PREDICTION FOR COOPERATIVE CREDIT ELIGIBILITY USING DATA MINING CLASSIFICATION WITH C4.5 ALGORITHM

BMT Artha Mandiri is a cooperative that provides savings and loans services. In providing credit, BMT Artha Mandiri still uses the manual method, namely by looking at the ledger and history of each customer, to find out whether the applicant is worthy or not worthy of credit so that it is not effective and efficient. The purpose of this research is to make an application that can predict whether a prospective customer is eligible or not to be given credit. Predictions are made using the data mining classification method, namely the C4.5 algorithm based on the supporting data each customer has to classify which factors have the most influence on the level of credit payments in the cooperative. In a built application, the C4.5 algorithm produces a decision tree that is easy to interpret based on the existing variables. In the application, there are features that can be used to make decisions about customers who will apply for credit at the cooperative. The blackbox test results on the application show that the application has been able to run as expected, while the results of the algorithm test also show that the application has been able to implement the C4.5 algorithm correctly. In addition, the results of testing for accuracy show that the maximum average value of Accuracy is 79.19%.

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Using Data Mining with C4.5 Algorithm for Student Department Selection at MTs N Kaliangkrik

Borobudur Informatics Review ◽

10.31603/binr.4989 ◽

2021 ◽

Vol 1 (1) ◽

pp. 22-36

Author(s):

Ardhin Primadewi

Keyword(s):

Data Mining ◽

Test Score ◽

General Knowledge ◽

General Intelligence ◽

Test Results ◽

Language Knowledge ◽

C4.5 Algorithm ◽

System Input ◽

Using Data ◽

Selection Of

Psychological tests can determine the characteristics of behavior, personality, attitudes, interests, motivation, attention, perceptions, thinking power, intelligence, fantasies of students. MTs N Kaliangkrik routinely conducts tests for the selection of majors on its students assisted by Pelita Harapan Bangsa Magelang. In the implementation of the test for students at MTs N Kaliangkrik, processing and calculating the score still used Ms. Excel which requires extra time to recap and know the test results and the school needs to recap the existing results. The system developed applies data mining using the C4.5 Algorithm to predict the selection of majors. The test that is used as system input is the grade IX test score of MTs N Kaliangkrik which includes verbal, non-verbal, general intelligence, language knowledge, definite knowledge, general knowledge, and qualitative power tests. The accuracy of the similarity in the system reaches 80% (good) so that the system is suitable for use as a prediction tool for selecting majors in other schools.

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Penerapan Algoritma C4.5 Dalam Memprediksi Ketersediaan Uang Pada Mesin ATM

JURNAL MEDIA INFORMATIKA BUDIDARMA ◽

10.30865/mib.v5i2.2933 ◽

2021 ◽

Vol 5 (2) ◽

pp. 556

Author(s):

Firman Syahputra ◽

Hartono Hartono ◽

Rika Rosnelly

Keyword(s):

Data Mining ◽

Decision Tree ◽

Travel Time ◽

Tree Model ◽

Optimal Service ◽

C4.5 Algorithm ◽

Cash Transaction ◽

Auc Value ◽

Using Data ◽

Balance Variable

This study aims to provide an evaluation of the availability of money in ATM machines using data mining. Data mining with the C4.5 algorithm is used to predict cash demand or total cash withdrawals at ATMs. To determine the need for ATM cash based on cash transaction data. It is hoped that this forecasting can help the monitoring department in making decisions about the money requirements that must be allocated to each ATM machine. The results of this study are expected to assist the ATM management unit in optimizing and monitoring the availability of money at an ATM machine for cash needs, so that it can provide optimal service to customers. Algortima C4.5 is an algorithm that is able to form a decision tree, where the decision tree will then generate new knowledge. The results of the test matched the data on the availability of money at the ATM machine. The results of implementing the C4.5 method on the availability of money at the ATM machine are seen from the travel time to the ATM location and also the remaining balance in the machine. The resulting decision tree model is to make the balance variable as the root, then the travel time as a branch at Level 1 with the variables fast, medium, long, and the bank becomes a branch at the last level (Level 2). Then the C4.5 algorithm was tested using the K-Fold Cross validation method with the value of fold = 10, it can be seen that the accuracy rate is 85%, the Precision value is 80% and the Recall value is 66.67%. While the AUC (Area Under Curve) value is 0.833, this shows that if the AUC value approaches the value 1, the accuracy level is getting better

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Analisis Rekomendasi Calon Debitur Motor pada PT.XYZ menggunakan Algortima C 4.5

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020742080 ◽

2020 ◽

Vol 7 (4) ◽

pp. 673

Author(s):

Lilis Nurellisa ◽

Devi Fitrianah

Keyword(s):

Decision Making ◽

Decision Tree ◽

Supervised Learning ◽

Data Driven ◽

Classification Method ◽

Service Company ◽

C4.5 Algorithm ◽

Application Data ◽

Using Data ◽

Existing Data

PT.XYZ merupakan perusahaan jasa pembiayaan atau leasing dengan berkonsentrasi kepada pembiayaan sepeda motor. Dalam bisnisnya PT.XYZ sering sekali dihadapkan oleh masalah kredit macet atau bahkan penipuan. Hal ini dikarenakan kesalahan dalam pemberian kredit kepada calon debitur yang tidak potensial. Jika tidak ditangani hal ini tentu saja akan berdampak buruk bagi perusahaan. Perusahaan mengalami penurunan kemampuan dalam membayar angsuran pinjaman ke perbankan bahkan dapat berdampak pada kebangkrutan. Dalam hal ini PT.XYZ perlu melalukan analisis untuk menentukan calon debitur yang potensial dengan menggunakan data driven method atau pendekatan berbasis kepada data. Yaitu pengambilan keputusan dengan melihat data pengajuan kredit yang pernah ada sebelumnya yang disebut juga sebagai supervised learning. Algoritma yang digunakan adalah algoritma C4.5 karena algoritma ini dapat mengklasifikasi data yang sudah ada sebelumnya. Dengan algoritma ini akan dihasilkan sebuah pohon keputusan yang akan membantu PT.XYZ dalam pengambilan keputusan. Dengan pengujian menggunakan 3587 sampel data pengajuan kredit dalam kurun waktu 1 tahun akurasi yang didapatkan ialah 97,96%. Dengan begitu hal ini menunjukkan bahwa metode klasifikasi menggunakan algoritma C4.4 berhasil diimplementasikan dengan baik. Hal ini diharapkan dapat membantu PT.XYZ dalam merekomendasikan calon debitur yang potensial. AbstractPT. XYZ is a finance or leasing service company by concentrating on motorcycle financing. In its business, PT. XYZ is often faced with problems of bad credit or even fraud. This is due to an error in giving credit to potential debtors. If it is not handled this, of course, will have a bad impact on the company. Companies experiencing a decline in the ability to repay loan installments to banks can even have an impact on bankruptcy. In this case, PT. XYZ needs to do an analysis to determine potential debtors using data-driven methods or data-based approaches. That is decision making by looking at credit application data that has never been before, which is also called supervised learning. The algorithm used is the C4.5 algorithm because this algorithm can classify pre-existing data. With this algorithm, a decision tree will be produced that will help PT. XYZ in decision making. By testing using 3587 samples of credit filing data within a period of 1 year the accuracy obtained was 97.96%. That way this shows that the classification method using the C4.4 algorithm is successfully implemented properly. This is expected to help PT. XYZ in recommending potential debtors.

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

10.31227/osf.io/x6svc ◽

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.

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Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

Creative Information Technology Journal ◽

10.24076/citec.2014v1i4.27 ◽

2015 ◽

Vol 1 (4) ◽

pp. 270

Author(s):

Muhammad Syukri Mustafa ◽

I. Wayan Simpen

Keyword(s):

Data Mining ◽

Nearest Neighbor ◽

Test Results ◽

K Nearest Neighbor ◽

Accuracy Rate ◽

Sample Data ◽

New Students ◽

K Nearest Neighbor Algorithm ◽

Using Data ◽

Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

JURIKOM (Jurnal Riset Komputer) ◽

10.30865/jurikom.v7i2.1762 ◽

2020 ◽

Vol 7 (2) ◽

pp. 200

Author(s):

Puji Santoso ◽

Rudy Setiawan

Keyword(s):

Data Mining ◽

Decision Tree ◽

Microsoft Excel ◽

Customer Data ◽

Data Mining Techniques ◽

C4.5 Algorithm ◽

Marketing Costs ◽

Excel Format ◽

Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

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Implementasi Data Mining dengan Algoritma C4.5 untuk Memprediksi Tingkat Kelulusan Mahasiswa

Jurnal ULTIMATICS ◽

10.31937/ti.v6i1.327 ◽

2014 ◽

Vol 6 (1) ◽

pp. 15-20 ◽

Cited By ~ 5

Author(s):

David Hartanto Kamagi ◽

Seng Hansun

Keyword(s):

Data Mining ◽

Drop Out ◽

Time Pass ◽

Academic Fields ◽

C4.5 Algorithm ◽

Index Terms ◽

Using Data ◽

Academic Year ◽

Source Of Information ◽

Test Result

Graduation Information is important for Universitas Multimedia Nusantara which engaged in education. The data of graduated students from each academic year is an important part as a source of information to make a decision for BAAK (Bureau of Academic and Student Administration). With this information, a prediction can be made for students who are still active whether they can graduate on time, fast, late or drop out with the implementation of data mining. The purpose of this study is to make a prediction of students’ graduation with C4.5 algorithm as a reference for making policies and actions of academic fields (BAAK) in reducing students who graduated late and did not pass. From the research, the category of IPS semester one to semester six, gender, origin of high school, and number of credits, can predict the graduation of students with conditions quickly pass, pass on time, pass late and drop out, using data mining with C4.5 algorithm. Category of semester six is the highly influential on the predicted outcome of graduation. With the application test result, accuracy of the graduation prediction acquired is 87.5%. Index Terms-Data mining, C4.5 algorithm, Universitas Multimedia Nusantara, prediction.

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Predicting customer churn in mobile industry using data mining technology

Industrial Management & Data Systems ◽

10.1108/imds-12-2015-0509 ◽

2017 ◽

Vol 117 (1) ◽

pp. 90-109 ◽

Cited By ~ 16

Author(s):

Eui-Bang Lee ◽

Jinwha Kim ◽

Sang-Gun Lee

Keyword(s):

Data Mining ◽

Decision Tree ◽

Online News ◽

Partial Least Square ◽

Churn Prediction ◽

Mining Technology ◽

Content Type ◽

Customer Churn ◽

Mobile Industry ◽

Using Data

Purpose The purpose of this paper is to identify the influence of the frequency of word exposure on online news based on the availability heuristic concept. So that this is different from most churn prediction studies that focus on subscriber data. Design/methodology/approach This study examined the churn prediction through words presented the previous studies and additionally identified words what churn generate using data mining technology in combination with logistic regression, decision tree graphing, neural network models, and a partial least square (PLS) model. Findings This study found prediction rates similar to those delivered by subscriber data-based analyses. In addition, because previous studies do not clearly suggest the effects of the factors, this study uses decision tree graphing and PLS modeling to identify which words deliver positive or negative influences. Originality/value These findings imply an expansion of churn prediction, advertising effect, and various psychological studies. It also proposes concrete ideas to advance the competitive advantage of companies, which not only helps corporate development, but also improves industry-wide efficiency.

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