scholarly journals Penerapan Educational Data Mining Untuk Memprediksi Hasil Belajar Siswa SMAK Ora et Labora

2019 ◽  
Vol 12 (2) ◽  
pp. 73
Author(s):  
Daniel David
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Educational Data Mining ◽  
Internal Assessment

Data mining adalah salah satu alternatif yang bisa dilakukan untuk melakukan penggalian informasi baru dari sejumlah data yang besar. Salah satu aliran data mining adalah Educational Data Mining (EDM). EDM adalah aliran data mining yang bergerak pada bidang pendidikan. Dengan memanfaatkan data-data yang berhubungan dengan pendidikan, proses data mining bisa dilakukan untuk menemukan informasi berguna untuk kemajuan dalam bidang pendidikan. Penelitian ini menggunakan EDM dengan tujuan untuk memanfaatkan data internal assessment dari dari masing-masing siswa sekolah dan melakukan prediksi terhadap hasil ujian akhir nasional siswa tersebut. Data mining ini menggunakan teknik klasifikasi dan metode Decision Tree C4.5. Selain itu akan digunakan juga metode penelitian deskriptif agar bisa memberikan hasil yang lebih akurat. Penelitian ini diharapkan bisa memberikan kontribusi dalam bentuk prediksi hasil ujian akhir nasional sehingga kedepannya bisa digunakan untuk siswa angkatan seterusnya.

scholarly journals Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

2021 ◽  
Vol 10 (3) ◽  
pp. 121-127
Author(s):  
Bareen Haval ◽  
Karwan Jameel Abdulrahman ◽  
Araz Rajab
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Student Performance ◽  
Educational Data Mining ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Data Mining Techniques ◽  
Academic History ◽  
Tree Classifier ◽  
Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

scholarly journals Developed third iterative dichotomizer based on feature decisive values for educational data mining

2020 ◽  
Vol 18 (1) ◽  
pp. 209
Author(s):  
Saja Taha Ahmed ◽  
Rafah Al-Hamdani ◽  
Muayad Sadik Croock
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Decision Tree ◽  
Predictive Analytics ◽  
Educational Data Mining ◽  
Target Class ◽  
Id3 Algorithm ◽  
Feature Weight ◽  
Holdout Validation ◽  
Fold Cross Validation

<p><span>Recently, the decision trees have been adopted among the preeminent utilized classification models. They acquire their fame from their efficiency in predictive analytics, easy to interpret and implicitly perform feature selection. This latter perspective is one of essential significance in Educational Data Mining (EDM), in which selecting the most relevant features has a major impact on classification accuracy enhancement. <br /> The main contribution is to build a new multi-objective decision tree, which can be used for feature selection and classification. The proposed Decisive Decision Tree (DDT) is introduced and constructed based on a decisive feature value as a feature weight related to the target class label. The traditional Iterative Dichotomizer 3 (ID3) algorithm and the proposed DDT are compared using three datasets in terms of some ID3 issues, including logarithmic calculation complexity and multi-values features<em></em>selection. The results indicated that the proposed DDT outperforms the ID3 in the developing time. The accuracy of the classification is improved on the basis of 10-fold cross-validation for all datasets with the highest accuracy achieved by the proposed method is 92% for the student.por dataset and holdout validation for two datasets, i.e. Iraqi and Student-Math. The experiment also shows that the proposed DDT tends to select attributes that are important rather than multi-value. </span></p>

scholarly journals Prediksi Kelulusan Mata Kuliah Menggunakan Hybrid Fuzzy Inference System

2016 ◽  
Vol 2 (2) ◽  
pp. 60
Author(s):  
Abidatul Izzah ◽  
Ratna Widyastuti
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Educational Data Mining ◽  
Fuzzy Rule ◽  
Posttest Score ◽  
Inference System ◽  
Is Implementation ◽  
Attention To Students

AbstrakPerguruan Tinggi merupakan salah satu institusi yang menyimpan data yang sangat informatif jika diolah secara baik. Prediksi kelulusan mahasiswa merupakan kasus di Perguruan Tinggi yang cukup banyak diteliti. Dengan mengetahui prediksi status kelulusan mahasiswa di tengah semester, dosen dapat mengantisipasi atau memberi perhatian khusus pada siswa yang diprediksi tidak lulus. Metode yang digunakan sangat bervariatif termasuk metode Fuzzy Inference System (FIS). Namun dalam implementasinya, proses pembangkitan rule fuzzy sering dilakukan secara random atau berdasarkan pemahaman pakar sehingga tidak merepresentasikan sebaran data. Oleh karena itu, dalam penelitian ini digunakan teknik Decision Tree (DT) untuk membangkitkan rule. Dari uraian tersebut, penelitian bertujuan untuk memprediksi kelulusan mata kuliah menggunakan hybrid FIS dan DT. Data yang digunakan dalam penelitian ini adalah data nilai Posttest, Tugas, Kuis, dan UTS dari 106 mahasiswa Politeknik Kediri pengikut mata kuliah Algoritma dan Struktur Data. Penelitian ini diawali dari membangkitkan 5 rule yang selanjutnya digunakan dalam inferensi. Tahap selanjutnya adalah implementasi FIS dengan tahapan fuzzifikasi, inferensi, dan defuzzifikasi. Hasil yang diperoleh adalah akurasi, sensitivitas, dan spesifisitas  masing-masing adalah 94.33%, 96.55%, dan 84.21%.Kata kunci: Decision Tree, Educational Data Mining, Fuzzy Inference System, Prediksi. AbstractCollege is an institution that holds very informative data if it mined properly. Prediction about student’s graduation is a common case that many discussed. Having the predictions of student’s graduation in the middle semester, lecturer will anticipate or give some special attention to students who would be not passed. The method used to prediction is very varied including Fuzzy Inference System (FIS). However, fuzzy rule process is often generated randomly or based on knowledge experts that not represent the data distribution. Therefore, in this study, we used a Decision Tree (DT) technique for generate the rules. So, the research aims to predict courses graduation using hybrid FIS and DT. Dataset used is the posttest score, tasks score, quizzes score, and middle test score from 106 students of the Polytechnic Kediri who took Algorithms and Data Structures. The research started by generating 5 rules by decision tree. The next is implementation of FIS that consist of fuzzification, inference, and defuzzification. The results show that the classifier give a good result in an accuracy, sensitivity, and specificity respectively was 94.33%, 96.55% and 84.21%.Keywords: Decision Tree, Educational Data Mining, Fuzzy Inference System, Prediction.

scholarly journals Educational data mining using cluster analysis and decision tree technique: A case study

2020 ◽  
Vol 12 ◽  
pp. 184797902090867
Author(s):  
Snježana Križanić
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Decision Tree ◽  
Student Behavior ◽  
Educational Data Mining ◽  
Education Institution ◽  
Data Mining Techniques ◽  
Event Logs ◽  
Hidden Knowledge ◽  
E Learning

Data mining refers to the application of data analysis techniques with the aim of extracting hidden knowledge from data by performing the tasks of pattern recognition and predictive modeling. This article describes the application of data mining techniques on educational data of a higher education institution in Croatia. Data used for the analysis are event logs downloaded from an e-learning environment of a real e-course. Data mining techniques applied for the research are cluster analysis and decision tree. The cluster analysis was performed by organizing collections of patterns into groups based on student behavior similarity in using course materials. Decision tree was the method of interest for generating a representation of decision-making that allowed defining classes of objects for the purpose of deeper analysis about how students learned.

scholarly journals An Analysis on Student Academic Performance by Using Decision Tree Models

2018 ◽  
pp. 615-620
Author(s):  
Jastini Mohd. Jamil ◽  
Nurul Farahin Mohd Pauzi ◽  
Izwan Nizal Mohd. Shahara Nee
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Decision Tree ◽  
Large Volume ◽  
Educational Data Mining ◽  
Data Mining Techniques ◽  
Student Academic Performance ◽  
Tree Models ◽  
Student’S Performance ◽  
Using Data

Large volume of educational data has led to more challenging in predicting student’s performance. In Malaysia currently, study about the performance of students in Malaysia institutions is very little being addressed. The previous studies are still insufficient to identify what factors contribute to student’s achievements and lack of investigations on exploring pattern of student’s behaviour that affecting their academic performance within Malaysia context. Therefore, predicting student’s academic performance by using decision trees is proposed to improve student’s achievements more effectively. The main objective of this paper is to provide an overview on predicting student’s academic performance using by using data mining techniques. This paper also focuses on identifying the pattern of student’s behaviour and the most important attributes that impact to the student’s achievement. By using educational data mining techniques, the students, lecturers and academic institution are able to have a better understanding on the student’s achievement.

scholarly journals Academic Decision Support System for Choosing Information Systems Sub Majors Programs using Decision Tree Algorithm

2019 ◽  
Vol 5 (1) ◽  
pp. 57
Author(s):  
Cut Fiarni ◽  
Evasaria M. Sipayung ◽  
Prischilia B.T. Tumundo
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Educational Data Mining ◽  
Educational Process ◽  
Student Interest ◽  
The Impact

Background: Educational data mining is an emerging trend, especially in today Big Data Era. Numerous method and technique already been implemented in order  to improve its process to gain better understanding of the educational process and to extract knowledge from various related data, but the implementation of these methods into Decision support system (DSS) application still limited, especially regarding help to choose university sub majors .Objective: To design an academic decision support system (DSS) by adopting Theory of Reasoned Action (TRA) concept and using Data Mining as a factor analytic apporach to extract rules for its knowledge model.Methods: We implemented factor analysis method and decision tree method  of C.45 to produce rules of the impact course of the sub- majors and the job interest as the basic rules of the DSS.Results: The proposed academic decision support system able to give sub majors recommendations in accordance with student interest and competence, with 79.03% of precision and 61.11% of recall. Moreover, the system also has a dashboard feature that shows the information about the statistic of students in each sub majors.Conclusion: C.45 algorithm and factor analysis are suitable to build a knowledge model for Academic Decision Support System for Choosing Information System Sub Majors Bachelor Programs. This system could also help the academic adviser on monitoring and make decision accordance with that academic information

Predicting Students Performance Based on Their Academic Profile

2020 ◽  
Vol 8 (2) ◽  
pp. 23-39
Author(s):  
Hadi Khalilia ◽  
Thaer Sammar ◽  
Yazeed Sleet
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
High Speed ◽  
Information Gain ◽  
Educational Data Mining ◽  
Computer Engineering ◽  
Use Of Data ◽  
Important Field ◽  
Index Measure ◽  
The University

Data mining is an important field; it has been widely used in different domains. One of the fields that make use of data mining is Educational Data Mining. In this study, we apply machine learning models on data obtained from Palestine Technical University-Kadoorie (PTUK) in Tulkarm for students in the department of computer engineering and applied computing. Students in both fields study the same major courses; C++ and Java. Therefore, we focused on these courses to predict student’s performance. The goal of our study is predicting students’ performance measured by (GPA) in the major. There are many techniques that are used in the educational data mining field. We applied three models on the obtained data which have been commonly used in the educational data mining field; the decision tree with information gain measure, the decision tree with Gini index measure, and the naive Bayes model. We used these models in our work because they are efficient and they have a high speed in data classification, and prediction. The results suggest that the decision tree with information gain measure outperforms other models with 0.66 accuracy. We had a deeper look on key features that we train our models; precisely, their branch of study at school, field of study in the University, and whether or not the students have a scholarship. These features have an influence on the prediction. For example, the accuracy of the decision tree with information gain measure increases to 0.71 when applied on the subset of students who studied in the scientific branch at high school. This study is important for both the students and the higher management of PTUK. The university will be able to do some predictions on the performance of the students. In the carried experiments, the prediction of the model was inline with the actual expectation.

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