Implementation of Weighted Naive Bayes Algorithm for Major Determination in Indonesian High School

2018 ◽  
Author(s):  
Erwin Yudi Hidayat ◽  
Aulia Sabiq Taufiqurrahman ◽  
Ardytha Luthfiarta ◽  
Junta Zeniarja ◽  
Heru Agus Santoso ◽  
...  
Keyword(s):  
High School ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Algorithm

scholarly journals ANALISIS KINERJA ALGORITMA C4.5 DAN NAÏVE BAYES DALAM MEMPREDIKSI KEBERHASILAN SEKOLAH MENGHADAPI UN

2020 ◽  
Vol 5 (2) ◽  
pp. 285-290
Author(s):  
Yeni Angraini ◽  
Siti Fauziah ◽  
Jordi Lasmana Putra
Keyword(s):  
High School ◽  
Junior High School ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
National Examination ◽  
National Examinations ◽  
C4.5 Algorithm ◽  
Student Graduation ◽  
Bayes Algorithm ◽  
Almost All

The national exam (UN) is one of the determinants of student graduation, both elementary school, junior high school and even high school. There are many businesses that are carried out by schools to prepare their students to face national examinations. In fact almost all schools provide material deepening to their students for subjects tested at the national examination. Therefore, this study was conducted to determine the level of success of the school in preparing students in facing national examinations. The method used is a decision tree with C4.5 algorithm and naïve Bayes algorithm. From the results of the study, the results of the accuracy of the naïve bayes algorithm were as big as 95,50% , while accuracy using the c4.5 algorithm is equal to 78,50%. Then it can be concluded that the predictions generated from the naïve bayes algorithm are better compared to the c4.5 algorithm .

scholarly journals Intelligent Computing System to Predict Vocational High School Student Learning Achievement Using Naïve Bayes Algorithm

2019 ◽  
Vol 4 (1) ◽  
pp. 15
Author(s):  
Admaja Dwi Herlambang ◽  
Satrio Hadi Wijoyo ◽  
Aditya Rachmadi
Keyword(s):  
High School ◽  
Student Achievement ◽  
Student Learning ◽  
Naive Bayes ◽  
Computing System ◽  
Naïve Bayes ◽  
Vocational High School ◽  
Intelligent Computing ◽  
Bayes Algorithm ◽  
Intelligent Computing System

Vocational High School with ICT major need an intelligent computing system that could predict the student learning achievement. The system used fifteen achievement indicators and Naïve Bayes algorithm in data processing. Testing on student achievement data produces the conclusion that is the highest intelligent accuracy values in 53% with lowest accuracy value in 48% based on Naïve Bayes algorithm processing. The result of mining process using Naïve Bayes algorithm can be used to classify the 3rd year student achievement to five categories. These categories are Very Good, Good, Fair, Poor, and Failed. The system testing result showed that this intelligent computing system function was fitted with Vocational High School’s system requirement, system design, and system implementation.

scholarly journals Implementation Naïve Bayes Algorithm for Student Classification Based on Graduation Status

2018 ◽  
Vol 1 (2) ◽  
pp. 6-12
Author(s):  
Ayundyah Kesumawati ◽  
Din Waikabu
Keyword(s):  
High School ◽  
Grade Point Average ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Grade Point ◽  
Normal Limits ◽  
Bayes Algorithm ◽  
Student Classification ◽  
Value Of Accreditation

Length of study in college is a time it takes a student to have completed the study in college. Bachelor degree in achieving normal that it takes time for four years, but still there are students who completed their studies beyond normal limits (over four years). This such as influence on the value of accreditation of the institution. In this paper, we used five variables: grade point average (GPA), Concentration in High School, Sex, participation in assistance and city of residence, which are classified by the Graduation Status students over four years and less than equal to four years. The method used for the classification of a student's study time is Naive Bayes algorithm. This study investigated classification student based on Graduation Status in Department Statistics of the Islamic University of Indonesia. From the result, Naïve Bayes algorithm classification is quite good with accuracy value for Naïve Bayes is 81,18%.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 504-512
Author(s):  
Faried Zamachsari ◽  
Gabriel Vangeran Saragih ◽  
Susafa'ati ◽  
Windu Gata
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Public Opinion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Capital City ◽  
Support Vector ◽  
National Capital ◽  
Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

Research and Application of Artificial Intelligence Based Integrated Teaching-Learning Modular Approach in Colleges and Universities

2021 ◽  
Author(s):  
Lingchong Jia ◽  
B. Santhosh Kumar ◽  
R. Parthasarathy
Keyword(s):  
Artificial Intelligence ◽  
Student Development ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Learning Performance ◽  
Barriers To Entry ◽  
Final Exam ◽  
Artificial Intelligence Technology ◽  
Bayes Algorithm ◽  
Teaching Learning

Nowadays, in various educational institutions, artificial intelligence technology is applied effectively and successfully. This artificial intelligence improves learning and student development in academic performance. Challenges of the conventional education approach, students’ dependence on teachers in all resources for study, unavailability of professional instructors, and a greater focus on conditioning learning than practical usefulness lead to lower learning performance. In this paper integrated teaching-learning model approach has been proposed using artificial intelligence in student education. It involves speeding up fulfilling education targets by reducing barriers to entry, automating management processes, and maximizing learning performance. The proposed ITLMA method used the naive Bayes algorithm to evaluate the student ranking using a class score, task, project score, and final exam. The result of artificial intelligence-based ITLMA and naive Bayes algorithm hasa high accuracy ratio of 80.1% with less error ratio of 15.7%, high prediction 88.2%, precision 98.2%, and improves student and teacher interaction compared to other existing methods.

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