A new nearest neighbor classifier via fusing neighborhood information

Asmono²

2020

pilar

Predicting student performance is very useful in analyzing weak students and providing support to students who face difficulties. However, the work done by educators has not been effective enough in identifying factors that affect student performance. The main predictor factor is an informative student academic score, but that alone is not good enough in predicting student performance. Educators utilize Educational Data Mining (EDM) to predict student performance. KK-Nearest Neighbor is often used in classifying student performance because of its simplicity, but the K-Nearest Neighbor has a weakness in terms of the high dimensional features. To overcome these weaknesses, a Gain Ratio is used to reduce the high dimension of features. The experiment has been carried out 10 times with the value of k is 1 to 10 using the student performance dataset. The results of these experiments are obtained an average accuracy of 74.068 with the K-Nearest Neighbor and obtained an average accuracy of 75.105 with the Gain Ratio and K-Nearest Neighbor. The experimental results show that Gain Ratio is able to reduce the high dimensions of features that are a weakness of K-Nearest Neighbor, so the implementation of Gain Ratio and K-Nearest Neighbor can increase the accuracy of the classification of student performance compared to using the K-Nearest Neighbor alone.

Section: K-nearest Neighbormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implementation of Gain Ratio and K-Nearest Neighbor for Classification of Student Performance

Asmono²

2020

pilar

“…K-Nearest Neighbor is an effective, intuitive and simple method (Gou et al, 2014) (Lin et al, 2014). In pattern recognition, the K-Nearest Neighbor algorithm is a non-parametric method that is useful for grouping objects based on close features.…”

Section: K-nearest Neighbormentioning

confidence: 99%

“…The K-Nearest Neighbor classification is a well-known pattern recognition method that has been used extensively in several applications (Cover & Hart, 1967) and has attracted wide interest in the research community (Gou et al, 2014) (Lin, Li, Lin, & Chen, 2014) (Lin et al, 2014). K-Nearest Neighbor is a method that is able to solve classification problems, has significant advantages and often produces competitive results from several other data mining methods (Adeniyi, Wei, & Yongquan, 2016).…”

Section: Introductionmentioning

confidence: 99%

Penerapan Metode K-Nearest Neighbor Dan Gini Index Pada Klasifikasi Kinerja Siswa

Asmono²

2019

techno

Predicting student academic performance is one of the important applications in data mining in education. However, existing work is not enough to identify which factors will affect student performance. Information on academic values or progress on student learning is not enough to be a factor in predicting student performance and helps students and educators to make improvements in learning and teaching. K-Nearest Neighbor is a simple method for classifying student performance, but K-Nearest Neighbor has problems in terms of high feature dimensions. To solve this problem, we need a method of selecting the Gini Index feature in reducing the high feature dimensions. Several experiments were conducted to obtain an optimal architecture and produce accurate classifications. The results of 10 experiments with values of k (1 to 10) in the student performance dataset with the K-Nearest Neighbor method showed the highest average accuracy of 74.068 while the K-Nearest Neighbor and Gini Index methods showed the highest average accuracy of 76.516. From the results of these tests it can be concluded that the Gini Index is able to overcome the problem of high feature dimensions in K-Nearest Neighbor, so the application of the K-Nearest Neighbor and Gini Index can improve the accuracy of student performance classification better than using the K-Nearest Neighbor method.

“…Beberapa penelitian telah dilakukan dalam memprediksi kinerja siswa dengan teknik klasifikasi, seperti K-Nearest Neighbor (Pandey & Taruna, 2016), Regression (Conijn, Snijders, Kleingeld, & Matzat, 2017), Support Vector Machine (Al-Shehri et al, 2017), Decision Tree (Lopez Guarin, Guzman, & Gonzalez, 2015), Naive Bayes (Lopez Guarin et al, 2015), dan Artificial Neural Networks (Alkhasawneh & Hobson, 2011). K-Nearest Neighbor bersifat efektif, intuitif dan sederhana sehingga K-Nearest Neighbor telah menarik minat luas dalam komunitas penelitian (Gou et al, 2014) (Lin, Li, Lin, & Chen, 2014) (Lin et al, 2014). K-Nearest Neighbor adalah salah satu metode yang mampu memecahkan masalah klasifikasi, sering menghasilkan hasil yang kompetitif dan memiliki keuntungan yang signifikan atas beberapa metode penambangan data lainnya (Adeniyi, Wei, & Yongquan, 2016).…”

unclassified

Penerapan Metode K-Nearest Neighbor Dan Information Gain Pada Klasifikasi Kinerja Siswa

Asmono

2019

jitk

Education is a very important problem in the development of a country. One way to reach the level of quality of education is to predict student academic performance. The method used is still using an ineffective way because evaluation is based solely on the educator's assessment of information on the progress of student learning. Information on the progress of student learning is not enough to form indicators in evaluating student performance and helping students and educators to make improvements in learning and teaching. K-Nearest Neighbor is an effective method for classifying student performance, but K-Nearest Neighbor has problems in terms of large vector dimensions. This study aims to predict the academic performance of students using the K-Nearest Neighbor algorithm with the Information Gain feature selection method to reduce vector dimensions. Several experiments were conducted to obtain an optimal architecture and produce accurate classifications. The results of 10 experiments with k values (1 to 10) in the student performance dataset with the K-Nearest Neighbor method showed the largest average accuracy of 74.068 while the K-Nearest Neighbor and Information Gain methods obtained the highest average accuracy of 76.553. From the results of these tests it can be concluded that Information Gain can reduce vector dimensions, so that the application of K-Nearest Neighbor and Information Gain can improve the accuracy of the classification of student performance better than using the K-Nearest Neighbor method.