scholarly journals COMPARISON OF DATA MINING CLASSIFICATION ALGORITHM FOR PREDICTING THE PERFORMANCE OF HIGH SCHOOL STUDENTS

2020 ◽  
Vol 17 (1) ◽  
pp. 22-30
Author(s):  
Tiska Pattiasina ◽  
Didi Rosiyadi
Keyword(s):  
Data Mining ◽  
High School Students ◽  
Student Performance ◽  
Extracurricular Activities ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
Added Value ◽  
K Nearest Neighbor ◽  
School Students ◽  
Pocket Money

Data Mining is a series of processes to explore added value in the form of unknown information manually from the database. In the world of data mining education can be used to obtain information about student performance. In this study the researchers took research samples from class XI (eleven) students at SMAN 3 Ambon by classifying student performance based on thirteen attributes, namely: age, sex, school organization, extracurricular activities, pocket money, duration of study at home, duration of social media, online game duration, attendance, illness, permits, semester 1 and semester 2 grades. Using the KDD (Knowledge Discovery Database) method and classification algorithm that will be used, namely, decision tree, Naïve Bayes and K-Nearest Neighbor. And then do the test using k-fold cross validation.

scholarly journals Supervised data mining approach for predicting student performance

2019 ◽  
Vol 16 (3) ◽  
pp. 1584 ◽  
Author(s):  
Wan Fairos Wan Yaacob ◽  
Syerina Azlin Md Nasir ◽  
Wan Faizah Wan Yaacob ◽  
Norafefah Mohd Sobri
Keyword(s):  
Data Mining ◽  
Academic Success ◽  
Student Performance ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
K Nearest Neighbor ◽  
Data Mining Approach ◽  
Accuracy Measure ◽  
Student’S Performance

<span>Data mining approach has been successfully implemented in higher education and emerge as an interesting area in educational data mining research. The approach is intended for identification and extraction of new and potentially valuable knowledge from the data. Predictive model developed using supervised data mining approach can derive conclusion on students' academic success. The ability to predict student’s performance can be beneficial for innovation in modern educational systems. The main objective of this paper is to develop predictive models using classification algorithm to predict student’s performance at selected university in Malaysia. The prediction model developed can be used to identify the most important attributes in the data. Several predictive modelling techniques of K-Nearest Neighbor, Naïve Bayes, Decision Tree and Logistic Regression Model models were used to predict student’s performance whether excellent or non-excellent.  Based on accuracy measure, precision, recall and ROC curve, results show that the Naïve Bayes outperform other classification algorithm.  The Naïve Bayes reveals that the most significant factors contributing to prediction of excellent students is when the student scores A+ and A in Multivariate Analysis; A+, A and A- in SAS Programming and A, A- and B+ in ITS 472.</span>

scholarly journals Educational Data Mining in Predicting Student Final Grades

2021 ◽  
Vol 10 (1) ◽  
pp. 366-371
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Student Performance ◽  
Parameter Optimization ◽  
Nearest Neighbor ◽  
Educational Data Mining ◽  
Real Data ◽  
K Nearest Neighbor ◽  
Student’S Performance ◽  
Student Grades

Educational data mining is a field of science that extracts knowledge from educational data. One of its implementations is to predict student performance, it helps teachers to identify students that need more support. This can potentially increase learning effectiveness and elevate overall student’s grades. There are various algorithms and optimization solutions to predict student’s performance. In this paper, we use real data from one of Indonesia’s public junior high schools to compare naive bayes, decision tree, and k-nearest neighbor algorithms and implement feature selection and parameter optimization to identify which combination of algorithm and optimization can achieve the highest accuracy in predicting student grades, i.e. 7-grade classification.The results show that k-NN achieves the highest accuracy with 77.36%, where both feature selection and parameter optimization are applied

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

IMPLEMENTASI ALGORITMA K-Modes UNTUK MENENTUKAN STRATEGI MARKETING STMIK BUDI DARMA

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Ewin Karman Nduru ◽  
Efori Buulolo ◽  
Pristiwanto Pristiwanto
Keyword(s):  
High School ◽  
Data Mining ◽  
High School Students ◽  
Marketing Strategy ◽  
Process Data ◽  
School Students ◽  
The North ◽  
The Government ◽  
Using Data ◽  
North Sumatra

Universities or institutions that operate in North Sumatra are very many, therefore, of course, competition in accepting new students is very tight, universities or institutions do certain ways or steps to be able to compete with other campuses in gaining interest from community or high school students who will continue their studies to a higher level. STMIK BUDI DARMA Medan (College of Information and Computer Management), is the first computer high school in Medan which was established on March 1, 1996 and received approval from the government through the Minister of Education and Culture, on July 23, 1996 with operating license number 48 / D / O / 1996, in promoting the campus, the team usually formed a promotion team to various regions in the North Sumatra Region to provide information to the community. Students who have learned in this campus are quite a lot who come from various regions in North Sumatra, from this point the need to process data from students who are active in college to be processed using data mining to achieve a target, one method that can be used in data mining, namely the ¬K-Modes clustering (grouping) algorithm. This method is a grouping of student data that will be a help to campus students in promoting, using the K-Modes algorithm is expected to help and become a reference for marketing in determining the marketing strategy STMIK Budi Darma MedanKeywords: STMIK Budi Darma, Marketing Strategy, K-Modes Algorithm.

scholarly journals Student Performance Prediction with Optimum Multilabel Ensemble Model

2021 ◽  
Vol 30 (1) ◽  
pp. 511-523
Author(s):  
Ephrem Admasu Yekun ◽  
Abrahaley Teklay Haile
Keyword(s):  
High School Students ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Transformation Method ◽  
Classification Task ◽  
Support Vector ◽  
School Students ◽  
K Nearest Neighbors ◽  
Classifier Chains

Abstract One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.

scholarly journals High school students’ academic performance associated with psychological aspects, body practices and physical activity

2020 ◽  
Vol 31 (1) ◽  
Author(s):  
Guilherme da Silva Gasparotto ◽  
Aline Bichels ◽  
Thaynara do Prado Szeremeta ◽  
Gislaine Cristina Vagetti ◽  
Valdomiro de Oliveira
Keyword(s):  
Physical Activity ◽  
High School ◽  
Academic Performance ◽  
High School Students ◽  
Extracurricular Activities ◽  
Self Concept ◽  
School Students ◽  
The Mean ◽  
Body Practices ◽  
Academic Self Efficacy

The objective of this study was to verify the association of psychological factors and body practices with the academic performance of high school students. A sample of 330 students participated, made up of 167 girls and 163 boys. Likert scale instruments were used for collecting information on self-concept, and on general and academic self-efficacy. Time spent on moderate to vigorous physical activity was recorded, and so was participation in several types of body practices, such as sports, dances, martial arts, performing arts, and systematic physical exercises. Academic achievement was referred to from the students' grades on regular subjects. Linear regression analysis was used for verifying the association of independent variables with academic performance. The adjusted regression model explains between 7% and 36% of academic performance variance, whereas Self-Concept explains academic performance on six of the twelve subjects, and the mean of the grades, with Beta values between 0.13 (p = 0.02) for Sociology and 0.28 (p <0.01) for Mathematics. Academic self-efficacy explained performance on eleven subjects and the mean of the grades, with Beta values between 0.21 (p <0.01) for Physical Education and Philosophy, and 0.44 (p <0.01) for Biology. Participation in extracurricular activities involving body practices explained academic performance on six subjects and the mean of the grades, with Beta values between 0.14 (p = 0.02) for Sociology and 0.31 (p <0.01) for Arts. The studied psychological variables and participation in projects concerning body practices during extracurricular activities correlated with academic achievement as to several school subjects, and with the mean of the grades.

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