Decision Support System for Choosing an Elective Course Using Naive Bayes Classifier

2019 ◽  
pp. 97-110
Author(s):  
Abiyoga ◽  
Arya Wicaksana ◽  
Ni Made Satvika Iswari
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Elective Course

scholarly journals Klasifikasi Data Antroprometri Individu Menggunakan Algoritma Naïve Bayes Classifier

2021 ◽  
Vol 2 (1) ◽  
pp. 1-10
Author(s):  
Johnson Sihombing
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Python Programming ◽  
Data Frequency

With the development of advances in computer technology today, most companies and organizations need a decision support system based on information systems, where the information is generally stored in the form of documents / text that is not structured. In this regard, a system for text management that is integrated with the decision support system is needed. One of them is the use of text data classification for anthropometric case studies of several samples. Anthropometry is a measurement of a person's body dimensions. The object of research is gender, first name, and height of a person. The research aims to determine the ratio of the number and height probability level of the number of men and women based on the input into an application using the Naïve Bayes Classifier method. The implementation design uses the Python programming language. The results showed that the height classification data frequency of women was more than the height classification data for men. And the number of height probability of a woman's body is greater than the number of height probability of a man's body.

scholarly journals Cranky comments: detecting clinical decision support malfunctions through free-text override reasons

2018 ◽  
Vol 26 (1) ◽  
pp. 37-43 ◽  
Author(s):  
Skye Aaron ◽  
Dustin S McEvoy ◽  
Soumi Ray ◽  
Thu-Trang T Hickman ◽  
Adam Wright
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Naive Bayes ◽  
Word List ◽  
Clinical Decision ◽  
Naïve Bayes ◽  
Free Text ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Abstract Background Rule-base clinical decision support alerts are known to malfunction, but tools for discovering malfunctions are limited. Objective Investigate whether user override comments can be used to discover malfunctions. Methods We manually classified all rules in our database with at least 10 override comments into 3 categories based on a sample of override comments: “broken,” “not broken, but could be improved,” and “not broken.” We used 3 methods (frequency of comments, cranky word list heuristic, and a Naïve Bayes classifier trained on a sample of comments) to automatically rank rules based on features of their override comments. We evaluated each ranking using the manual classification as truth. Results Of the rules investigated, 62 were broken, 13 could be improved, and the remaining 45 were not broken. Frequency of comments performed worse than a random ranking, with precision at 20 of 8 and AUC = 0.487. The cranky comments heuristic performed better with precision at 20 of 16 and AUC = 0.723. The Naïve Bayes classifier had precision at 20 of 17 and AUC = 0.738. Discussion Override comments uncovered malfunctions in 26% of all rules active in our system. This is a lower bound on total malfunctions and much higher than expected. Even for low-resource organizations, reviewing comments identified by the cranky word list heuristic may be an effective and feasible way of finding broken alerts. Conclusion Override comments are a rich data source for finding alerts that are broken or could be improved. If possible, we recommend monitoring all override comments on a regular basis.

An Approach for the Segmentation of Satellite Images Using Moving KFCM and Naive Bayes Classifier

2013 ◽  
Vol 3 (2) ◽  
pp. 7-15 ◽  
Author(s):  
S. Praveena ◽  
◽  
S.P. Singh ◽  
I.V. Muralikrishna ◽  
◽  
...  
Keyword(s):  
Satellite Images ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

IMPLEMENTASI TEOREMA NAÏVE BAYES PADA SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN REKOMENDASI TEMPAT WISATA TERBAIK DISULAWESI SELATAN

2019 ◽  
Vol 2 (1) ◽  
pp. 40-46
Author(s):  
Rikardo Chandra ◽  
Izmy alwiah Musdar ◽  
Junaedy .
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Programming Language ◽  
Support System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Web Based ◽  
Tourist Attractions ◽  
South Sulawesi

This study aims to design and build web-based decision support system applications used to recommend the best tourist attractions in South Sulawesi to tourists. The expected benefit of this research is to help the user get the best tourist recommendation information available in South Sulawesi based on the conditions in input factors. The theorem or method used in this study, namely the theorem Naïve Bayes. The design of the system isimplemented using PHP programming language and MYSQL database. Based on the results of the research, the authors have successfully built the application of decision support system to determine the recommendation of tourist attractions in South Sulawesi with 65% accuracy based on 20 tests conducted.

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