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

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

An Educational Data Mining Application by Using Multiple Intelligences

2020 ◽  
pp. 93-110
Author(s):  
Esra Aksoy ◽  
Serkan Narli ◽  
Mehmet Akif Aksoy
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Learning Styles ◽  
Multiple Intelligences ◽  
Gifted Students ◽  
Personality Types ◽  
Decision Tree Model ◽  
Tree Model ◽  
Educational Domain ◽  
Using Data

The aim of this chapter is to illustrate both uses of data mining methods and the way of these methods can be applied in education by using students' multiple intelligences. Data mining is a data analysis methodology that has been successfully used in different areas including the educational domain. In this context, in this study, an application of EDM will be illustrated by using multiple intelligence and some other variables (e.g., learning styles and personality types). The decision tree model was implemented using students' learning styles, multiple intelligences, and personality types to identify gifted students. The sample size was 735 middle school students. The constructed decision tree model with 70% validity revealed that examination of mathematically gifted students using data mining techniques may be possible if specific characteristics are included.

scholarly journals The Application of Data Mining in Determining Timely Graduation Using the C45 Algorithm

2019 ◽  
Vol 3 (1) ◽  
pp. 31
Author(s):  
Asro Pradipta ◽  
Dedy Hartama ◽  
Anjar Wanto ◽  
Saifullah Saifullah ◽  
Jalaluddin Jalaluddin
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Ordinary People ◽  
Tree Model ◽  
Student Profile ◽  
Student Gender ◽  
C4.5 Algorithm ◽  
Student Graduation ◽  
Value Of Accreditation

Graduating on time is one element of higher education accreditation assessment. In the Strata 1 level, students are declared to graduate on time if they can complete their studies <= eight semesters or four years. BAN-PT sets a timely graduation standard of >= 50%. If the standard is not met, it will reduce the value of accreditation. These problems encourage the Universitas Simalungun Pematangsiantar to conduct evaluations and strategic steps in an effort to increase student graduation rates so that the targets of BAN-PT can be achieved. For this reason it is necessary to know in advance the pattern of students who tend not to graduate on time. In this study, C4.5 Algorithm is proposed to predict student graduation. This algorithm will process student profile datasets totaling 150 data. This dataset has a graduation status label. The value of the label is categorical, that is, right and late. The features or attributes used, namely the name of the student, gender, student status, GPA. The results of the C4.5 algorithm are in the form of a decision tree model that is very easy to analyze. In fact, even by ordinary people. This model will map the patterns of students who have the potential to graduate on time and late.

Mathematical modelling of evapotranspiration by using remote sensing and data mining

2021 ◽  
Author(s):  
Lamya Neissi ◽  
Mona Golabi ◽  
Mohammad Albaji ◽  
Abd Ali Naseri
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Decision Tree ◽  
Specific Area ◽  
Decision Tree Model ◽  
Tree Model ◽  
Water Index ◽  
Model Builder ◽  
Using Data ◽  
Extended Area

Abstract Precise evaluation of evapotranspiration in an extended area is crucial for water requirement. By using remote sensing evapotranspiration algorithms, many climatological variables are needed. In case of using climatological variable measurements, many climatic stations must be established in that specific area. By using data mining method integrated with remote sensing, evapotranspiration can be calculated with high accuracy. A physical-based SEBAL evapotranspiration algorithm was modeled by GIS model builder for ET calculations. Albedo, emissivity, and Normalized Difference Water Index (NDWI) were considered as M5 decision tree model inputs. Evapotranspiration was evaluated for 3 April 2020 to 17 September 2020 and the equations were extracted in the M5 decision tree model and these equations were modeled in GIS by using python scripts for 3 April 2020 to 17 September 2020. The results make clear that the mathematical decision tree model can estimate the evapotranspiration gained by physical-based SEBAL algorithm in high accurately.

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

2021 ◽  
Vol 2 (2) ◽  
pp. 67-74
Author(s):  
Yogiek Indra Kurniawan ◽  
Annastalia Fatikasari ◽  
Muhammad Luthfi Hidayat ◽  
Mohamad Waluyo
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Classification Method ◽  
Test Results ◽  
Savings And Loans ◽  
Manual Method ◽  
Average Value ◽  
C4.5 Algorithm ◽  
History Of ◽  
Using Data

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%.

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 Evaluating Evapotranspiration By Using Data Mining Instead of The Physical Based Model In Remote Sensing

2021 ◽  
Author(s):  
Lamya Neissi ◽  
Mona Golabi ◽  
Mohammad Albaji ◽  
Abd Ali Naseri
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Decision Tree ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Plant Production ◽  
Tree Model ◽  
Water Index ◽  
Input Variables ◽  
Using Data

Abstract Precise calculations for plant water requirements and evapotranspiration is very crucial in determining the volume of water consumption for plant production. In order to estimate evapotranspiration in the extended area, different remote sensing algorithms required many climatological variables. Climatological variable measurements will cover small limited areas which can cause an error in extended areas. By using data mining and remote sensing, the evapotranspiration process can be modeled. In this research, the physical-based SEBAL evapotranspiration algorithm was modeled by M5 decision tree equations in GIS. Input variables of the M5 decision tree consisted of albedo, emissivity, and Normalized Difference Water Index (NDWI) which are represented as absorbed light, transformed light, and plant moisture, respectively. After extracting the best equations in the M5 decision tree model for 8 April 2019, these equations were modeled in GIS by using python scripts for 8 April 2019 and 3 April 2020. The calculated correlation coefficient (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for 8 April 2019 were 0.92, 0.54, and 0.42 and for 3 April 2020 were 0.95, 0.31, and 0.23, respectively. Also, sensitivity and uncertainty analysis were considered for more model evaluation. Those analysis revealed that evapotranspiration is sensitive to albedo more than the two other model inputs and the estimated evapotranspiration obtained by data mining is in acceptable range of certainty.

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

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

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

2014 ◽  
Vol 6 (1) ◽  
pp. 15-20 ◽  
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.

scholarly journals Using Data Mining to Analyze Dwell Time and Nonstop Running Time in Road-Based Mass Transit Systems

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1217
Author(s):  
Teresa Cristóbal ◽  
Gabino Padrón ◽  
Alexis Quesada ◽  
Francisco Alayón ◽  
Gabriel de Blasio ◽  
...  
Keyword(s):  
Data Mining ◽  
Travel Time ◽  
Dwell Time ◽  
Mass Transit ◽  
Transit Systems ◽  
Running Time ◽  
Bus Stop ◽  
Transport Line ◽  
Using Data

Travel Time plays a key role in the quality of service in road-based mass transit systems. In this type of mass transit systems, travel time of a public transport line is the sum of the dwell time at each bus stop and the nonstop running time between pair of consecutives bus stops of the line. The aim of the methodology presented in this paper is to obtain the behavior patterns of these times. Knowing these patterns, it would be possible to reduce travel time or its variability to make more reliable travel time predictions. To achieve this goal, the methodology uses data related to check-in and check-out movements of the passengers and vehicles GPS positions, processing this data by Data Mining techniques. To illustrate the validity of the proposal, the results obtained in a case of use in presented.

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