scholarly journals Ranking Index Berita New Normal dengan Metode Information Retrieval Menggunakan Vector Space Model

2020 ◽  
Vol 5 (1) ◽  
pp. 61
Author(s):  
Nandang Suwela
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
New Normal ◽  
Space Model ◽  
Ranking Index

Aplikasi Deteksi Kemiripan Tugas Paper

2017 ◽  
Vol 15 (2) ◽  
pp. 5
Author(s):  
Anthony Anggrawan ◽  
Azhari
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Mean Average Precision ◽  
Average Precision ◽  
Information Searching ◽  
Space Model ◽  
Model Method

Information searching based on users’ query, which is hopefully able to find the documents based on users’ need, is known as Information Retrieval. This research uses Vector Space Model method in determining the similarity percentage of each student’s assignment. This research uses PHP programming and MySQL database. The finding is represented by ranking the similarity of document with query, with mean average precision value of 0,874. It shows how accurate the application with the examination done by the experts, which is gained from the evaluation with 5 queries that is compared to 25 samples of documents. If the number of counted assignments has higher similarity, thus the process of similarity counting needs more time, it depends on the assignment’s number which is submitted.

On Generalized Vector Space Model in Information Retrieval

1985 ◽  
Vol 8 (2) ◽  
pp. 253-267
Author(s):  
S.K.M. Wong ◽  
Wojciech Ziarko
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
A Priori ◽  
Vector Space Model ◽  
Smart System ◽  
Space Model ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Index Terms ◽  
Minimal Modification

In information retrieval, it is common to model index terms and documents as vectors in a suitably defined vector space. The main difficulty with this approach is that the explicit representation of term vectors is not known a priori. For this reason, the vector space model adopted by Salton for the SMART system treats the terms as a set of orthogonal vectors. In such a model it is often necessary to adopt a separate, corrective procedure to take into account the correlations between terms. In this paper, we propose a systematic method (the generalized vector space model) to compute term correlations directly from automatic indexing scheme. We also demonstrate how such correlations can be included with minimal modification in the existing vector based information retrieval systems.

scholarly journals The Performance of Boolean Retrieval and Vector Space Model in Textual Information Retrieval

2017 ◽  
Vol 11 (1) ◽  
pp. 33 ◽  
Author(s):  
Budi Yulianto ◽  
Widodo Budiharto ◽  
Iman Herwidiana Kartowisastro
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Experimental Results ◽  
Inverted Index ◽  
Exact Results ◽  
Textual Information ◽  
Space Model ◽  
Corpus Data

Boolean Retrieval (BR) and Vector Space Model (VSM) are very popular methods in information retrieval for creating an inverted index and querying terms. BR method searches the exact results of the textual information retrieval without ranking the results. VSM method searches and ranks the results. This study empirically compares the two methods. The research utilizes a sample of the corpus data obtained from Reuters. The experimental results show that the required times to produce an inverted index by the two methods are nearly the same. However, a difference exists on the querying index. The results also show that the numberof generated indexes, the sizes of the generated files, and the duration of reading and searching an index are proportional with the file number in the corpus and thefile size.

scholarly journals Analysis of a Vector Space Model, Latent Semantic Indexing and Formal Concept Analysis for Information Retrieval

2012 ◽  
Vol 12 (1) ◽  
pp. 34-48 ◽  
Author(s):  
Ch. Aswani Kumar ◽  
M. Radvansky ◽  
J. Annapurna
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Formal Concept Analysis ◽  
Vector Space Model ◽  
Latent Semantic Indexing ◽  
Concept Analysis ◽  
Formal Concept ◽  
Semantic Indexing ◽  
Space Model ◽  
Classical Vector

Abstract Latent Semantic Indexing (LSI), a variant of classical Vector Space Model (VSM), is an Information Retrieval (IR) model that attempts to capture the latent semantic relationship between the data items. Mathematical lattices, under the framework of Formal Concept Analysis (FCA), represent conceptual hierarchies in data and retrieve the information. However, both LSI and FCA use the data represented in the form of matrices. The objective of this paper is to systematically analyze VSM, LSI and FCA for the task of IR using standard and real life datasets.

scholarly journals INFORMATION RETRIEVAL PADA DATA JUDUL SKRIPSI BERBASIS TEXT MENGGUNAKAN VECTOR SPACE MODEL

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Eka Sabna
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Vector Model ◽  
Space Vector ◽  
Space Model

Penyimpanan data judul skripsi mahasiswa semakin banyak dan akan terus bertambah.  Untuk mencari informasi dari judul skripsi tersebut akan menjadi sulit. Untuk itu dikembangkanlah metode pencarian yang disebut dengan temu-kembali informasi (information retrieval). Metode-metode temu-kembali informasi sudah dikenal sejak lama, salah satu dari metode tersebut yang paling banyak digunakan karena kemudahan implementasinya adalah Space Vector Model (SVM). Tujuan  penelitian  ini adalah memberikan paparan tentang proses pencarian  dokumen  digital dengan metode Vektor Space Model. Pada model ini dilakukan dengan proses  token dan    indexing   sehingga    ditemukan    hasil    dari maksimal  terdapat  dalam  data judul skripsi  menggunakan kata    kunci,    sehingga    di lakukan pencarian   sesuai   dengan   kata   kunci  dan   akan   dibandingkan dengan     data     yang     terdapat     pada     file dokumen judul skripsi, sehingga    dapat    menghasilkan    informasi    yang benar.

scholarly journals Sistem Temu Kembali Informasi Pada Gejala Autisme Dengan Metode Vector Space Model

2019 ◽  
Vol 3 (2) ◽  
pp. 257-264
Author(s):  
Bayu Sugara ◽  
Dody Dody ◽  
Donny Donny
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Large Scale ◽  
Retrieval System ◽  
Dimensional Space ◽  
Vector Space Model ◽  
The Internet ◽  
Space Model ◽  
Information And Communication ◽  
Retrieval Software

Information is now very easy to get anywhere. Information technology, especially the internet, strongly supports the exchange of information very quickly. The internet has become an information and communication media that has been used by many people with many interests, especially in taking large-scale information data, Unfortunately the information presented is sometimes less relevant. Quality information is influenced by relevance, accuracy and on time. However, there are not many effective search systems available. This study discusses the implementation of an information retrieval system to find and find symptoms of autism disorders using the Vector Space Model (VSM) method. Vector Space Model (VSM) is a model used to measure the similarity between a document and a query. In this model, queries and documents are considered vectors in n dimensional space. Where n is the number of all terms listed. The purpose of this study was to design an information retrieval software to find and match the symptoms of autism disorders. By using Vector Space Model, it is hoped that it can provide a solution to the search engine to provide text matching information in the database using certain keywords, the results of the matching are presented in the form of ranks.

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