scholarly journals Decision Tree dalam Menganalisis Data Pengunjung Wisata Danau Poso untuk Pengambilan Keputusan

2021 ◽  
Vol 2 (3) ◽  
pp. 261
Author(s):  
Fredryc Joshua Pa'o ◽  
Hendry Hendry
Keyword(s):  
Decision Tree ◽  
Classification System ◽  
Leaf Node ◽  
Test Results ◽  
Class Distribution ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
The Right ◽  
Data Transforming ◽  
Tree Method

This study uses a classification system in managing its data. In classification there are several methods provided, one of which is the decision tree method with the C4.5 algorithm this method means a decision tree where the structure is the same as a flowchart where each node signifies an attribute test, each branch presents the test results and the leaf node represents the class or class distribution. The data used is the data of Lake Poso Tourism visitors from 2009 to 2020, then the method used in this study is divided into several stages, namely the data being studied, analyzing the data, transforming data and designing a decision tree with the C4.5 algorithm. The results achieved from this study are that the number of visitors more than 28,984 has a description of "Much" which is dominated by local tourists, while the value with the name "Less" is in foreign tourists. This is one of the important points in determining the right strategy for developing tourism in Lake Poso.

scholarly journals Implementation of Data Mining Using C4.5 Algorithm for Predicting Customer Loyalty of PT. Pegadaian (Persero) Pati Area Office

2021 ◽  
Vol 2 (3) ◽  
pp. 64-68
Author(s):  
Ridlo Muttaqien ◽  
Musthofa Galih Pradana ◽  
Andri Pramuntadi
Keyword(s):  
Decision Tree ◽  
Customer Loyalty ◽  
Influential Factors ◽  
Marketing Strategies ◽  
Management Information ◽  
Experimental Application ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
The Right ◽  
Tree Method

PT Pegadaian (Persero) is engaged in the business of providing credit services with pawn, non-pawning and gold investment products. One of the right marketing strategies to survive today's high competition is to maintain customer loyalty, researchers use several data variables available in the MIS (Management Information System) in the form of customer transaction frequency, how many products are taken by customers, customer satisfaction and direct interviews. to predict customer loyalty of PT Pegadaian (Persero) by implementing the c4.5 algorithm. The c4.5 algorithm is the algorithm used to create a decision tree. Decision trees are a very powerful and well-known method of classification and prediction. The decision tree method converts very large facts into a decision tree that represents the rule. Rules can be easily understood in natural language. This study aims to determine the accuracy of the C4.5 algorithm to predict customer loyalty of PT Pegadaian (Persero) and the most influential factors in loyalty. The results of the experimental application of the c4.5 algorithm show that the level of accuracy generated in predicting customer loyalty is quite high, namely 89.94% in data testing 1 and 94% in data testing 2. The application of the c4.5 algorithm in predicting customer loyalty of PT Pegadaian (Persero) can be well applied.

scholarly journals Penerapan Metode Klasifikasi Decision Tree Pada Status Gizi Balita Di Kabupaten Simalungun

2020 ◽  
Vol 1 (2) ◽  
pp. 68-72
Author(s):  
Hananda Hafizan ◽  
Anggita Nadia Putri
Keyword(s):  
Decision Tree ◽  
Nutritional Status ◽  
Public Health Service ◽  
Children Under Five ◽  
Under Five ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
Nutritional Status Of Children ◽  
Tree Method

One of the health problems in Indonesia is the problem of nutritional status of children under five years. Cases of malnutrition are not only a family problem, but also a state problem. The nutritional status of children under five years can be assessed by measuring the human body known as "Anthropometry". To be able to carry out anthropometric examinations and measurements in order to find out the nutritional status of children under five, they can go to public health service places such as the Posyandu. We went to the KENANGA Posyandu located in Wonorejo, Kerasaan sub-district, Simalungun district. The purpose of this study will be to test the model for the classification of nutritional status of children under the WHO-2005 reference standard by utilizing data mining techniques using the Decision Tree method C4.5 Algorithm.

The Water Conservancy Water Electricity Construction Engineering Professional Result Analysis of Application Base on C4.5 Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 703-707
Author(s):  
Hu Yong
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Construction Engineering ◽  
Improvement Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Selection Decision ◽  
Decision Tree Method ◽  
Result Analysis ◽  
Tree Method

Aimed at the student the result problem, give student the result data scoops out the model. The decision tree method is a very valid classification method, in the data that scoop out. According to student the result data characteristics, adopted the C4.5 decision tree algorithm. C4.5 algorithm is the improvement algorithm of the decision trees core algorithm ID3, it construct in brief, the speed compare quickly, easy realization. Selection decision belongs to sex, scoop out the result enunciation, that algorithm can be right to get student the result data classification, and some worthy conclusion, provide the decision the analysis.

scholarly journals Classification Analysis Using C4.5 Algorithm To Predict The Level of Graduation of Nurul Falah Pekanbaru High School Students

2019 ◽  
Vol 2 (2) ◽  
pp. 43
Author(s):  
Fana Wiza ◽  
Bayu Febriadi
Keyword(s):  
High School ◽  
High School Students ◽  
Decision Tree ◽  
Graduation Rates ◽  
Graduation Rate ◽  
School Students ◽  
C4.5 Algorithm ◽  
Student Graduation ◽  
Decision Tree Method ◽  
Tree Method

School as one of the processes for implementing formal education is required to carry out the learning process optimally to produce quality students. Regarding the research process carried out to predict the graduation rate of SMA Nurul Falah students by using the decision tree method. The data used in this study are student data using the criteria for student names, majors, average report cards from semester one (I), two (II), three (III), four (IV), five (V), and the average value of the National Standard School Examination (USBN). The data is then managed using Rapidminer 5.3 software to make it easier to predict student graduation rates. The application of data mining is used to predict the graduation rate by using the decision tree method and C4.5 algorithm as a supporter as well as to find out information on the graduation rate of Nurul Falah High School students. This study aims to predict student graduation rates in order to get useful information and the school can make policies in the coming year.

scholarly journals ANALISIS REKOMENDASI PEMINATAN MENGGUNAKAN METODE DECISION TREE DENGAN ALGORITMA C4.5

2018 ◽  
Vol 2 (2) ◽  
pp. 178
Author(s):  
Tri Sutrisno ◽  
Stefanny Claudia
Keyword(s):  
Information Technology ◽  
Decision Tree ◽  
Training Data ◽  
Prediction System ◽  
Tree Model ◽  
Average Accuracy ◽  
C4.5 Algorithm ◽  
Academic Grades ◽  
Decision Tree Method ◽  
Tree Method

The application created are used to analyze which thesis preference subject suits students academic performance based on their academic grades. The application also provide online academic consultations features which students can use for their academic consultations. To find their thesis preference, the application use decision tree method with C4.5 algorithm. Testing prediction system using students data from 2012 to 2015 who have found their thesis preference. The value data used is 32 mandatory courses in the Faculty of Information Technology before thesis preference. The application can run , use and perform well in accordance with the design made. Testing is to compare the accuracy of the selected tree model build from training data and the thesis preference students have selected. The average accuracy percentage of this a 72,6227%.

scholarly journals Application of Classification Algorithm C 4.5 for Predicting Asset Maintenance

2019 ◽  
Vol 7 (4) ◽  
pp. 18
Author(s):  
Muhammad Rizki ◽  
Deni Mahdiana
Keyword(s):  
Decision Tree ◽  
Asset Management ◽  
Decision Rules ◽  
Test Results ◽  
C4.5 Algorithm ◽  
Decision Making Processes ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Decision Tree Method ◽  
A Company

In asset management, determining maintenance actions is one of the problems faced by the company. The importance of maintenance to accelerate the production or performance of a company is now a necessity that must be run. The problem faced by Astra Daihatsu Motor is the difficulty in determining the maintenance action that must be chosen because of information delays when there are assets that are damaged, failed or failure. With the proposal using a decision tree with C4.5 algorithm can predict failures and damage that occur so that it can determine more accurate maintenance actions. Decision tree is a prediction model using tree structure or hierarchical structure. The concept of a decision tree is to transform data into decision trees and decision rules. The main benefit of using a decision tree is its ability to break down complex decision-making processes to be simpler so that decision makers will better interpret the solution of the problem. Using the decision tree method with the C4.5 algorithm can help the problems faced by Astra Daihatsu Motor in determining maintenance. This is shown from the test results of 98.20%. And it can be concluded that the application of C4.5 algorithm is able to produce asset maintenance patterns with better accuracy.

scholarly journals Prediksi Mahasiswa Berpotensi Non Aktif Menggunakan Data Mining dalam Decision Tree dan Algoritma C4.5

2020 ◽  
pp. 23-29
Author(s):  
Nur Yanti Lumban Gaol
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Dropping Out ◽  
Mining Science ◽  
Prediction Rules ◽  
Tertiary Institutions ◽  
Student Data ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
Tree Method

Non-active students are students who do not attend the lecture process and do not pay tuition administration fees within two semesters or more. Reports on students who are not active will have an impact on the quantity of tertiary institutions. Students who are not registered in non-active students will potentially be expelled or dropped out. For this reason, this research was conducted to explore information on potentially non-active students by applying data mining science with the Decision Tree method and C4.5 algorithm. The tested data were sourced from Triguna Dharma Medan College of Information and Computer Management (STMIK). The results of the study get prediction rules for student data that are potentially non-active with a very good degree of accuracy. So this research can be used to avoid students dropping out unilaterally.

Estimation of Gourami Supplies Using Gradient Boosting Decision Tree Method of XGBoost

TEM Journal ◽  
2021 ◽  
pp. 144-151
Author(s):  
I Made Sukarsa ◽  
Ngakan Nyoman Pandika Pinata ◽  
Ni Kadek Dwi Rusjayanthi ◽  
Ni Wayan Wisswani
Keyword(s):  
Decision Tree ◽  
Absolute Error ◽  
Feature Model ◽  
Gradient Boosting ◽  
Percentage Error ◽  
Transaction Data ◽  
Time Period ◽  
Decision Tree Method ◽  
The Right ◽  
Tree Method

The need for food supplies are very crucial in a food business, therefore it is necessary to estimate the right supplies to maximize profit. One of the methods to determine these is by looking for patterns and forecasting transaction data. The purpose of this research is to estimate the gourami supplies using transaction data to forecast using the gradient boosting decision tree method from XGBoost. The transaction data used comes from Restaurant X with a time period from 2016 to 2019. A measurement error rate of the model is achieved by using MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This study tried five XGBoost models with different features such as lag, rolling window, mean encoding, and mix. The results of this study indicate that the mixed feature model produces an accuracy of 97.54% with an MAE of 0.63 and a MAPE of 2.64%.

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