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Rate Movie App: Implementation of K-Nearest Neighbors Algorithm in the Development of Decision Support System for Philippine Movie Rating and Classification

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@article{IJASEIT7579,
   author = {Ian Dexter M. Siñel and Benilda Eleonor V. Comendador},
   title = {Rate Movie App: Implementation of K-Nearest Neighbors Algorithm in the Development of Decision Support System for Philippine Movie Rating and Classification},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {1},
   year = {2019},
   pages = {92--99},
   keywords = {movie application; K-Nearest Neighbors algorithm; C4.5 algorithm; naïve bayes algorithm; decision support system.},
   abstract = {Movies that are publicly exhibited in the Philippine Cinema, regardless if produced locally (local films) and/or outside the country (foreign films) undergo a thorough evaluation before public exhibition to properly identify suited audiences. There are many factors that contribute to the classification and rating of a specific movie. Movies play a vital role for Filipino culture as for some people; these serve as their leisure activity, for other people, these are not just a leisure activity instead a form of visual art that may send important messages to the audiences and/or may re-enact human personal experiences. It is very important that movie(s) will be classified accordingly without any form of biases. This paper promotes a Decision Support System that can be used in predicting movie classification and rating using historically evaluated movies from 2010 to 2017. The study considers the user ratings on the following attributes: Sex & Nudity, Violence & Gore, Profanity, Alcohol, Drugs & Smoking and Frightening and Intense Scenes scrapped from a public movie database. Along with these considerations are the genre(s) associated with a movie. The study conducted revealed that K-Nearest Neighbors Algorithm outperforms Naive Bayes and J48/C4.5 Algorithm in classifying Philippine Movie rating with 92.80% accuracy as compared to 68.70% and 56.79% for Naive Bayes and J48/C4.5 algorithm respectively. The developed decision support system implements the K-Nearest Neighbors algorithm to satisfy the objectives mentioned. With this, Review Committees who evaluate movies may have guides in making critical decisions in the domain of movie evaluation.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7579},
   doi = {10.18517/ijaseit.9.1.7579}
}

EndNote

%A Siñel, Ian Dexter M.
%A Comendador, Benilda Eleonor V.
%D 2019
%T Rate Movie App: Implementation of K-Nearest Neighbors Algorithm in the Development of Decision Support System for Philippine Movie Rating and Classification
%B 2019
%9 movie application; K-Nearest Neighbors algorithm; C4.5 algorithm; naïve bayes algorithm; decision support system.
%! Rate Movie App: Implementation of K-Nearest Neighbors Algorithm in the Development of Decision Support System for Philippine Movie Rating and Classification
%K movie application; K-Nearest Neighbors algorithm; C4.5 algorithm; naïve bayes algorithm; decision support system.
%X Movies that are publicly exhibited in the Philippine Cinema, regardless if produced locally (local films) and/or outside the country (foreign films) undergo a thorough evaluation before public exhibition to properly identify suited audiences. There are many factors that contribute to the classification and rating of a specific movie. Movies play a vital role for Filipino culture as for some people; these serve as their leisure activity, for other people, these are not just a leisure activity instead a form of visual art that may send important messages to the audiences and/or may re-enact human personal experiences. It is very important that movie(s) will be classified accordingly without any form of biases. This paper promotes a Decision Support System that can be used in predicting movie classification and rating using historically evaluated movies from 2010 to 2017. The study considers the user ratings on the following attributes: Sex & Nudity, Violence & Gore, Profanity, Alcohol, Drugs & Smoking and Frightening and Intense Scenes scrapped from a public movie database. Along with these considerations are the genre(s) associated with a movie. The study conducted revealed that K-Nearest Neighbors Algorithm outperforms Naive Bayes and J48/C4.5 Algorithm in classifying Philippine Movie rating with 92.80% accuracy as compared to 68.70% and 56.79% for Naive Bayes and J48/C4.5 algorithm respectively. The developed decision support system implements the K-Nearest Neighbors algorithm to satisfy the objectives mentioned. With this, Review Committees who evaluate movies may have guides in making critical decisions in the domain of movie evaluation.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7579
%R doi:10.18517/ijaseit.9.1.7579
%J International Journal on Advanced Science, Engineering and Information Technology
%V 9
%N 1
%@ 2088-5334

IEEE

Ian Dexter M. Siñel and Benilda Eleonor V. Comendador,"Rate Movie App: Implementation of K-Nearest Neighbors Algorithm in the Development of Decision Support System for Philippine Movie Rating and Classification," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 1, pp. 92-99, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.1.7579.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Siñel, Ian Dexter M.
AU  - Comendador, Benilda Eleonor V.
PY  - 2019
TI  - Rate Movie App: Implementation of K-Nearest Neighbors Algorithm in the Development of Decision Support System for Philippine Movie Rating and Classification
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 1
Y2  - 2019
SP  - 92
EP  - 99
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - movie application; K-Nearest Neighbors algorithm; C4.5 algorithm; naïve bayes algorithm; decision support system.
N2  - Movies that are publicly exhibited in the Philippine Cinema, regardless if produced locally (local films) and/or outside the country (foreign films) undergo a thorough evaluation before public exhibition to properly identify suited audiences. There are many factors that contribute to the classification and rating of a specific movie. Movies play a vital role for Filipino culture as for some people; these serve as their leisure activity, for other people, these are not just a leisure activity instead a form of visual art that may send important messages to the audiences and/or may re-enact human personal experiences. It is very important that movie(s) will be classified accordingly without any form of biases. This paper promotes a Decision Support System that can be used in predicting movie classification and rating using historically evaluated movies from 2010 to 2017. The study considers the user ratings on the following attributes: Sex & Nudity, Violence & Gore, Profanity, Alcohol, Drugs & Smoking and Frightening and Intense Scenes scrapped from a public movie database. Along with these considerations are the genre(s) associated with a movie. The study conducted revealed that K-Nearest Neighbors Algorithm outperforms Naive Bayes and J48/C4.5 Algorithm in classifying Philippine Movie rating with 92.80% accuracy as compared to 68.70% and 56.79% for Naive Bayes and J48/C4.5 algorithm respectively. The developed decision support system implements the K-Nearest Neighbors algorithm to satisfy the objectives mentioned. With this, Review Committees who evaluate movies may have guides in making critical decisions in the domain of movie evaluation.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7579
DO  - 10.18517/ijaseit.9.1.7579

RefWorks

RT Journal Article
ID 7579
A1 Siñel, Ian Dexter M.
A1 Comendador, Benilda Eleonor V.
T1 Rate Movie App: Implementation of K-Nearest Neighbors Algorithm in the Development of Decision Support System for Philippine Movie Rating and Classification
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 1
YR 2019
SP 92
OP 99
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 movie application; K-Nearest Neighbors algorithm; C4.5 algorithm; naïve bayes algorithm; decision support system.
AB Movies that are publicly exhibited in the Philippine Cinema, regardless if produced locally (local films) and/or outside the country (foreign films) undergo a thorough evaluation before public exhibition to properly identify suited audiences. There are many factors that contribute to the classification and rating of a specific movie. Movies play a vital role for Filipino culture as for some people; these serve as their leisure activity, for other people, these are not just a leisure activity instead a form of visual art that may send important messages to the audiences and/or may re-enact human personal experiences. It is very important that movie(s) will be classified accordingly without any form of biases. This paper promotes a Decision Support System that can be used in predicting movie classification and rating using historically evaluated movies from 2010 to 2017. The study considers the user ratings on the following attributes: Sex & Nudity, Violence & Gore, Profanity, Alcohol, Drugs & Smoking and Frightening and Intense Scenes scrapped from a public movie database. Along with these considerations are the genre(s) associated with a movie. The study conducted revealed that K-Nearest Neighbors Algorithm outperforms Naive Bayes and J48/C4.5 Algorithm in classifying Philippine Movie rating with 92.80% accuracy as compared to 68.70% and 56.79% for Naive Bayes and J48/C4.5 algorithm respectively. The developed decision support system implements the K-Nearest Neighbors algorithm to satisfy the objectives mentioned. With this, Review Committees who evaluate movies may have guides in making critical decisions in the domain of movie evaluation.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7579
DO  - 10.18517/ijaseit.9.1.7579