Implementation of rabin-karp algorithm to determine the similarity of synoptic gospels

Khoir, Anan Bahrul; Qodim, Husnul; Busro, Busro; Atmadja, Aldy Rialdy

doi:10.1088/1742-6596/1175/1/012120

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…The fifth step involves calculating the softmax function, which acts as the last activation function of the neural network. It normalizes the network output into a probability distribution based on the predicted output classes [32]. The standard softmax function is defined within the range of K [0, 1] and can be expressed using (3).…”

Section: Figure 4 Interaction Process Between N Inputsmentioning

confidence: 99%

Generate fuzzy string-matching to build self attention on Indonesian medical-chatbot

Suwarningsih,

Nuryani

2024

IJECE

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Chatbot is a form of interactive conversation that requires quick and precise answers. The process of identifying answers to users’ questions involves string matching and handling incorrect spelling. Therefore, a system that can independently predict and correct letters is highly necessary. The approach used to address this issue is to enhance the fuzzy string-matching method by incorporating several features for self-attention. The combination of fuzzy string-matching methods employed includes Jaro Winkler distance + Levenshtein Damerau distance and Damerau Levenshtein + Rabin Carp. The reason for using this combination is their ability not only to match strings but also to correct word typing errors. This research contributes by developing a self-attention mechanism through a modified fuzzy string-matching model with enhanced word feature structures. The goal is to utilize this self-attention mechanism in constructing the Indonesian medical bidirectional encoder representations from transformers (IM-BERT). This will serve as a foundation for additional features to provide accurate answers in the Indonesian medical question and answer system, achieving an exact match of 85.7% and an F1-score of 87.6%.

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Section: Figure 4 Interaction Process Between N Inputsmentioning

confidence: 99%

Generate fuzzy string-matching to build self attention on Indonesian medical-chatbot

Suwarningsih,

Nuryani

2024

IJECE

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“…Dice Similarity determines the degree of similarity between the vector data by dividing the number of vector values by the number of intersection values between vector 1 and the other vectors. Equation 6 [18] can be used to calculate dice similarity. In this equation, x is equal to (x1, x2, x3,..., xn), y is equal to (y1, y2, y3,..., yn), and ∩ is the intersection of the vector data.…”

Section: Dice Similaritymentioning

confidence: 99%

Comparison of Similarity Methods on New Student Admission Chatbots Using Retrieval-Based Concepts

Priyatno,

Prasetya,

Cholidhazia

et al. 2024

jesa

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A college's students are an essential component. The college always opens registration for new students each year. Every year, more than 1,000 prospective new students register. Because of this, the new student admissions committee is constantly overwhelmed when responding to campus-related questions. As a result, developing a chatbot to assist new students is necessary. The best similarity method is needed for the development of a chatbot using a retrieval-model approach. The New Student Admission Chatbot and the Similarity Method are compared in this study using the Retrieval-Based Concept. The cosine, Jaccard, dice, euclidean, Manhattan, Canberra, and Chebyshev similarity methods are compared. In the context of Universitas Pahlawan Tuanku Tambusai, the data used are information about new students as well as accreditation for study program. There are 41 pieces of information used. Labels and information make up data. According to the test results, the dice and cosine similarity methods are the most effective. On all tested thresholds, dice and cosine similarity achieved an f1-score above 80%. Recall produces extremely optimal results, including 100%.Over 75% of the time, good results are reliably achieved. This demonstrates that the retrieval-model concept can be applied

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“…In this case, the k-gram value also becomes minimum string length for each comparison. Example: Words : kursinyaadi K-Gram : 4 Result : {kurs} {ursi} {rsin} {siny} {inya} {nyaa} {yaad} {aadi} D. Hashing Hashing is a function convert sub-strings into numbers where the output will be called as hash [12]. It actually works by using the formula below.…”

Section: B Rabin Karp Algorithmmentioning

confidence: 99%

Implementation of Rabin Karp Algorithm in E-Commerce Search Box Feature (Case Study: Sinar Baja Store)

Arifin

Ismail

Firdaus

2023

Journal of Information Systems

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Inside any e-commerce platform, search features are a key factor in an online business. In order to look for the desired item name, users need to type a pattern into the search feature. Inside the typing process users may make some mistypes. Based on the problem, the writer tried to create a search box as the search feature inside an e-commerce system with a capability to look for item names, even if there are some accidental mistypes inside the user's typing process. The main purpose is to reduce the possibility of empty results after a searching process due to the user’s accidental mistypes. This research utilizes Rabin Karp Algorithm in order to look for the item names. Result of this research is that the Rabin Karp Algorithm can be implemented in a website-based e-commerce, but this method is slower than SQL as a method to search item names inside the same system. It happens because Rabin Karp requires a longer process than SQL to get the similarity percentage from each comparison. This research shows that k-gram value inside the algorithm affects the overall results with a condition where greater the value.

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Implementation of rabin-karp algorithm to determine the similarity of synoptic gospels

Cited by 5 publications

References 4 publications

Generate fuzzy string-matching to build self attention on Indonesian medical-chatbot

Generate fuzzy string-matching to build self attention on Indonesian medical-chatbot

Comparison of Similarity Methods on New Student Admission Chatbots Using Retrieval-Based Concepts

Implementation of Rabin Karp Algorithm in E-Commerce Search Box Feature (Case Study: Sinar Baja Store)

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