancement to the Rabin–Karp algorithm and then mixes it with the fuzzy ratio to make this algorithm suitable for working with CBC test data. The selection of these algorithms was performed after evaluating the utility of various string matching algorithms in order to choose the best ones to establish an accurate text collection tool to be a baseline for building a general report on patient information. The proposed method includes several basic steps: Firstly, the CBC-driven parameters are extracted using an efficient method for retrieving data information from pdf files or images of the CBC tests. This will be performed by implementing 12 traditional string matching algorithms, then finding the most effective ways based on the implementation results, and, subsequently, introducing a hybrid approach to address the shortcomings or issues in those methods to discover a more effective and faster algorithm to perform the analysis of the pathological tests. The proposed algorithm (Razy) was implemented using the Rabin algorithm and the fuzzy ratio method. The results show that the proposed algorithm is fast and efficient, with an average accuracy of 99.94% when retrieving the results. Moreover, we can conclude that the string matching algorithm is a crucial tool in the report analysis process that directly affects the efficiency of the analytical system.

Similarity Identification Based on Word Trigrams Using Exact String Matching Algorithms

Several studies regarding excellent exact string matching algorithms can be used to identify similarity, including the Rabin-Karp, Winnowing, and Horspool Boyer-Moore algorithms. In determining similarities, the Rabin-Karp and Winnowing algorithms use fingerprints, while the Horspool Boyer-Moore algorithm uses a bad-character table. However, previous research focused on identifying similarities using these algorithms based on character n-gram. In contrast, identification based on the word n-gram to determine the similarity based on its linguistic meaning, especially for longer strings, had not been covered yet. Therefore, a word-level trigram was proposed to identify similarities based on the word trigrams using the three algorithms and compare each performance. Based on precision, recall, and running time comparison, the Rabin-Karp algorithm results were 100%, 100%, and 0.19 ms, respectively; the Winnowing algorithm results with the smallest window were 100%, 56%, and 0.18 ms, respectively; and the Horspool algorithm results were 100%, 100%, and 0.06 ms. From these results, it can be concluded that the performance of the Horspool Boyer-Moore algorithm is better in terms of precision, recall, and running time.

Text Pattern Searching Algorithm: Naive, KMP, Rabin Karp Comparative Study

The Pattern Searching algorithms are sometimes also referred to as String Searching Algorithms and are considered as a part of the String algorithms.These algorithms are useful in the case of searching a string within another string.String matching is the problem of finding all occurrences of a character pattern in a text.This paper provides an overview of different string-matching algorithms and comparative study of these algorithms.In this paper, we have evaluated several algorithms, such as Naive string-matching algorithm, Brute Force algorithm, Rabin-Karp algorithm, Boyer-Moore algorithm, Knuth-Morris-Pratt algorithm, Aho-Corasick Algorithm and Commentz Walter algorithm.

Implementasi Algoritma Rabin-Karp Untuk Pendeteksian Plagiarisme Pada File Dokumen Berupa Text Berbasis Web

Plagiarism is always in the spotlight, not least in the academic sector both from school to college level. Therefore, prevention and early detection need to be done to minimize this plagiarism action. There are several methods that can be implemented, one of which uses the Rabin-Karp algorithm. Rabin-Karp algorithm is one of the string matching, algorithms that can be used to measure the level of similarity of text. The thesis aims to design and build an application using the Rabin-Karp algorithm to find the percentage of similarities in two document files in the form of tested text. According to the results of tests conducted by (Priambodo, 2018) between the original document and the document tested from the test results of 10 text documents using the Rabin-Karp algorithm, resulting in the largest accuracy rate of 47.58%. While the smallest accuracy rate is 19.28%. While the results of the analysis conducted from 10 documents tested with a value of k-gram 1, have obtained the largest percentage of similarity, which is 57.14% and the smallest at 28.57%. Where if the similarity value <30% is included in mild plagiarism, 30%-70% moderate plagiarism and >70% large plagiarism.

Selection of the Best K-Gram Value on Modified Rabin-Karp Algorithm

The Rabin-Karp algorithm is used to detect similarity using hashing techniques, from related studies modifications have been made in the hashing process but in previous studies have not been conducted research for the best k value in the K-Gram process. At the stage of stemming the Nazief & Adriani algorithm is used to transform the words into basic words. The researcher uses several variations of K-Gram values to determine the best K-Gram values. The analysis was performed using Ukara Enhanced public data obtained from the Kaggle with a total of 12215 data. The student essay answers data totaled to 258 data in the group A and 305 in the group B, every student essay answers data in each group will be compared with the answers of other fellow group member. Research results are the value of k = 3 has the best performance which has the highest some interpretations of 1-14% (Little degree of similarity) and 15-50% (Medium level of similarity) compared to values of k = 5, 7, and 9 which have the highest number of interpretation results 0%-0.99% (Document is different). However, if the students essay answers compared have 100% (Exactly the same) interpretations, the k value on K-Gram does not affect the results.

The Development of A Tourism Information System Website in Kepahiang District Using The Rabin-Karp Algorithm

Kepahiang Regency is one of the regencies in Bengkulu province which has a natural tourist attraction destination. Some of these potentials include kabawetan tea plantations and Suro Ilir dam, Raflesia flower nature reserve, agro tirto, glagah estuary lake and black hill waterfall. The potential for natural tourism in Kepahiang district is very interesting because of the geographical location of Kepahiang district. With all the attractions on offer, it would be a pity if it was not accompanied by an attractive tourism promotion as well. One of the efforts to increase the number of tourists is to build applications that can make it easier for users to travel. Applications that can accommodate tourist needs data, especially regarding data on tourist attractions, lodging, culinary and historical places that can be visited. A tourism website that is equipped with a gallery so that it can increase the interest of tourists to come to visit. The application is built using the PHP and MySQL programming languages so that it can be accessed only using a browser application. Applications that are intended for users do not need to use a login system so that they can be accessed by anyone and anywhere. The implementation of rabin karp in the application has not been maximized because it has not been able to follow the formal concept of the algorithm, but the application can find the data sought with a similar concept because the search is carried out on every word contained in the tourist, hotel and culinary name data. The application can display complete tourist, culinary and hotel data along with the location on the map but cannot provide assistance on the closest route to visit the place you want to go on the map.

Parallel implementation of the primer search algorithm for loop-mediated isothermal amplification

In the paper, the implementation of an algorithm of search for primers in a DNA sequence with a size varying between one nucleotide and multiple million nucleotides was discussed. This analysis was done within the objective of finding a set of six specific primers that are used for a conduction of the loop-mediated isothermal amplification (LAMP). For the fastest search result possible, a parallel search for the primer with the use of the Rabin-Karp Algorithm which enables the search for a primer’s entry in DNA sequence in each thread was proposed. A new software for search for primers was developed using Python with BioPython library which implements the algorithm.

PENDETEKSIAN SOURCE CODE PLAGIARISM DENGAN ALGORITMA RABIN-KARP PADA ONLINE JUDGE

The difficulty of testing the truth of the source code and the detection of plagiarismsource code is the underlying problem of this research. The string matchingalgorithm is the main algorithm required in the detection of source code plagiarism.Currently, there are many existing strings matching algorithms, therefore it isnecessary to compare and select the appropriate string matching algorithm to theexisting requirement. As for the system, the researcher conducts a literature study and direct observation to find the appropriate source code testing system. The Rabin-Karp algorithm is chosen based on the average of a good time and space complexity. And also can be modified, so that the Rabin-Karp algorithm can be made in such a way that can meet all the needs that exist. As for the system testing source code researchers choose the online judge system because the system in the test is objective.

The Hybrid of Jaro-Winkler and Rabin-Karp Algorithm in Detecting Indonesian Text Similarity

The String-matching technique is part of the similarity technique. This technique can detect the similarity level of the text. The Rabin-Karp is an algorithm of string-matching type. The Rabin-Karp is capable of multiple patterns searching but does not match a single pattern. The Jaro-Winkler Distance algorithm can find strings within approximate string matching. This algorithm is very suitable and gives the best results on the matching of two short strings. This study aims to overcome the shortcomings of the Rabin-Karp algorithm in the single pattern search process by combining the Jaro-Winkler and Rabin-Karp algorithm methods. The merging process started from pre-processing and forming the K-Gram data. Then, it was followed by the calculation of the hash value for each K-Gram by the Rabin-Karp algorithm. The process of finding the same hash score and calculating the percentage level of data similarity used the Jaro-Winkler algorithm. The test was done by comparing words, sentences, and journal abstracts that have been rearranged. The average percentage of the test results for the similarity level of words in the combination algorithm has increased. In contrast, the results of the percentage test for the level of similarity of sentences and journal abstracts have decreased. The experimental results showed that the combination of the Jaro-Winkler algorithm on the Rabin-Karp algorithm can improve the similarity of text accuracy.

An arrangement of the number of K-Grams in the performance of rabin karp algorithm in text adjustment

Rabin Karp Algorithm is oftentimes used to determine the similarity between texts, using the hash function as a comparison among the string that is being identified and the substring in the text. The choice of the k value in k-gram is often done unrestrained. The number of k values that can be used when cutting some terms will take longer time if tried one by one. In this research, a word cutting test will be performed on a script using K-gram 0 to 8. The results will cover the effect of the value of each k used on the percentage of similarity produced. This research aims to determine the effect of the number of K-grams on the performance of Rabin Karp in text matching. The test underwent 20 sentences and 10 times using the Dice Coefficient as the text similarity testing. The conclusion of this research is that the K-gram 0 to 2 should not be used because of the K-gram basic principle that is character deduction. Accordingly, if the character is 0.1.2 then it does not yet have a meaning thus it gets a high percentage of similarity, based on trials that have been carried out with taking samples of K-gram 0 to 8 from 10 test data sets, researchers recommend that the K-gram 3 is the best among K-grams 0 to 8.

Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

Comparison of the BM25 and rabinkarp algorithm for plagiarism detection

Plagiarism occurs because of the easy distribution of data. Plagiarism detection of documents such as student assignments and final projects requires a long process, often overlooked. However, to avoid plagiarism, a document must be checked for the level of plagiarism. Plagiarism detection can be done online / offline with the plagiarism checker. However, checking documents with plagiarism checkers such as Turnitin, Dupli Checker, Copyleaks, PaperRater, Grammarly and others requires additional fees. Several studies have been conducted to detect plagiarism. BM25 and Rabin Karp are examples of the Plagiarism Checker method. BM25 is tfidf based, while Rabin Karp is Hashing based. Each method needs to know its performance to detect plagiarism. Based on these problems, a study on the comparison of plagiarism detection with the BM25 algorithm with Rabin-Karp will be conducted. The case study is to use the article in Indonesian. The application of the BM25 and Rabin Karp algorithms goes through the Pre-Processing stage which consists of case folding, cleaning, tokenizing, filtering, and stemming. In this study, Sastrawi stemmer was used in this study. The test was conducted on twenty articles in Indonesian. The test results that are seen are the performance in the form of execution time.