Determining single tuition fee of higher education in Indonesia: A comparative analysis of data mining classification algorithms

2017 ◽  
Author(s):  
Muhammad Nur Yasir Utomo ◽  
Adhistya Erna Permanasari ◽  
Eddy Tungadi ◽  
Irfan Syamsuddin
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Comparative Analysis ◽  
Classification Algorithms ◽  
Tuition Fee

Detection of Anomalous Transactions in Mobile Payment Systems

2020 ◽  
Vol 1 (2) ◽  
pp. 58-66
Author(s):  
Ibrar Hussain ◽  
Muhammad Asif
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Economic Activity ◽  
Research Study ◽  
Mobile Payment ◽  
Financial Fraud ◽  
Classification Algorithms ◽  
Classification Models ◽  
Payment Systems ◽  
Substantial Impact

Mobile payment systems are providing an opportunity for smartphone users for transferring money to each other with ease. This simple way of transferring through mobile payment systems has great potential for economic activity. However, fraudulent transactions may occur and can have a substantial impact on the economy of a country. Financial fraud and anomalous transactions can cause a loss of billions of dollars annually. Therefore, there is a need to detect anomalous transactions through mobile payment systems to prevent financial fraud. For this research study, a synthetic dataset is generated by using a PAYSIM simulator due to the lack of availability of a realistic dataset. This research study performed experiments on a financial transactional dataset using eight data mining classification algorithms. The performance of classification models was measured by using evaluation metrics: accuracy, precision, F-score, recall, and specificity. A comparative analysis of classification models was also performed based on their performance.

A Comparative Analysis of Classification Algorithms on Students’ Performance

2020 ◽  
Vol 8 (2) ◽  
pp. 20-34
Author(s):  
Nilar Aye
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Comparative Evaluation ◽  
Classification Algorithms ◽  
Large Dataset ◽  
Classification Technique ◽  
Education Data ◽  
Future Events ◽  
Student’S Performance ◽  
Events Data

Recently educational system, many features control a student’s performance. Students should be well stimulated to study their education. Motivation leads to interest, interest leads to success in their lives. Appropriate assessment of abilities encourages the students to do better in their education. Data mining is to find out patterns by analyzing a large dataset and apply those patterns to predict the possibility of the future events. Data mining is a very critical field in educational area and it provides high potential for the schools and universities. In data mining, there are various classification techniques with various levels of accuracy. This paper focuses to make comparative evaluation of four classifiers such as J48, Naive Bayesian, Bayesian Network and Decision Stump by using WEKA tool.  This study is to investigate and identify the best classification technique to analyze and predict the students’ performance of University of Jordan.

scholarly journals A Comparative Analysis of Classification Algorithms on Diverse Datasets

2018 ◽  
Vol 8 (2) ◽  
pp. 2790-2795 ◽  
Author(s):  
M. Alghobiri
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Comparative Analysis ◽  
Nearest Neighbor ◽  
Absolute Error ◽  
Classification Algorithms ◽  
Kappa Statistics ◽  
Data Sets ◽  
Evaluation Measures ◽  
F Measure

Data mining involves the computational process to find patterns from large data sets. Classification, one of the main domains of data mining, involves known structure generalizing to apply to a new dataset and predict its class. There are various classification algorithms being used to classify various data sets. They are based on different methods such as probability, decision tree, neural network, nearest neighbor, boolean and fuzzy logic, kernel-based etc. In this paper, we apply three diverse classification algorithms on ten datasets. The datasets have been selected based on their size and/or number and nature of attributes. Results have been discussed using some performance evaluation measures like precision, accuracy, F-measure, Kappa statistics, mean absolute error, relative absolute error, ROC Area etc. Comparative analysis has been carried out using the performance evaluation measures of accuracy, precision, and F-measure. We specify features and limitations of the classification algorithms for the diverse nature datasets.

Students' Independent Professional Activity in Pedagogical Practice

2010 ◽  
Vol 12 (2) ◽  
pp. 38-58
Author(s):  
Aina Strode
Keyword(s):  
Higher Education ◽  
Comparative Analysis ◽  
Empirical Study ◽  
Qualitative Data ◽  
Academic Staff ◽  
Professional Activity ◽  
Pedagogical Practice ◽  
Structured Interviews ◽  
Criteria And Indicators ◽  
Study Process

Students' Independent Professional Activity in Pedagogical PracticeThe topicality of the research is determined by the need for changes in higher education concerned with implementing the principles of sustainable education. The article focuses on teacher training, highlighting the teacher's profession as an attractive choice of one's career that permits to ensure the development of general and professional skills and an opportunity for new specialists to align with the labour market. The empirical study of students' understanding of their professional activity and of the conditions for its formation is conducted by applying structured interviews (of practice supervisors, students, academic staff); students and experts' questionnaire. Comparative analysis of quantitative and qualitative data and triangulation were used in case studies. As a result, a framework of pedagogical practice organisation has been created in order to form students' independent professional activity. The criteria and indicators of independent professional activity have been formulated and suggestions for designers of study programmes and organisers of the study process have been provided.

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