Comparison of Knuth Morris Pratt and Boyer Moore algorithms for a web-based dictionary of computer terms

Khumaidi, Ali; Ronisah, Yusuf Aras; Putro, Harjono Padmono

doi:10.26555/jifo.v14i1.a17038

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…Then, researchers who compared the Knuth Morris Pratt method with Bayer Moore using the Exponential Comparison Method (MPE) by [18] found that the Knuth Morris-Pratt algorithm is faster than the Boyer-Moore algorithm for the word search process [18]. Research comparing the two methods was also conducted by [19] which obtained different results, where from the total ECM score that the BM algorithm is 0.55% superior to KMP as evidenced by the BM search time is 37.9%, and the KMP algorithm is 62.1%, then the search memory usage for the KMP algorithm is 50.6%, and the BM algorithm is 49.4% [19]. Furthermore, still related to the comparison of the two models by implementing into Java and Java Microbenchmark Harness to evaluate the execution time of the method using a number of experimental test scenarios and the results obtained that the BM algorithm outperforms the KMP algorithm in all test scenarios [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Chatbot PTIPD Customer Care Center Service using Dialogfow

Ndruru,

Fikry,

2023

JIIKM

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Chatbot research is a unique innovation in the development of Artificial Intelli- gence and has promising prospects in the field of Education. One form of information service available at the university is the Customer Care Center (C3) PTIPD UIN Suska Riau, which is responsible for handling problems submitted by students. However, with so many questions or problems submitted to the PTIPD Customer Care Center, it is difficult for the PTIPD Cus- tomer Care Center to respond to student questions submitted, the service becomes ineffective and the response to the answers to the problems submitted becomes late. To overcome this problem, chatbot development was carried out for PTIPD UIN Suska Riau Customer Care Center Services using Dialogflow to improve services and overcome existing problems. Di- alogflow as conversation development platform that uses natural language processing (NLP) to understand and interpret user intent in conversations. Through User Acceptance Test (UAT) testing, the chatbot managed to achieve an acceptance rate of 84% overall. This shows that users, in this case, students respond positively to the use of chatbots in PTIPD Customer Care Center services. In addition, Usability Testing was also conducted to evaluate the level of usability of the chatbot. Based on this test, the chatbot achieved a score of 76, which indicates a good level of usability in interaction with users. The test results illustrate that the chatbot at the Customer Care Center PTIPD UIN Suska Riau has provided a positive user experience.

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Section: Literature Reviewmentioning

confidence: 99%

Chatbot PTIPD Customer Care Center Service using Dialogfow

Ndruru,

Fikry,

2023

JIIKM

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“…Multi-pattern matching algorithms include AC (Aho-Corasick automaton) algorithm [11][12], and improved AC-BM (Aho-Corasick Boyer-Moore) algorithm based on AC algorithm [13][14].…”

Section: Introductionmentioning

confidence: 99%

“…However, the dictionary tree construction of the AC algorithm wastes a lot of memory and requires multiple backtracking to transfer to the effective successor state when there is a mismatch, which affects the performance of the algorithm. Literature [13] and [14] is based on the AC algorithm, combined with the idea of BM algorithm, and adopts bad characters and good suffix rules to realize the jump matching of the pattern tree. The major disadvantage of this algorithm is that the good prefix rule preprocessing is complex and difficult to achieve.…”

Section: Introductionmentioning

confidence: 99%

Simulation Research on Fast Matching of Big Data Based on Spark

Song

Leng

et al. 2023

IEEE Access

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To solve the problem of low efficiency in real-time processing and matching of CNAME records in massive DNS log data, a parallel AC automaton enhancement method based on Spark was proposed. The method is based on the Spark distributed cluster computing engine of Hadoop, which ensures the stability of massive DNS log data storage with high fault tolerance and 24-hour real-time processing. At the same time, the Spark distributed cluster uses the multi-thread parallel computing method combined with the improved AC automaton algorithm, which not only reduces the memory occupied by trie construction, but also improves the efficiency of rapid matching of CNAME records of massive DNS logs. Simulation results show that the proposed method can quickly match CNAME records of massive DNS log data. Compared with the original AC algorithm, the efficiency is significantly improved, and the time complexity and storage space are reduced.

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Comparison of Knuth Morris Pratt and Boyer Moore algorithms for a web-based dictionary of computer terms

Cited by 2 publications

References 8 publications

Chatbot PTIPD Customer Care Center Service using Dialogfow

Chatbot PTIPD Customer Care Center Service using Dialogfow

Simulation Research on Fast Matching of Big Data Based on Spark

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