scholarly journals Enhancement of student performance prediction using modified K-nearest neighbor

2020 ◽  
Vol 18 (4) ◽  
pp. 1777
Author(s):  
Saja Taha Ahmed ◽  
Rafah Al-Hamdani ◽  
Muayad Sadik Croock
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Nearest Neighbor ◽  
K Nearest Neighbor

IMPLEMENTATION OF GAIN RATIO AND K-NEAREST NEIGHBOR FOR CLASSIFICATION OF STUDENT PERFORMANCE

2020 ◽  
Vol 16 (1) ◽  
pp. 19-24
Author(s):  
Tyas Setiyorini ◽  
Rizky Tri Asmono
Keyword(s):  
Student Performance ◽  
Nearest Neighbor ◽  
Educational Data Mining ◽  
K Nearest Neighbor ◽  
High Dimensions ◽  
Gain Ratio ◽  
Average Accuracy ◽  
Predicting Student Performance ◽  
Work Done

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.

scholarly journals Convolutional Recurrent Neural Networks with a Self-Attention Mechanism for Personnel Performance Prediction

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1227 ◽  
Author(s):  
Xia Xue ◽  
Jun Feng ◽  
Yi Gao ◽  
Meng Liu ◽  
Wenyu Zhang ◽  
...  
Keyword(s):  
Performance Prediction ◽  
Nearest Neighbor ◽  
Contextual Information ◽  
Research Area ◽  
Attention Mechanism ◽  
K Nearest Neighbor ◽  
Technology Industry ◽  
Knn Classifier ◽  
Significant Performance ◽  
Important Research Area

Personnel performance is important for the high-technology industry to ensure its core competitive advantages are present. Therefore, predicting personnel performance is an important research area in human resource management (HRM). In this paper, to improve prediction performance, we propose a novel framework for personnel performance prediction to help decision-makers to forecast future personnel performance and recruit the best suitable talents. Firstly, a hybrid convolutional recurrent neural network (CRNN) model based on self-attention mechanism is presented, which can automatically learn discriminative features and capture global contextual information from personnel performance data. Moreover, we treat the prediction problem as a classification task. Then, the k-nearest neighbor (KNN) classifier was used to predict personnel performance. The proposed framework is applied to a real case of personnel performance prediction. The experimental results demonstrate that the presented approach achieves significant performance improvement for personnel performance compared to existing methods.

scholarly journals Educational Data Mining in Predicting Student Final Grades

2021 ◽  
Vol 10 (1) ◽  
pp. 366-371
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Student Performance ◽  
Parameter Optimization ◽  
Nearest Neighbor ◽  
Educational Data Mining ◽  
Real Data ◽  
K Nearest Neighbor ◽  
Student’S Performance ◽  
Student Grades

Educational data mining is a field of science that extracts knowledge from educational data. One of its implementations is to predict student performance, it helps teachers to identify students that need more support. This can potentially increase learning effectiveness and elevate overall student’s grades. There are various algorithms and optimization solutions to predict student’s performance. In this paper, we use real data from one of Indonesia’s public junior high schools to compare naive bayes, decision tree, and k-nearest neighbor algorithms and implement feature selection and parameter optimization to identify which combination of algorithm and optimization can achieve the highest accuracy in predicting student grades, i.e. 7-grade classification.The results show that k-NN achieves the highest accuracy with 77.36%, where both feature selection and parameter optimization are applied

scholarly journals COMPARISON OF DATA MINING CLASSIFICATION ALGORITHM FOR PREDICTING THE PERFORMANCE OF HIGH SCHOOL STUDENTS

2020 ◽  
Vol 17 (1) ◽  
pp. 22-30
Author(s):  
Tiska Pattiasina ◽  
Didi Rosiyadi
Keyword(s):  
Data Mining ◽  
High School Students ◽  
Student Performance ◽  
Extracurricular Activities ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
Added Value ◽  
K Nearest Neighbor ◽  
School Students ◽  
Pocket Money

Data Mining is a series of processes to explore added value in the form of unknown information manually from the database. In the world of data mining education can be used to obtain information about student performance. In this study the researchers took research samples from class XI (eleven) students at SMAN 3 Ambon by classifying student performance based on thirteen attributes, namely: age, sex, school organization, extracurricular activities, pocket money, duration of study at home, duration of social media, online game duration, attendance, illness, permits, semester 1 and semester 2 grades. Using the KDD (Knowledge Discovery Database) method and classification algorithm that will be used, namely, decision tree, Naïve Bayes and K-Nearest Neighbor. And then do the test using k-fold cross validation.

scholarly journals PENERAPAN METODE K-NEAREST NEIGHBOR DAN GINI INDEX PADA KLASIFIKASI KINERJA SISWA

2019 ◽  
Vol 16 (2) ◽  
pp. 121-126
Author(s):  
Tyas Setiyorini ◽  
Rizky Tri Asmono
Keyword(s):  
Student Performance ◽  
Gini Index ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Academic Values ◽  
Simple Method ◽  
Learning And Teaching ◽  
Student Academic Performance ◽  
Average Accuracy ◽  
Predicting Student Performance

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.

scholarly journals Applicability of Traditional Classification Techniques on Educational Data

2020 ◽  
Vol 8 (6) ◽  
pp. 1672-1677
Keyword(s):  
Data Mining ◽  
Pilot Study ◽  
Decision Tree ◽  
Student Performance ◽  
Performance Prediction ◽  
Nearest Neighbor ◽  
Class Imbalance ◽  
Classification Techniques ◽  
Statistical Measures ◽  
Traditional Classification

Student performance prediction and analysis is an essential part of higher educational institutions, which helps in overall betterment of the educational system. Various traditional Data Mining (DM) techniques like Regression, Classification, etc. are prominently utilized for analyzing the data coming from educational settings. The usage of DM in the area of academics is called Educational Data Mining (EDM). The current pilot study aims to determine the applicability of these standalone classification techniques namely; Decision Tree, BayesNet, Nearest Neighbor, Rule-Based, and Random Forest (RF). The present pilot study uses the WEKA tool to implement traditional classification techniques on a standard dataset containing student academic information and background. The paper also implements feature selection to identify the high influential features from the dataset. It helps in reducing the dimensionality of the dataset as well as enhancing the accuracy of the classifier. The results of classifiers are compared on basis of standard statistical measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Kappa, etc. The results show the applicability of classification algorithms for student performance prediction which will help under-achievers and struggling students to improve. It is found the output that, J48 algorithm of the Decision tree gave the best results. Further, it is deduced from the comparative analysis that individual classifiers give different accuracy on the same dataset due to class imbalance in a multiclass dataset.

Komparasi Metode Decision Tree, Naive Bayes Dan K-Nearest Neighbor Pada Klasifikasi Kinerja Siswa

2018 ◽  
Vol 15 (2) ◽  
pp. 85
Author(s):  
Tyas Setiyorini ◽  
Rizky Tri Asmono
Keyword(s):  
Decision Tree ◽  
Student Performance ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor

Dalam pendidikan, kinerja siswa merupakan bagian yang penting. Untuk mencapai kinerja siswa yang baik dan berkualitas dibutuhkan analisa atau evaluasi terhadap faktor-faktor yang mempengaruhi kinerja siswa.  Metode yang dilakukan masih menggunakan cara evaluasi berdasarkan hanya penilaian pendidik terhadap informasi kemajuan pembelajaran siswa. Cara tersebut tidak efektif karena informasi kemajuan pembelajaran siswa semacam itu tidak cukup untuk membentuk indikator dalam mengevaluasi kinerja siswa serta membantu para siswa dan pendidik untuk melakukan perbaikan dalam pembelajaran dan pengajaran. Penelitian-penelititan terdahulu telah dilakukan tetapi belum diketahui metode mana yang terbaik dalam mengklasifikasikan kinerja siswa. Pada penelitian ini dilakukan komparasi metode Decision Tree, Naive Bayes dan K-Nearest Neighbor dengan menggunakan dataset student performance. Dengan menggunakan metode Decision Tree didapatkan akurasi sebesar 78,85, dengan menggunakan metode Naive Bayes didapatkan akurasi sebesar 77,69 dan dengan menggunakan metode K-Nearest Neighbor didapatkan akurasi sebesar 79,31. Setelah dikomparasi hasil tersebut menunjukkan bahwa dengan menggunakan metode K-Nearest Neighbor didapatkan akurasi tertinggi. Hal tersebut menyimpulkan bahwa metode K-Nearest Neighbor memiliki kinerja yang lebih baik dibanding metode Decision Tree dan Naive Bayes.

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