MARC details
| 000 -LEADER |
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03592nam a2200253 a 4500 |
| 001 - CONTROL NUMBER |
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vtls000067530 |
| 003 - CONTROL NUMBER IDENTIFIER |
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KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251114204524.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
121207t2012 my a f m 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0001966(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
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| Level of rules in bibliographic description |
201905131546 |
| Level of effort used to assign nonsubject heading access points |
yusri |
| Level of effort used to assign subject headings |
201710161548 |
| Level of effort used to assign classification |
aishah |
| Level of effort used to assign subject headings |
201212071649 |
| Level of effort used to assign classification |
Fida |
| -- |
201212071613 |
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Fida |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMP |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
QA76.9.D343 S25 2012 rs Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Sajadin Sembiring |
| 245 13 - TITLE STATEMENT |
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| Title |
An application of predicting student performance using kernel k-means and smooth support vector machine / |
| Statement of responsibility, etc. |
Sajadin Sembiring |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2012 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiv, 123 p. : |
| Other physical details |
ill. ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Master of Computer Science) -- Universiti Malaysia Pahang - 2012 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Bibliography : p. 88-100 |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
This thesis presents the model of predicting student academic performances inHigher Learning Institution (HLI).The prediction ofstudentssuccessfulis one of the most vital issues inHLI.In the previous work, thereare many methodsproposed topredictthe performanceof students such as Scholastic Aptitude Test (SAT) or American College Test (ACT), Intelligent Test, Fuzzy Set Theory, Neural Network, Decision Tree and Naïve Bayes.However, thefactremainsfound ina variety of debateamongeducators inhigher learning institution, especially those relatedto predictorvariablesthatused and the resulting level of prediction accuracy.This shown that the rule model in predicting student performanceisstilla gapand it is urgent for educators to obtain a more accurate prediction results.The objective of thisstudyis to create a rule model in predicting of students performance based on their psychometric factors. In this study, psychometric factors used as predictor variables, thereare Interest, Study Behavior, Engaged Time, Believe, and Family Support.The rulemodel developed using Kernel K-means Clustering and Smooth Support Vector MachineClassification.Both of these techniquesbased on kernel methodsand relativelynew algorithms of data mining techniques, recently received increasingly popularity in machine learning community. These techniques successfullyapplied in processing large amounts of data, especially on high dimensional data that are nonlinearly separable. The data collection from student academic databases and surveyed the psychometric factors of undergraduatestudentin semester 3 sessions 2007/2008 at Universiti Malaysia Pahang.Theresultof this study indicatesa positive correlation between the proposed predictor variables and the students performance.These predictor variables contributesignificantly in increasing or decreasing student performance that is equalto52.2%(R2=0.522).The studyalsofound the cluster model of students based on their performance. Eachmember of the clusters labeledwith their performance index to describe the current condition of student performance.The prediction accuracy of predicting modelproposed have thelowest accuracy 61%(R2= 0.61)in predicting Good performance indexand thehighest accuracy 93.67% (R2= 0.9367)in predicting Poor Performance index. This studyshowedthat the kernel methodhasa capabilityas data mining technique on educational data mining. The results of this studyaresuitableto beusedinmonitoringthe progression of students performancesemester by semesterand supportedthe decision making process by decision makerinHLI. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Data mining |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="http://ecollib.ump.edu.my/3676/">http://ecollib.ump.edu.my/3676/</a> |
| Public note |
Library access only |