Information Retrieval System for Searching JSON Files with Vector Space Model Method

Wahyudi, Eko; Sfenrianto, Sfenrianto; Hakim, Marwan; Subandi, Reko; Sulaeman, Okky Robiana; Setiyawan, Rochmat

doi:10.1109/icaiit.2019.8834457

Cited by 7 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Temat budowy wydajności przetwarzania plików JSON został poruszony parokrotnie, jednakże nie w kontekście parserów ogólnego zastosowania. Analizowane publikacje skupiają się raczej na wydajności konkretnych czynności, jak poszukiwanie słów kluczowych [3] lub wyszukiwanie analityczne [4]. W tej sytuacji można mówić o proponowaniu nowej metody przetwarzania, różniącej się od istniejących implementacji.…”

Section: Przegląd Sposobu Działania Istniejących Parserówunclassified

Performance comparison of chosen JSON parsers and a parser that employs a different reading method

Koter¹

2020

jcsi

View full text Add to dashboard Cite

show abstract

Section: Przegląd Sposobu Działania Istniejących Parserówunclassified

Performance comparison of chosen JSON parsers and a parser that employs a different reading method

Koter¹

2020

jcsi

View full text Add to dashboard Cite

show abstract

“…One of the most popular similarity measurements for VSM is Cosine similarity, and one way to encode textual documents into vectors is Term Frequency-inverse Document Frequency (TF-IDF). Wahyudi et al [13] conducted research using cosine similarity with TF-IDF as a weighting scheme for searching JSON files relevant to a given query. Rofiqi et al [14] conducted a study to find news documents based on similarities using TF-IDF.…”

Section: Introductionmentioning

confidence: 99%

Medical Record Document Search with TF-IDF and Vector Space Model (VSM)

Heryawan,

Novitaningrum,

Nastiti

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

The growth of medical record documents is increasing over time, and the various types of diseases and therapies needed are increasing. However, this has not been followed by an effective and efficient search process. This study aims to deal with search problems that often take a long time with search results that are not necessarily as expected by building a search model for medical record documents using the vector space model (VSM) and TF-IDF methods. The VSM method allows retrieval of results that are not the same as the search queries entered by the user but are expected to provide still results relevant to the user's desired needs. The model development process was taken based on the data in the FS_ANAMNESA and FS_DIAGNOSA columns, followed by preprocessing, which consists of deleting blank lines, lowercase, removing punctuation marks, HTML tags, stop words, excess spaces between words, and normalizing typo words, then forming a TF-IDF matrix based on the frequency of occurrence of each word feature, and followed by the calculation of the similarity value of the search query compared to medical record documents based on the cosine similarity formula. The retrieval results were all columns of each existing medical record document and were sorted based on 10 rows with the highest similarity value. The model evaluation results were based on 1000 medical record documents and tested with 20 search queries in this study, which gave an average precision value of 0.548 and an average recall value of 0.796.

show abstract

“…1009 query, and assign scores from the biggest to smallest. The documents and query are assigned with weights using term frequency and inverse document frequency method [7]. There are many variants TF-IDF model to get the weights including term frequency, classical TF-IDF, normalized TF-IDF and sub-linear normalized TF-IDF [8].…”

Section: Introductionmentioning

confidence: 99%

The search for science and technology verses in Qur’an and hadith

et al. 2021

View full text Add to dashboard Cite

Currently, the Vector Space Model algorithm has been widely implemented for the document search feature because of its reliability in retrieving information. One of them in the search for verses of the Qur'an based on the translation. However, if the phrase or word used is different (even though it has one meaning) with the word in the document in the database, the system will not display the verse. As we know that the Qur'an has a very deep meaning, so an interpretation of the verse is needed. Therefore, this research focuses on implementing the Vector Space Model (VSM) algorithm for searching verses and hadiths in science and technology by using the discussion parameters of these verses or hadiths. The test results obtained with 20 keyword samples using metric recall were 81% with an average time of 2.24 seconds.

show abstract

Information Retrieval System for Searching JSON Files with Vector Space Model Method

Cited by 7 publications

References 10 publications

Performance comparison of chosen JSON parsers and a parser that employs a different reading method

Performance comparison of chosen JSON parsers and a parser that employs a different reading method

Medical Record Document Search with TF-IDF and Vector Space Model (VSM)

The search for science and technology verses in Qur’an and hadith

Contact Info

Product

Resources

About