Garuda - Garba Rujukan Digital

The power of social networking sites: Student involvement toward education M. Norhailawati; Lina Handayani; HU Kalsum; Zainuri Saringat; A. Aidahani; SH Bakri; Rully Charitas Indra Prahmana
International Journal of Evaluation and Research in Education (IJERE) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Social networking web sites is not a new medium for users. The growth of this media helps students communicate and socialize with their friends or followers and used for education. The retention number of students is an issue or real problem in higher educational institutions since it is related to institution overall performance. This research focused on how students’ involvement in social network web site for educational purposes can have a positive effect to their studies and can help institution to retain number of students in the institution. During the research study, students did give full cooperation and involvement in the group that created and dedicated to their collaborative learning. Facebook is the social networking website that used to support learning. The research method applied was survey and the questionnaires distributed to 40 students who got probations result (the Cumulative Grade Point Average (CGPA) was below 2.00) from various academic programs. The result showed that social network website plays an important role in education. The finding also showed that the probation students improve their CGPA and status, while the institution able to maintain their student retention number.

Comparative analysis of classification algorithms for chronic kidney disease diagnosis Zainuri Saringat; Aida Mustapha; R. D. Rohmat Saedudin; Noor Azah Samsudin
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Chronic Kidney Disease (CKD) is one of the leading cause of death contributed by other illnesses such as diabetes, hypertension, lupus, anemia or weak bones that lead to bone fractures. Early prediction of CKD is important in order to contain the disesase. However, instead of predicting the severity of CKD, the objective of this paper is to predict the diagnosis of CKD based on the symptoms or attributes observed in a particular case, whether the stage is acute or chronic. To achieve this, a classification model is proposed to label stage of severity for kidney diseases patients. The experiments then investigated the performance of the proposed classification model based on eight supervised classification algorithms, which are ZeroR, Rule Induction, Support Vector Machine, Naïve Bayes, Decision Tree, Decision Stump, k-Nearest Neighbour, and Classification via Regression. The performance of the all classifiers is evaluated based on accuracy, precision, and recall. The results showed that the regression classifier perform best in the kidney diagnostic procedure.

Comparative analysis of classification algorithms for chronic kidney disease diagnosis Zainuri Saringat; Aida Mustapha; R. D. Rohmat Saedudin; Noor Azah Samsudin
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Chronic Kidney Disease (CKD) is one of the leading cause of death contributed by other illnesses such as diabetes, hypertension, lupus, anemia or weak bones that lead to bone fractures. Early prediction of CKD is important in order to contain the disesase. However, instead of predicting the severity of CKD, the objective of this paper is to predict the diagnosis of CKD based on the symptoms or attributes observed in a particular case, whether the stage is acute or chronic. To achieve this, a classification model is proposed to label stage of severity for kidney diseases patients. The experiments then investigated the performance of the proposed classification model based on eight supervised classification algorithms, which are ZeroR, Rule Induction, Support Vector Machine, Naïve Bayes, Decision Tree, Decision Stump, k-Nearest Neighbour, and Classification via Regression. The performance of the all classifiers is evaluated based on accuracy, precision, and recall. The results showed that the regression classifier perform best in the kidney diagnostic procedure.

Comparative analysis of classification algorithms for chronic kidney disease diagnosis Zainuri Saringat; Aida Mustapha; R. D. Rohmat Saedudin; Noor Azah Samsudin
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Chronic Kidney Disease (CKD) is one of the leading cause of death contributed by other illnesses such as diabetes, hypertension, lupus, anemia or weak bones that lead to bone fractures. Early prediction of CKD is important in order to contain the disesase. However, instead of predicting the severity of CKD, the objective of this paper is to predict the diagnosis of CKD based on the symptoms or attributes observed in a particular case, whether the stage is acute or chronic. To achieve this, a classification model is proposed to label stage of severity for kidney diseases patients. The experiments then investigated the performance of the proposed classification model based on eight supervised classification algorithms, which are ZeroR, Rule Induction, Support Vector Machine, Naïve Bayes, Decision Tree, Decision Stump, k-Nearest Neighbour, and Classification via Regression. The performance of the all classifiers is evaluated based on accuracy, precision, and recall. The results showed that the regression classifier perform best in the kidney diagnostic procedure.

A review of object-oriented approach for test case prioritization Umar Farooq; Hannani Aman; Aida Mustapha; Zainuri Saringat
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp429-434

The most essential phase in regression testing is Test Case Prioritization (TCP), with its primary objective to increase the fault detection rate at different stages during testing. Prior to achieving the objective, existing evidence of techniques in TCP must be synthesized and analyzed. At present, fault detection for TCP based on object-oriented features only consider statement, module, and class level. The important features of object-oriented (OO) programming like inheritance and polymorphism have not been fully explored for fault detection in TCP. Such OO concepts are important for test case selection and in turn for ranking the test cases (prioritization). This paper reviews various test case prioritization techniques specific to OO systems. This review is hoped to highlight the importance and usage of TCP in relation to object-oriented software development lifecycle.

Title

Found 5 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 5 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 5 Documents
Search