Classification of Student Academic Performance using Fuzzy Soft Set

Yanto, Iwan Tri Riyadi; Sutoyo, Edi; Rahman, Arif; Hidayat, Rahmat; Ramli, Azizul Azhar; Fudzee, Mohd Farhan Md

doi:10.1109/icosta48221.2020.1570606632

Cited by 4 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy was 80% when assessing newcomer students. Moreover, [18] use Fuzzy Soft Set Classification (FSSC) reached up to accuracy results to be able to detect students at risk in the early stages of education. So, the study found that higher education can minimize students not graduating on time or dropout by providing appropriate treatment and designing strategic programs.…”

Section: A Literature Reviewmentioning

confidence: 99%

Case Study: Using Data Mining to Predict Student Performance Based on Demographic Attributes

Binti Muhammad Zahruddin,

Kamarudin,

Mat Jusoh

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

This study predicts student performance at Universiti Pertahanan Nasional Malaysia (UPNM) based on their socio-demographic profile; it also determines how a prediction algorithm can be used to classify the student data for the most significant demographic attributes. The analytical pattern in academic results per batch has been identified using demographic attributes and the student's grades to improve short-term and long-term learning and teaching plans. Understanding the likely outcome of the education process based on predictions can help UPNM lecturers enhance the achievements of the subsequent batch of students by modifying the factors contributing to the prior success. This study identifies and predicts student performance using data mining and classification techniques such as decision trees, neural networks, and k-nearest neighbors. This frequently adopted method comprises data selection and preparation, cleansing, incorporating previous knowledge datasets, and interpreting precise solutions. This study presents the simplified output from each data mining method to facilitate a better understanding of the result and determine the best data mining method. The results show that the critical attributes influencing student performance are gender, age, and student status. The Neural Networks method has the lowest Root of the Mean of the Square of Errors (RMSE) for accuracy measurement. In contrast, the decision tree method has the highest RMSE, which indicates that the decision tree method has a lower performance accuracy. Moreover, the correlation coefficient for the k-nearest neighbor has been recorded as less than one.

show abstract

Section: A Literature Reviewmentioning

confidence: 99%

Case Study: Using Data Mining to Predict Student Performance Based on Demographic Attributes

Binti Muhammad Zahruddin,

Kamarudin,

Mat Jusoh

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…The experiments conducted to explore fuzzy soft set (FSS) at several similarities focus on determining the phishing dataset's classification performance. This paper also describes the basic theory and definitions of fuzzy set (FS), soft set (SS), fuzzy soft set (FSS) [24], Similarity measure, and Classification. In addition, FSS and new related results are presented, and open-ended questions are provided for further investigation.…”

Section: A Rt I Cl E I N F Omentioning

confidence: 99%

Similarity measure fuzzy soft set for phishing detection

Hidayat

Yanto

Ramli

et al. 2021

Int. J. Adv. Intell. Informatics

Self Cite

View full text Add to dashboard Cite

Phishing is a serious web security problem, and the internet fraud technique involves mirroring genuine websites to trick online users into stealing their sensitive information and taking out their personal information, such as bank account information, usernames, credit card, and passwords. Early detection can prevent phishing behavior makes quick protection of personal information. Classification methods can be used to predict this phishing behavior. This paper presents an intelligent classification model for detecting Phishing by redefining a fuzzy soft set (FSS) theory for better computational performance. There are four types of similarity measures: (1) Comparison table, (2) Matching function, (3) Similarity measure, and (4) Distance measure. The experiment showed that the Similarity measure has better performance than the others in accuracy and recall, reached 95.45 % and 99.77 %, respectively. It concludes that FSS similarity measured is more precise than others, and FSS could be a promising approach to avoid phishing activities. This novel method can be implemented in social media software to warn the users as an early warning system. This model can be used for personal or commercial purposes on social media applications to protect sensitive data.

show abstract

“…The multiplicity and complexity of the variables (Yanto, 2020), factors (Yamin, 2016), and economic crises that affect the self-financing units and in response (Ningrum, 2018). Thus, these units moved towards developing their structures (Thejas, 2019), re-engineering their operations (Sari, 2019), and improving their tools and methods to provide more reliability (Mulili, 2020;Trisnaningtias, 2021).…”

Section: Introductionmentioning

confidence: 99%

Case Study: Internal Audit's Contribution to Financial Sustainability in Self-Financing Units

Alhabow,

Hamoudi,

Alshaikhli

2023

RJME

View full text Add to dashboard Cite

The aim of this research is to demonstrate the role of internal audit activity in achieving financial sustainability in self-financing units, and to achieve the objectives of the research and test its hypotheses, we conducted a case study in municipal institutions in Nineveh Governorate. One of the most important findings of the study that the researchers reached was the weak role of internal audit in achieving financial sustainability, the lack of adoption of modern internal audit objectives, and the lack of a clear program for internal audit prepared according to sustainability. The study concluded with a number of recommendations, the most important of which are: Activating the internal audit advisory services with regard to the financial sustainability of the unit, directing its expenditures and maximizing its revenues, and the internal audit should measure the financial sustainability of the financial reporting information through specific quantitative measures.

show abstract

Classification of Student Academic Performance using Fuzzy Soft Set

Cited by 4 publications

References 23 publications

Case Study: Using Data Mining to Predict Student Performance Based on Demographic Attributes

Case Study: Using Data Mining to Predict Student Performance Based on Demographic Attributes

Similarity measure fuzzy soft set for phishing detection

Case Study: Internal Audit's Contribution to Financial Sustainability in Self-Financing Units

Contact Info

Product

Resources

About