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Comparisons between artificial neural networks and fuzzy logic models in forecasting general examinations results

机译：人工神经网络与模糊逻辑模型在一般考试成绩预测中的比较

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MARA Junior Science College (MRSM) Lenggong is one of the educational institutes under Majlis Amanah Rakyat (MARA). Based on the current academic performance and selected criteria of 6A's in the Penilaian Menengah Rendah (PMR, now it is known as PT3), rationally there should be no reason for the failure to achieve excellent results in the Sijil Pelajaran Malaysia (SPM). However, every time the results are announced, the average school achievement grade (GPS) does not meet the performance goals of an average grade of 1.00 for PMR and below 2.00 for SPM, even though it has been in operation for 10 years. Therefore, this research aimed at identifying the influencing factors that affected the students' academic performance. Early prediction is one of the strategies performed in order to improve the students' performance. Neural network and fuzzy logic models are used to realize the accurate prediction based on three factors namely demography, academic and co-curricular activities, including a combination of all three factors. Demography, academic and co-curricular information for the year 2008 to 2010 SPM candidates of MRSM Lenggong are the data sample used. It can be concluded that the prediction outcome using the neural network model shows that the academic factor influences the students' academic performance with the prediction accuracy around 93.65%. Meanwhile, the fuzzy logic model gives an opposite result, where the students' academic performance has also been influenced by the demography factor with an accuracy of 87.00%. Although different techniques yield different results, it is undeniable that the combination of demography and academic factors establishes a solid outcome in identifying the students' present and future academic performances.
机译：MARA初级科学学院（MRSM）Lenggong是Majlis Amanah Rakyat（MARA）旗下的教育机构之一。根据目前的学术表现和Penilaian Menengah Rendah（PMR，现称为PT3）中6A的选定标准，在合理的情况下，在马来西亚Sijil Pelajaran（SPM）中没有理由不能取得优异成绩。但是，每次公布结果时，即使已经运行了10年，平均学校成绩等级（GPS）都无法达到PMR平均等级1.00和SPM低于2.00的绩效目标。因此，本研究旨在确定影响学生学习成绩的影响因素。早期预测是为了提高学生表现而采取的策略之一。神经网络和模糊逻辑模型用于基于人口统计学，学术活动和课外活动这三个因素，包括所有三个因素的组合，来实现准确的预测。所使用的数据样本是MRSM Lenggong的2008年至2010年SPM候选人的人口统计学，学术和辅助课程信息。可以得出结论，使用神经网络模型的预测结果表明，学术因素影响学生的学习成绩，其预测准确性约为93.65％。同时，模糊逻辑模型给出了相反的结果，即学生的学业成绩也受到人口统计学因素的影响，准确性为87.00％。尽管不同的技术会产生不同的结果，但不可否认的是，人口统计学和学术因素的结合在确定学生目前和将来的学业成绩方面建立了坚实的成果。

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