The Web is a constantly expanding global information space that includes disparate types of data and resources. Recent trends demonstrate the urgent need to manage the large amounts of data stream, especially in specific domains of application such as critical infrastructure systems, sensor networks, log file analysis, search engines and more recently, social networks. All of these applications...

In the study, two methods for recommending application software were implemented and evaluated based on their ability to recommend alternative applications with related functionality to the one that a user is currently browsing. One method was based on Term Frequency–Inverse Document Frequency (TF-IDF) and the other was based on Latent Semantic Indexing (LSI). The dataset used was a set of 2501...

Web Spam Detection is the processing to organize the search result according to specified criteria. Most often this refers to the automatic processing of search result, but the term also applies to the automatic classification of search results into ham and spam. Our work also evaluates change in performance by using different representation for the document vector like term frequency (TF), Bin...

In this study a clustering technique has been implemented which is K-Means like with hierarchical initial set (HKM). The goal of this study is to prove that clustering document sets do enhancement precision on information retrieval systems, since it was proved by Bellot & El-Beze on French language. A comparison is made between the traditional information retrieval system and the clustered one....

In the information retrieval system, relevance manifestation is pivotal and regularly based on document-term statistics, i.e., term frequency (tf), inverse document (idf), etc. Query proximity (QTP) within matched documents mostly under-explored. this article, a novel framework proposed to promote among all relevant retrieved ones. The estimation weighted combination of statistics query term-te...

TF-IDF is one of the most popular term-weighting schemes, and is applied by search engines, recommender systems, and user modeling engines. With regard to user modeling and recommender systems, we see two shortcomings of TF-IDF. First, calculating IDF requires access to the document corpus from which recommendations are made. Such access is not always given in a user-modeling or recommender sys...

Recently, the identification of human text and ChatGPT-generated has become a hot research topic. The current study presents Tunicate Swarm Algorithm with Long Short-Term Memory Recurrent Neural Network (TSA-LSTMRNN) model to detect both as well text. purpose proposed TSA-LSTMRNN method is investigate model’s decision presence any particular pattern. In addition this, technique focuses on desig...

The classification of documents is one the problems studied since ancient times and still continues to be studied. With social media becoming a part daily life its misuse, importance text has started increase. This paper investigates effect data augmentation with sentence generation on performance in an imbalanced dataset. We propose LSTM based method, Term Frequency-Inverse Document Frequency ...

Twitter is one of the most popular social media platforms in world nowadays. users Indonesia are fifth largest and always active expressing themselves getting information through tweets. A hoax a lie created as if it were true. Hoaxes also often spread via The hoaxes extremely dangerous because can cause discord even misunderstanding. Therefore, must be resisted. This study aims to build system...