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.
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.
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.
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.
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