scholarly journals Perencanaan Search Engine E-commerce dengan Metode Latent Semantic Indexing Berbasis Multiplatform

2017 ◽  
pp. 31
Author(s):  
Ni Made Ari Lestari ◽  
Made Sudarma
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Word Processing ◽  
Latent Semantic Indexing ◽  
Electronic Data Interchange ◽  
Levenshtein Distance ◽  
Electronic Systems ◽  
Semantic Indexing ◽  
Automated Data Collection ◽  
Data Interchange

E-commerce is a sale and purchase transactions that occur through electronic systems such as the Internet, WWW, or other computer networks. E-commerce involves electronic data interchange and automated data collection systems. In all e-commerce search engine provided a column for the search items desired by the user. In e-commerce such as Tokopedia, Lazada, MatahariMall, Amazon, and other search engines that provided just use a regular search engine technology. In the usual search engines getting longer sentences from the input or output of goods search results will be more extensive and more. However, by utilizing the semantic indexing technology, the longer and clear input desired goods, the number of searches will be few and accurately in accordance with the input that helps the user in decision making. In this study discussed how to build a search engine on the web e-commerce by using Latent Semantic Indexing. The first starts from the use of Text Mining methods for word processing, and the method Levenshtein Distance to repair automatic word and the last Latent Semantic Indexing for information processing and input expenditure.

scholarly journals Enhanced Latent Semantic Indexing Using Cosine Similarity Measures for Medical Application

2020 ◽  
Vol 17 (5) ◽  
pp. 742-749
Author(s):  
Fawaz Al-Anzi ◽  
Dia AbuZeina
Keyword(s):  
Language Processing ◽  
Search Engines ◽  
Dimensional Space ◽  
Similarity Measures ◽  
Medical Application ◽  
Latent Semantic Indexing ◽  
Arabic Language ◽  
Cosine Similarity ◽  
Semantic Indexing ◽  
Cosine Similarity Measures

The Vector Space Model (VSM) is widely used in data mining and Information Retrieval (IR) systems as a common document representation model. However, there are some challenges to this technique such as high dimensional space and semantic looseness of the representation. Consequently, the Latent Semantic Indexing (LSI) was suggested to reduce the feature dimensions and to generate semantic rich features that can represent conceptual term-document associations. In fact, LSI has been effectively employed in search engines and many other Natural Language Processing (NLP) applications. Researchers thereby promote endless effort seeking for better performance. In this paper, we propose an innovative method that can be used in search engines to find better matched contents of the retrieving documents. The proposed method introduces a new extension for the LSI technique based on the cosine similarity measures. The performance evaluation was carried out using an Arabic language data collection that contains 800 medical related documents, with more than 47,222 unique words. The proposed method was assessed using a small testing set that contains five medical keywords. The results show that the performance of the proposed method is superior when compared to the standard LSI

scholarly journals Autocomplete and Spell Checking Levenshtein Distance Algorithm To Getting Text Suggest Error Data Searching In Library

2018 ◽  
Vol 5 (1) ◽  
pp. 75
Author(s):  
Muhamad Maulana Yulianto ◽  
Riza Arifudin ◽  
Alamsyah Alamsyah
Keyword(s):  
Computer Program ◽  
Search Engine ◽  
Search Engines ◽  
Internet Technology ◽  
The Internet ◽  
Levenshtein Distance ◽  
Input Process ◽  
Data Search ◽  
Source Case

Nowadays internet technology provide more convenience for searching information on a daily. Users are allowed to find and publish their resources on the internet using search engine. Search engine is a computer program designed to facilitate a user to find the information or data that they need. Search engines generally find the data based on keywords they entered, therefore a lot of case when the user can’t find the data that they need because there are an error while entering a keyword. Thats why a search engine with the ability to detect the entered words is required so the error can be avoided while we search the data. The feature that used to provide the text suggestion is autocomplete and spell checking using Levenshtein distance algorithm. The purpose of this research is to apply the autocomplete feature and spell checking with Levenshtein distance algorithm to get text suggestion in an error data searching in library and determine the level of accuracy on data search trials. This research using 1155 data obtained from UNNES Library. The variables are the input process and the classification of books. The accuracy of Levenshtein algorithm is 86% based on 1055 source case and 100 target case.

scholarly journals Latent semantic indexing (LSI) and its impact on copywriting

2019 ◽  
pp. 4-12
Author(s):  
N. Blynova
Keyword(s):  
Search Engines ◽  
Ordinary Language ◽  
Latent Semantic Indexing ◽  
Semantic Indexing ◽  
New Paradigm ◽  
Keywords And Phrases ◽  
The Media ◽  
Internet Environment ◽  
The Difference ◽  
Key Phrases

Latent semantic indexing (LSI) is becoming more and more popular in copywriting, gradually replacing texts written on the principles of SEO. LSI was called in the 2010s, when popular search engines switched to a qualitatively new way of ranking materials and sites. The difference between SEO and LSI ways of creation lies in the fact that search engines rank SEO materials by keywords, while LSI are ranked how fully the topic is covered and how useful the article will be to the reader. Consequently, in addition to keywords and phrases, the associative core is involved here. Materials written for people have replaced the texts created for the search engine. The article describes the algorithm for creation of the associative and thematic core, the ways in which this can be done. The basic steps helping to create an LSI text are also shown.The author underlines that due to the specificity of the presentation of a significant amount of information and the maximum expertise in the disclosure of the topic, text writers accustomed to working on the principles of SEO have to learn to write within a new paradigm. The owners of the websites that host articles created by LSI principles have discovered the advantages of this way of presenting information, since their resources have become better indexed and take the leading positions in search results. Such algorithms as “Baden-Baden”, “Korolev” and “Panda” have positively influenced the Internet environment as a whole, since re-optimized texts, which were filled with keys and were of little use to the reader, now have turned out to be on the last positions of issue. The new method of ranking according to the LSI method allows specialists to create the texts that are not only useful and expert but also differ in lexical richness, using expressive and figurative means of the language, which could not be assumed in SEO materials.It is highlighted in the article the use of neural networks should bring the way of presenting information to the consumer’s needs even more, inventing techniques that will allow leading materials created in an ordinary language to lead the positions without the need to incorporate key phrases into the text. We believe that the LSI-method, which has perfectly manifested itself in copywriting, is capable of unlocking the potential of the media texts, which are now being written on the principles of SEO.

Electronic document management in chain companies of the FMCG-segment

2019 ◽  
pp. 46-51
Author(s):  
O. V. Voronova ◽  
V. A. Khareva
Keyword(s):  
Business Processes ◽  
Electronic Data Interchange ◽  
Retail Trade ◽  
Document Management ◽  
Electronic Document ◽  
Electronic Data ◽  
Document Management System ◽  
Electronic Document Management ◽  
Data Interchange ◽  
Chain Retail

This article considers the features of electronic document management at retail trade enterprises of the FMCG-segment. The paper observes the types of document flow at chain companies and examines the process of implementation of electronic document management system. The notions of “electronic document management” and “electronic data interchange” are also distinguished in the article.The paper explores positive changes caused by the introduction of electronic document management and the complexity of its implementation. The study reveals that in the Russian Federation the process of mass introduction of electronic document management in the chain retail companies of the FMCG segment has been ongoing for about ten years, though has reached the highest level of its activity in the last two years. At present day, the major part of chain retail companies in the FMCG-segment has already started to actively work with the Electronic Data Interchange system. Moreover, in recent years the number of partner-enterprises that join this system has been steadily increasing.The results of the study show that introduction of electronic document management in chain companies of the FMCG-segment allows to reduce the time spent for processing documents and to track all stages of the core business processes more effectively. It also ensures information security, improves staff discipline and the quality of service, which in its turn significantly improves management efficiency of the company in general.

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