scholarly journals SISTEM TEMU KEMBALI INFORMASI PADA DOKUMEN DENGAN METODE VECTOR SPACE MODEL

2017 ◽  
Vol 9 (1) ◽  
pp. 74
Author(s):  
Irmawati Irmawati
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Search Engine ◽  
Vector Space Model ◽  
Space Model

Informasi saat ini sangat mudah didapatkan dengan memanfaatkan fasilitas internet dimanapun dan kapanpun. Di sisi lain informasi yang didapat dari  search engine merupakan semua hal yang berkaitan dengan kata kunci yang dicari. Hal ini menyebabkan pengguna terpaksa menyaring untuk mendapatkan dokumen yang relevan. Oleh karena itu diperlukan cara untuk mengelompokkan banyaknya informasi yang tersedia, yang dibutuhkan pengguna sehingga memudahkan pengguna untuk mendapatkan dokumen yang diinginkan. Pada penelitian ini diusulkan suatu solusi dari permasalahan tersebut dengan mengembangkan metode ilmu pencarian yang dikenal dengan temu-kembali informasi (information retrieval) dan metode Vector Space Model (VSM). Pada metode Vector Space Model (VSM) beberapa dokumen online akan diindeks dan diurutkan berdasarkan bobot dari kata pencarian yang terdapat di dalam dokumen online tersebut. Salah satu algoritma pembobotannya adalah algoritma tf-idf yang dipengaruhi oleh frekuensi kemunculan kata pada tiap dokumen online dan frekuensi dari dokumen online yang memiliki kata tersebut.

scholarly journals SISTEM TEMU KEMBALI INFORMASI PADA DOKUMEN DENGAN METODE VECTOR SPACE MODEL

2017 ◽  
Vol 9 (1) ◽  
pp. 74
Author(s):  
Irmawati Irmawati
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Search Engine ◽  
Vector Space Model ◽  
Space Model

Informasi saat ini sangat mudah didapatkan dengan memanfaatkan fasilitas internet dimanapun dan kapanpun. Di sisi lain informasi yang didapat dari  search engine merupakan semua hal yang berkaitan dengan kata kunci yang dicari. Hal ini menyebabkan pengguna terpaksa menyaring untuk mendapatkan dokumen yang relevan. Oleh karena itu diperlukan cara untuk mengelompokkan banyaknya informasi yang tersedia, yang dibutuhkan pengguna sehingga memudahkan pengguna untuk mendapatkan dokumen yang diinginkan. Pada penelitian ini diusulkan suatu solusi dari permasalahan tersebut dengan mengembangkan metode ilmu pencarian yang dikenal dengan temu-kembali informasi (information retrieval) dan metode Vector Space Model (VSM). Pada metode Vector Space Model (VSM) beberapa dokumen online akan diindeks dan diurutkan berdasarkan bobot dari kata pencarian yang terdapat di dalam dokumen online tersebut. Salah satu algoritma pembobotannya adalah algoritma tf-idf yang dipengaruhi oleh frekuensi kemunculan kata pada tiap dokumen online dan frekuensi dari dokumen online yang memiliki kata tersebut.

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.

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