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 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 .

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 UNTUK MEMPREDIKSI PRESTASI SISWA SEKOLAH MENENGAH KEJURUAN

2019 ◽  
Vol 5 (1) ◽  
pp. 23-28
Author(s):  
Astrid Noviriandini ◽  
Nurajijah Nurajijah
Keyword(s):  
Student Achievement ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Tree Model ◽  
Learning Achievement ◽  
Achievement Prediction ◽  
C4.5 Decision Tree ◽  
Auc Value ◽  
Bayes Algorithm

This research informs students and teachers to anticipate early in following the learning period in order to get maximum learning outcomes. The method used is C4.5 decision tree algorithm and Naïve Bayes algorithm. The purpose of this study was to compare and evaluate the decision tree model C4.5 as the selected algorithm and Naïve Bayes to find out algorithms that have higher accuracy in predicting student achievement. Learning achievement can be measured by the value of report cards. After comparison of the two algorithms, the results of the learning achievement prediction are obtained. The results showed that the Naïve Bayes algorithm had an accuracy value of 95.67% and the AUC value of 0.999 was included in Excellent Clasification, for the C4.5 algorithm the accuracy value was 90.91% and the AUC value of 0.639 was included in the state of Poor Clasification. Thus the Naïve Bayes algorithm can better predict student achievement.

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%.

scholarly journals IMPLEMENTASI ALGORITMA NAIVE BAYES DAN RANDOM FOREST DALAM MEMPREDIKSI PRESTASI AKADEMIK MAHASISWA UNIVERSITAS ISLAM NEGERI AR-RANIRY BANDA ACEH

2020 ◽  
Vol 4 (1) ◽  
pp. 50
Author(s):  
Bustami Yusuf ◽  
Muthmainna Qalbi ◽  
Basrul Basrul ◽  
Ima Dwitawati ◽  
Malahayati Malahayati ◽  
...  
Keyword(s):  
Academic Achievement ◽  
Student Achievement ◽  
Random Forest ◽  
Naive Bayes ◽  
Secondary Data ◽  
Naïve Bayes ◽  
Random Forest Algorithm ◽  
Two Factors ◽  
Bayes Algorithm ◽  
The Individual

Academic achievement is determined by two factors, namely internal factors originating from within the individual in this case students and external factors that come from outside the individual or things that are influenced by the environment. There are many ways to find an academic achievement, one of which uses data mining which aims to predict or classify data using a classification algorithm. This study aims to 1) find out how to apply the Naive Bayes algorithm to student achievement, and 2) see the accuracy of the Naive Bayes algorithm to student achievement. This type of research is secondary data in the form of student data obtained from the information technology center and the Ar-Raniry UIN database. This research uses Naive Bayes algorithm and random forest algorithm. The results obtained from this study indicate the highest correlation value in the initial IP variable of r = 0.783 and the leave variable has a very weak correlation level of r = 0.054. The accuracy value of Naive Bayes algorithm after cleaning is 78.0% and Random Forest algorithm variable is 76.7%.

IMPACT OF STUDENT TEAMS ACHIEVEMENT DIVISION BASED ON HYBRID LEARNING AND JIGSAW TOWARD HIGH ORDER THINKING SKILL VOCATIONAL HIGH SCHOOL

2020 ◽  
Vol 4 (3) ◽  
pp. 560
Author(s):  
Arief Rahman Yusuf ◽  
Sandi Kurniawan ◽  
Eddy Sutadji ◽  
Imam Sudjono
Keyword(s):  
High School ◽  
Student Learning ◽  
Hybrid Learning ◽  
Thinking Skills ◽  
High Order ◽  
Learning Activities ◽  
Vocational High School ◽  
Average Value ◽  
Student Teams ◽  
High Order Thinking

The background of the research is the low assessment of high order thinking skills of students due to the conventional methods used by the school. The aims of this study are: (1) how student learning activities when using hybrid learning Student Teams Achievement Division (STAD) and jigsaw, (2) how student learning activities when taught using the direct learning model, and (3) the effect of hybrid learning Student Teams Achievement Division (STAD) and jigsaw towards high order thinking skills. This study used a quasi experimental nonequivalent control group design with the sample of 50 students from a population of vocational high school students in Ponorogo. Data collection techniques used instruments in the form of high order tests and non-test instruments in the form of observation sheets. Data analysis used was independent sample t-test. The results showed: (1) the use of Student Teams Achievement Division (STAD) based on hybrid learning and jigsaw made 28% of students were very active, 28% of students active, and 44% of students quite active in the learning process, this was evidenced by an average value of 70.56, (2) the use of direct learning models in learning made 24% of students quite active, 36% of students less active, and 40% of students passive in the learning process, which can be seen from the acquisition of an average value of 51.52, and (3) there was a significant effect of Student Learning Achievement Division (STAD) based on hybrid learning and jigsaw on students' high order thinking skills.

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%.

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