scholarly journals Implementation of the C4.5 Algorithm to Predict Student Achievement at SMK Negeri 6 Surakarta

2020 ◽  
Vol 4 (2) ◽  
pp. 51
Author(s):  
Giovanni Anggiesta Putri ◽  
Dwi Maryono ◽  
Febri Liantoni
Keyword(s):  
Data Mining ◽  
Student Achievement ◽  
Educational Data Mining ◽  
Special Treatment ◽  
Research Use ◽  
Good Prediction ◽  
Data Mining Algorithm ◽  
Multiple Data ◽  
C4.5 Algorithm ◽  
Knowledge Discovery In Database

Data mining is a knowledge used to get information from multiple data. C.45 Algorithm is one of data mining algorithm to classify data to many categories. Implementation of data mining not only could be used in industrial section but it could be used to in educational section (educational data mining) to help teacher and student improve their learning quality. This research purposed to know the implementation of data mining to predict student achievement from many factors could be effected . The research use Knowledge Discovery in Database method and it would be analyzed by accuration calculated from classify model that be form. Result of the research is the rules that formed by the decision tree and it could predict student achievement . Teacher could use it to give special treatment to student who got not good prediction.

scholarly journals Educational Data Mining for Student Learning Pattern Analysis using Clustering Algorithms

2020 ◽  
Vol 9 (6) ◽  
pp. 481-488
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Data Mining Algorithm ◽  
Learning Behavior ◽  
Study Program ◽  
Data Mining Algorithms ◽  
Students First ◽  
And Performance

The exponential increase in universities’ electronic data creates the need to derive some useful information from these massive amounts of data. The progression in the data mining field causes it conceivable to educational data to improve the nature of educational processes. This study, thus, uses data mining methods to study the learning behavior and performance of university students. It focused on two aspects of the performance of the students. First, predicting students' learning behavior at the end of a complete year of the study program. Second, predict student performance with the help of the data model proposed by this study. Finally, provide course material recommendations using the data mining algorithm. Three data mining algorithms were considered which are K-Means, FCM, and KFCM., and maximum accuracy of 90.22% was achieved by KFCM. The study indicates that in terms of time and memory usages K-means algorithm give better results. This creates an opportunity for identifying students that may graduate with poor results or may not graduate at all, so early intercession might be possible.

scholarly journals Application of Data Mining in Determining Social Assistance Recipients With C4.5 Algorithm in Maligas Mountain District

2021 ◽  
Vol 10 (3) ◽  
pp. 88-99
Author(s):  
Rika Nur Adiha ◽  
Sundari Retno Andani ◽  
Widodo Saputra
Keyword(s):  
Data Mining ◽  
Local Government ◽  
Selection Process ◽  
Social Assistance ◽  
Data Mining Algorithm ◽  
Assistance Program ◽  
District Office ◽  
The Social ◽  
C4.5 Algorithm ◽  
Aid Programs

The Gunung Maligas District Office is a government agency tasked with running a government program, namely the Social Assistance Receipt program, to run the social assistance program, many residents complain that they do not receive assistance, while some residents who are considered capable actually get assistance, where each aid program is have different criteria in determining the recipient. Due to the large number of existing aid programs with different criteria in determining the acceptance of the aid program, of course, local government staff will have difficulty in conducting the selection process. So we need a system that is able to help local government staff to more easily determine the recipients of the social assistance. Based on the historical data of beneficiaries, recommendations for the classification of beneficiaries can be made that will assist government staff. Classification can be done using the C4.5 algorithm. In this study, it has parameters, namely, occupation, income, housing conditions and number of dependents. By applying the C4.5 data mining algorithm, it is hoped that it will make it easier and faster for government staff to determine the recipients of social assistance at the Gunung Maligas District Office.

scholarly journals Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs

2019 ◽  
Vol 9 (24) ◽  
pp. 5539 ◽  
Author(s):  
Shaojie Qu ◽  
Kan Li ◽  
Bo Wu ◽  
Shuhui Zhang ◽  
Yongchao Wang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Student Achievement ◽  
Short Term Memory ◽  
Online Courses ◽  
Educational Data Mining ◽  
Prediction Method ◽  
Learning Processes ◽  
Achievement Prediction ◽  
Temporal Learning

With the development of data mining technology, educational data mining (EDM) has gained increasing amounts of attention. Research on massive open online courses (MOOCs) is an important area of EDM. Previous studies found that assignment-related behaviors in MOOCs (such as the completed number of assignments) can affect student achievement. However, these methods cannot fully reflect students’ learning processes and affect the accuracy of prediction. In the present paper, we consider the temporal learning behaviors of students to propose a student achievement prediction method for MOOCs. First, a multi-layer long short-term memory (LSTM) neural network is employed to reflect students’ learning processes. Second, a discriminative sequential pattern (DSP) mining-based pattern adapter is proposed to obtain the behavior patterns of students and enhance the significance of critical information. Third, a framework is constructed with an attention mechanism that includes data pre-processing, pattern adaptation, and the LSTM neural network to predict student achievement. In the experiments, we collect data from a C programming course from the year 2012 and extract assignment-related features. The experimental results reveal that this method achieves an accuracy rate of 91% and a recall of 94%.

scholarly journals DATA MINING ALGORITHM C4.5 CLASSIFICATION DETERMINATION CREDIT ELIGIBILITY FOR JAYA BERSAMA COOPERATIVES (KORJABE)

JURTEKSI ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 59-68
Author(s):  
Christnatalis Christnatalis ◽  
Roni Rayandi Saragih ◽  
Bobby Christianto Tambunan
Keyword(s):  
Data Mining ◽  
Test Data ◽  
Selection Method ◽  
Training Data ◽  
Classification Error ◽  
Data Mining Algorithm ◽  
Mining Method ◽  
Data Mining Method ◽  
Mining Algorithm ◽  
C4.5 Algorithm

Abstract: This study uses the C4.5 classification algorithm to determine creditworthness, clasification aims to divide the assigned object intoin a number of categories called classes. In this study, the authorusing data mining and C4.5 algorithm as the selection method. The criteria used are loan installments, prospective customer income, termloan time, status of prospective customers. This study resulted in a classification modeldecision tree using the C4.5 algorithm is included in the Excellent category Classification with an accuracy value of 98.33% and a classification error of 1.67%,so that this study uses 70% training data and 30% test data. From resultthe calculation obtained shows that the C4.5 algorithm can be usedto determine the feasibility of granting credit to Koperasi Jaya customers Together (KORJABE).            Keywords: Analysis, Credit Eligibility, C4 Algorithm, Data Mining, Method  Abstrak: Penelitian ini menggunakan metode Algoritma C4.5 klasifikasi untuk menentukan kelayakan kredit, klasifikasi bertujuan untuk membagi objek yang ditetapkan ke dalam satu  nomor kategori yang disebut kelas. Dalam penelitian ini, penulis menggunankan data mining dan algoritma C4.5 sebagai metode pemilihannya. Kriteria yang digunakan yaitu , angsuran  pinjaman,penghasilan calon nasabah,jangka waktu pinjaman ,status calon nasabah. Penelitian ini menghasillkan model klasifikasi pohon keputusan menggunakan algoritma C4.5 termasuk dalam kategori Excellent Classification dengan nilai akurasi sebesar 98,33% dan klasifikasi eror 1,67%, sehingga penelitian ini kan menggunakan data latih 70% dan data uji 30%. Dari hasil perhitungan yang diperoleh menunjukan bahwa algoritma C4.5 dapat digunakan untuk menen tukan kelayakan pemberian kredit kepada nasabah Koperasi Jaya Bersama (KORJABE). Kata kunci: Algoritma C4.5, Analisis,  Data Mining, Kelayakan Kredit, Metode

scholarly journals Penerapan Data Mining Untuk Prediksi Penjualan Wallpaper Menggunakan Algoritma C4.5

2018 ◽  
Author(s):  
Juna Eska
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Added Value ◽  
Data Mining Algorithm ◽  
Material Quality ◽  
Mining Algorithm ◽  
C4.5 Algorithm ◽  
Using Data ◽  
Wall Decoration ◽  
Price Algorithm

Wallpaper wallpaper or wallpaper wall is a wall decoration with a variety of motifs and colors. Wallpaper isused to change the appearance of a space to be more beautiful and has added value. Plain house walls tend tomake the occupants of the house feel bored because of the monotonous wall appearance. For that, having theinitiative to design the wall of the house with wallpaper into a bright idea that should be tried. Coloring thewalls of the house with wallpaper can add a beautiful impression on a room, so the room looks more expressive.Various motifs, colors, and wallpaper styles can be selected. Therefore, the seller must be more careful toprovide wallpaper which will be a lot of devotees, so it is necessary to recommend the type of wallpaper typeusing Classification method is done using data mining algorithm C4.5. data required is the best wallpaperbrand data, color, motif, material quality, size, and price. Algorithm C4.5 is a data classification algorithm oftype of decision tree. The decision tree The C4.5 algorithm is constructed with several stages including theselection of attributes as roots, creating branches for each value and dividing instances in branches. Thesestages will be repeated for each branch until all the cases on the branch have the same class. From thecompletion of the decision tree there will be some rules.

scholarly journals Data Mining Power Determination Nurses Sultan Sulaiman Hospital With C4.5 Algorithm

2020 ◽  
Vol 2 (1) ◽  
pp. 77-82
Author(s):  
Wenika Hidayati ◽  
Paska Marto Hasugian
Keyword(s):  
Data Mining ◽  
Health Services ◽  
Decision Tree ◽  
Regional Hospital ◽  
Data Mining Algorithm ◽  
Hospital Data ◽  
Mining Algorithm ◽  
C4.5 Algorithm ◽  
Existing Data

The hospital is an agency engaged in health services in the which there are a number of special professions that can provide health services to the community items, namely doctors, Midwives and nurses and other professes. In this discussion, Arise and problems that can be raised into case studies to find out the results and information of each process in data mining Carried out with the C4.5 algorithm items, namely nurses. However, there are Several obstacles to Determine the nurses who will be declared passed or failed and accepted to work and can provide health services to the community, especially Patients who come for treatment. Therefore we need a method to identify nurses in a hospital. Data Mining with c4. 5 Algorithm can be used to the make predictions or classifications of nurses who are eligible to perform health services in hospitals by making decision trees based on existing data. This study aims to apply the data mining algorithm C4.5 in Determining nurses based on four attributes of used items, namely Accreditation, GPA, Age, and the value of each criterion has been determined in advance. The results of the study in the form of a decision tree Obtained from the data mining process with the C4.5 algorithm will provide information on the determination of nurses in the Sultan Sulaiman Regional Hospital.

scholarly journals DATA MINING IN EDUCATION FOR STUDENTS ACADEMIC PERFORMANCE: A SYSTEMATIC REVIEW

2014 ◽  
Vol 13 (9) ◽  
pp. 5020-5028
Author(s):  
Anurag Jindal ◽  
Er. Williamjeet Singh
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Academic Performance ◽  
Nearest Neighbor ◽  
Educational Data Mining ◽  
Educational Systems ◽  
Good Prediction ◽  
Data Set ◽  
Data Mining Techniques ◽  
Higher Education System

Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. Higher education, throughout the world is delivered through universities, colleges affiliated to various universities and some other recognized academic institutes. The main objective of higher education institutes is to provide quality education to its students. Indian education sector has a lot of data that can produce valuable information which can be used to increase the quality of education. Good prediction of student’s success in higher learning institution is one way to reach the higher level of quality in higher education system. In this paper we analyzed the potential use of data mining in education section and survey the most relevant work in this area. Data Mining can be used for dropout students, student’s academic performance, teacher’s performance and student’s complaints. As we know large amount of data is stored in educational database, so in order to get required data and to find the hidden relationship, different data mining techniques are developed & used. Various algorithms and data mining techniques like Classification, Clustering, Regression, Artificial Intelligence, Neural Networks, Association Rules, Decision Trees (CART and CHIAD), Genetic algorithms, Nearest Neighbor method etc. are used for knowledge discovery from databases and helps in prediction of students academic performance. In future work we can apply different data mining techniques on an expanded data set with more distinct attributes to get more accurate results.

MONITORING OF OPERATIONAL LOGISTIC PROCESSES IN GENERAL CARGO WAREHOUSES USING PREDICTIVE ANALYSIS

2019 ◽  
Vol 13 (1) ◽  
pp. 27-36
Author(s):  
Andreas Neubert
Keyword(s):  
Data Mining ◽  
Human Error ◽  
Predictive Analysis ◽  
Data Mining Algorithm ◽  
It Infrastructure ◽  
Monitoring Procedure ◽  
Manual Recording ◽  
Mining Tool ◽  
General Cargo ◽  
Different Characteristics

Due to the different characteristics of the piece goods (e.g. size and weight), they are transported in general cargo warehouses by manually-operated industrial trucks such as forklifts and pallet trucks. Since manual activities are susceptible to possible human error, errors occur in logistical processes in general cargo warehouses. This leads to incorrect loading, stacking and damage to storage equipment and general cargo. It would be possible to reduce costs arising from errors in logistical processes if these errors could be remedied in advance. This paper presents a monitoring procedure for logistical processes in manually-operated general cargo warehouses. This is where predictive analysis is applied. Seven steps are introduced with a view to integrating predictive analysis into the IT infrastructure of general cargo warehouses. These steps are described in detail. The CRISP4BigData model, the SVM data mining algorithm, the data mining tool R, the programming language C++ for the scoring in general cargo warehouses represent the results of this paper. After having created the system and installed it in general cargo warehouses, initial results obtained with this method over a certain time span will be compared with results obtained without this method through manual recording over the same period.

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

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