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.

The Analysis and Application of the C4.5 Algorithm in Decision Tree Technology

2012 ◽  
Vol 457-458 ◽  
pp. 754-757
Author(s):  
Hong Yan Zhao
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Teaching Quality ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Result Analysis

The Decision Tree technology, which is the main technology of the Data Mining classification and forecast, is the classifying rule that infers the Decision Tree manifestation through group of out-of-orders, the non-rule examples. Based on the research background of The Decision Tree’s concept, the C4.5 Algorithm and the construction of The Decision Tree, the using of C4.5 Decision Tree Algorithm was applied to result analysis of students’ score for the purpose of improving the teaching quality.

Research on Asset Recognition Algorithm of Information Security Product Based on Decision Tree Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 2296-2300 ◽  
Author(s):  
Fei Shuai ◽  
Jun Quan Li
Keyword(s):  
Computational Complexity ◽  
Information Security ◽  
Decision Tree ◽  
Recognition Algorithm ◽  
Space Complexity ◽  
Complex Relationship ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree

In current, there are complex relationship between the assets of information security product. According to this characteristic, we propose a new asset recognition algorithm (ART) on the improvement of the C4.5 decision tree algorithm, and analyze the computational complexity and space complexity of the proposed algorithm. Finally, we demonstrate that our algorithm is more precise than C4.5 algorithm in asset recognition by an application example whose result verifies the availability of our algorithm.Keywordsdecision tree, information security product, asset recognition, C4.5

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.

scholarly journals Implementasi Algoritma Ant Tree Miner Untuk Klasifikasi Jenis Fauna

Infotekmesin ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 150-154
Author(s):  
Yunita Ardilla ◽  
Wilda Imama Sabilla ◽  
Nurissaidah Ulinnuha
Keyword(s):  
Decision Tree ◽  
Ant Colony Optimization ◽  
Time Series Data ◽  
Image Data ◽  
Ant Colony ◽  
Series Data ◽  
C4.5 Decision Tree ◽  
Decision Tree Method ◽  
Time Required ◽  
Tree Method

Classification is a field of data mining that has many methods, one of them is decision tree. Decision tree is proven to be able to classify many kinds of data such as image data and time series data. However, there are several obstacles that are often encountered in the decision tree method. Running time required for the execution of this algorithm is quite long, so this study proposed to use the ant tree miner algorithm which is a development algorithm from the C4.5 decision tree. Ant tree miner works by utilizing ant colony optimization in the process of building its tree structure. Use ant colony optimization expected can optimize the tree that will be formed. From the testing that have been carried out, an accuracy of about 95% is obtained in the process of classifying Zoo dataset with the number of ants between 60 - 90.

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%.

An Improved Synthesized Decision Tree Algorithm and its Application

2010 ◽  
Vol 108-111 ◽  
pp. 244-249
Author(s):  
Jian Lin Qiu ◽  
Dan Ji ◽  
Xiang Gu ◽  
Fen Li ◽  
Peng He
Keyword(s):  
Decision Tree ◽  
Dimension Reduction ◽  
Small Scale ◽  
Decision Tree Algorithm ◽  
Clustering Methods ◽  
Tree Algorithm ◽  
Initial Dataset ◽  
Tree Methods ◽  
Decision Tree Method ◽  
Tree Method

Decision tree classification is one of the most widely-used methods in data mining which can provide useful decision-making analysis for users. But most of the decision tree methods have some efficiency bottle-necks and can only applied to small-scale datasets. In this paper, we present an new improved synthesized decision tree algorithm named CA which includes three important parts like dimension reduction, pre-clustering and decision tree method, and also give out its formalized specification. Through dimension reduction and synthesized pre-clustering methods, we can optimize the initial dataset and considerably reduce the decision tree’s input computation costs. We also improve the decision tree method by introducing parallel processing concept which can enhance its calculation precision and decision efficiency. This paper applies CA into maize seed breeding and analyzes its efficiency in every part comparing with original methods, and the results shows that CA algorithm is better.

scholarly journals METODE ALGORITMA DECISION TREE C4.5 UNTUK ANALISIS KELAYAKAN KREDIT NASABAH PADA BSM KCP KEMANG PRATAMA

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Dyah Wulandari ◽  
Nur Lutfiyana ◽  
Heny Sumarno
Keyword(s):  
Decision Tree ◽  
Profit Sharing ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Customer Data ◽  
A Value ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Credit Worthiness

Abstract - Credit is the provision of money or equivalent claims, based on agreements or agreements on loans between banks and other parties which require the borrowing party to repay the debt after a certain period of time with the amount of interest, compensation or profit sharing. From the credit customer data available at BSM KCP Kemang Pratama still has Non Performing Financing (NPF) or Bad Credit.In analyzing a credit sometimes an analyst does an inaccurate analysis, so there are some customers who are less able to make credit payments, resulting in bad credit. So the researchers conducted an analysis using the C4.5 decision tree algorithm and Rapid Miner application for determining credit worthiness. From the analysis of credit customer data using the C4.5 decision tree algorithm method, the feasibility of credit recipient customers is very effective and produces a value of accuracy on Rapid Miner 5.3 of 80%, Precision of 100% and Recall of 0% so as to minimize the risk.Keywords— Credit, C4.5 Algorithm, Rapid Miner, Value AccuracyAbstrak - Kredit merupakan penyediaan uang atau tagihan yang dapat disamakan dengan hal itu, berdasarkan persetujuan atau kesepakatan pinjaman-pinjaman antara bank dengan pihak lain yang mewajibkan pihak peminjam untuk melunasi utangnya setelah jangka waktu tertentu dengan jumlah bunga, imbalan atau pembagian hasil keuntungan. Dari data nasabah kredit yang ada pada BSM KCP Kemang Pratama masih memiliki Non Performing Financing (NPF) atau Kredit Macet. Dalam menganalisa sebuah kredit terkadang seorang analis melakukan analisa tidak akurat, sehingga ada beberapa nasabah yang kurang mampu dalam melakukan pembayaran kredit, dan pada akhirnya mengakibatkan kredit macet. Peneliti melakukan analisis menggunakan algoritma decision tree C4.5 dan aplikasi Rapid Miner untuk penentuan kelayakan pemberian kredit. Dari analisis data nasabah kredit menggunakan metode Algoritma decision tree C4.5 menghasilkan kelayakan nasabah penerima kredit sangat efektif dan menghasilkan nilai akurasi pada Rapid Miner 5.3 sebesar 80%, Precision sebesar 100% dan Recall sebesar 0% sehingga dapat meminimalisir resiko yang terjadi.Kata kunci— Kredit, Algoritma C4.5, Rapid Miner, Nilai Akurasi

Sign in / Sign up

Export Citation Format

Share Document