A Descriptive Answer Evaluation System Using Cosine Similarity Technique

2021 ◽  
Author(s):  
Meenakshi A. Thalor
Keyword(s):  
Evaluation System ◽  
Cosine Similarity

scholarly journals Information Retrieval Document Classified with K-Nearest Neighbor

2018 ◽  
Vol 1 (2) ◽  
pp. 129 ◽  
Author(s):  
Alifian Sukma ◽  
Badruz Zaman ◽  
Endah Purwanti
Keyword(s):  
Information Retrieval ◽  
Evaluation System ◽  
Nearest Neighbor ◽  
Technology Development ◽  
Health Science ◽  
Cosine Similarity ◽  
K Nearest Neighbor ◽  
Physical Sciences ◽  
Social Sciences And Humanities ◽  
A Value

Along with the rapid advancement of technology development led to the amount of information available is also increasingly abundant. The aim of this study was to determine how the implementation of information retrieval system in the classification of the journal by using the cosine similarity and K-Nearest Neighbor (KNN).The data used as many as 160 documents with categories such as Physical Sciences and Engineering, Life Science, Health Science, and Social Sciences and Humanities. Construction stage begins with the use of text mining processing, the weighting of each token by using the term frequency-inverse document frequency (TF-IDF), calculate the degree of similarity of each document by using the cosine similarity and classification using k-Nearest Neighbor.Evaluation is done by using the testing documents as much as 20 documents, with a value of k = {37, 41, 43}. Evaluation system shows the level of success in classifying documents on the value of k = 43 with a value precision of 0501. System test results showed that 20 document testing used can be classified according to the actual category

scholarly journals Information Retrieval Document Classified with K-Nearest Neighbor

2016 ◽  
Vol 1 (2) ◽  
pp. 129
Author(s):  
Badruz Zaman ◽  
Endah Purwanti ◽  
Alifian Sukma
Keyword(s):  
Information Retrieval ◽  
Evaluation System ◽  
Nearest Neighbor ◽  
Technology Development ◽  
Health Science ◽  
Cosine Similarity ◽  
K Nearest Neighbor ◽  
Physical Sciences ◽  
Social Sciences And Humanities ◽  
A Value

Along with the rapid advancement of technology development led to the amount of information available is also increasingly abundant. The aim of this study was to determine how the implementation of information retrieval system in the classification of the journal by using the cosine similarity and K-Nearest Neighbor (KNN). The data used as many as 160 documents with categories such as Physical Sciences and Engineering, Life Science, Health Science, and Social Sciences and Humanities. Construction stage begins with the use of text mining processing, the weighting of each token by using the term frequency-inverse document frequency (TF-IDF), calculate the degree of similarity of each document by using the cosine similarity and classification using k-Nearest Neighbor.Evaluation is done by using the testing documents as much as 20 documents, with a value of k = {37, 41, 43}. Evaluation system shows the level of success in classifying documents on the value of k = 43 with a value precision of 0501. System test results showed that 20 document testing used can be classified according to the actual category.

scholarly journals Implementation of Cosine Similarity in an automatic classifier for comments

2019 ◽  
Vol 3 (2) ◽  
pp. 110
Author(s):  
Muhammad Habibi
Keyword(s):  
Evaluation System ◽  
Cross Validation ◽  
Cosine Similarity ◽  
Assessment Process ◽  
Classification Model ◽  
Learning Evaluation ◽  
Similarity Method ◽  
Fold Cross Validation

Classification of text with a large amount is needed to extract the information contained in it. Student comments containing suggestions and criticisms about the lecturer and the lecture process on the learning evaluation system are not well classified, resulting in a difficult assessment process. So from that, we need a classification model that can classify comments automatically into classification categories. The method used is the Cosine Similarity method, which is a method for calculating similarities between two objects expressed in two vectors. The data used in this study were 1,630 comment data with several different categories. The test in this study uses k-fold cross-validation with k = 10. The results showed that the percentage accuracy of the classification model was 80.87%.

scholarly journals Research on Intelligent Evaluation System of Influence Model Using Cosine Similarity

2021 ◽  
Vol 1982 (1) ◽  
pp. 012035
Author(s):  
Zhitao Li ◽  
Xin Wang ◽  
Xiaoling Li
Keyword(s):  
Evaluation System ◽  
Cosine Similarity ◽  
Influence Model

A proposed method for assessing the quality of sealants - the CCC Sealant Evaluation System

2001 ◽  
Vol 29 (2) ◽  
pp. 83-91 ◽  
Author(s):  
Christopher Deery ◽  
Hazel E. Fyffe ◽  
Zoann J. Nugent ◽  
Nigel M. Nuttall ◽  
Nigel B. Pitts
Keyword(s):  
Evaluation System

R I Crash Outcome Data Evaluation System (CODES)

2005 ◽  
Author(s):  
Ted Donnelly
Keyword(s):  
Evaluation System ◽  
Outcome Data ◽  
Data Evaluation

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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