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

2019 ◽  
Vol 1 (4) ◽  
pp. 40-46
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.

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.

Data Mining for ERW Welded Tube Scheduling Rules Based on Decision Tree

2011 ◽  
Vol 403-408 ◽  
pp. 1804-1807
Author(s):  
Ning Zhao ◽  
Shao Hua Dong ◽  
Qing Tian
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Arc Welding ◽  
Data Cleaning ◽  
Data Extraction ◽  
Electric Arc ◽  
Welded Tube ◽  
Electric Arc Welding ◽  
Decision Tree Method ◽  
Tree Method

In order to optimize electric- arc welding （ERW） welded tube scheduling , the paper introduces data cleaning, data extraction and transformation in detail and defines the datasets of sample attribute, which is based on analysis of production process of ERW welded tube. Furthermore, Decision-Tree method is adopted to achieve data mining and summarize scheduling rules which are validated by an example.

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.

Animal Husbandry Decision Tree Data Mining Applications in Intelligent System

2014 ◽  
Vol 926-930 ◽  
pp. 2529-2532
Author(s):  
Shu Yan Wu ◽  
Hui Ling Wu ◽  
Tai Yu Liu ◽  
Xian Yu Cui
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Theoretical Basis ◽  
Animal Husbandry ◽  
Specific Work ◽  
Decision System ◽  
Intelligent Decision ◽  
Decision Tree Method ◽  
Tree Method ◽  
Intelligent Decision System

In this paper, the research based on decision tree-based methods can be customized to achieve a common data analysis system for animal husbandry intelligent decision support, conducted the following study: First, detail the current data mining techniques and animal husbandry areas where the decision tree method for various applications, purpose, content and methods. Secondly, the theoretical basis of the data mining system, basic theory and the theory of decision tree methods of animal husbandry intelligent decision system, which constitute the theoretical basis of the study. Third, given the detailed design of common animal husbandry intelligent decision system based decision tree method. Detailed descript of the specific work processes and systems function modules of the system on the line division. To complete the design of the database based on the completion of the system design.

scholarly journals Simulasi Prakiraan Dan Klasifikasi Hujan Wilayah Kota jakarta dengan Metode Decision Tree

2021 ◽  
Vol 4 (1) ◽  
pp. 27
Author(s):  
Arif Ega Prakosa ◽  
Ahmad Fawaid ◽  
Irkhas Nusantara ◽  
Faisal Amri ◽  
Aries Saifudin
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Natural Occurrence ◽  
Decision Tree Method ◽  
Tree Method

Rain is a natural occurrence that occurs in the hydrological and climatic circulation. Based on the ups and downs of the hydrological circulation, one of the sources of water is rain. rain is very useful in life, because rain can meet the needs of water for living creatures. However, rain can also cause floods. A flood tragedy can cause loss and casualties. Then our activities are checking the forecast simulation forecast and rain classification using the accurate decision tree method. We take forecast and classification data because the weather in DKI Jakarta is currently very difficult to predict. Next, we use a decision tree for data mining with a dataset spanning 5 years from 2011-2015. The problem here is the weather in DKI Jakarta which is very difficult to predict.

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%.

The Pruning Strategy of Decision Tree of SMT Assembling Quality Control Based on Data Mining

2011 ◽  
Vol 204-210 ◽  
pp. 704-707
Author(s):  
Hua Jiang ◽  
Tong Lai Liu ◽  
Han Lei He
Keyword(s):  
Data Mining ◽  
Quality Control ◽  
Decision Tree ◽  
Data Warehouse ◽  
Decision Support Model ◽  
Pruning Strategy ◽  
Effective Decision ◽  
Decision Tree Method ◽  
Effective Decision Making ◽  
Tree Method

Based on the establishment of data warehouse of the decision support model of the SMT assembling quality control, this paper relies its basis on the SLIQ algorithm of the decision tree method of data mining, improves the pruning strategy and conducts a useful exploration on how to form more effective decision-making basis of the quality control system of SMT assembling in order to improve the accuracy and predictability of decision analysis.

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