Identifikasi Tingkat Kerusakan Peralatan Labor Teknik Komputer Jaringan Menggunakan Metode Decision Tree

The computer laboratory is a place for practical learning for students, where computers have an important role in the smooth running of the practice. The use of computer labor at any time is very vulnerable to damage. If there is damage it will disrupt the teaching and learning process. Utilization of data mining in determining the level of damage is one of them. SMKN 1 Sintuk Toboh Gadang has 3 laboratories, TKJ (Network Computer Engineering), RPL (Software Engineering) and Technician labor. Application of the Decision Tree method in identifying damage to computer laboratory equipment, especially TKJ (Computer Network Engineering) labor. The data obtained in this study are computer equipment sourced from the computer laboratory of SMKN 1 Sintuk Toboh Gadang. Based on the analysis of the computer laboratory, there are 50 computer laboratory equipment. Furthermore, if the data is processed, several variables are needed to identify the level of damage to labor equipment including the name of the tool, number of tools, inspection, duration of use, and condition. The result of testing this method is to test whether the labor equipment can still be used or repaired. The purpose of this research is to help computer labor technicians to identify computer labor equipment that can still be used or repaired so that no damage occurs during practical learning hours. Furthermore, the best method in determining the level of damage to computer laboratory equipment is the Decision Tree Algorithm method. Decision Tree Algorithm is a predictive model using a decision tree structure and makes complex decisions simpler. The results of the research method show that the condition variable has the highest Gain value, namely 0.4734353, then the variable length of use is obtained with a Gain value of 0.896038. The factors that cause damage include the condition of the tool and the duration of use.

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An Improved Synthesized Decision Tree Algorithm and its Application

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.108-111.244 ◽

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.

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Study on Data Mining with Decision Tree Algorithm in the Student Information Management System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.3602 ◽

2014 ◽

Vol 543-547 ◽

pp. 3602-3605

Author(s):

Liang Li ◽

Ying Zheng ◽

Xiao Hua Sun ◽

Fu Shun Wang

Keyword(s):

Data Mining ◽

Decision Tree ◽

Information Management ◽

Management System ◽

Information Management System ◽

Decision Tree Algorithm ◽

Mining Technology ◽

Tree Algorithm ◽

Student Information ◽

Decision Tree Method

With the advance of education informationization in china, information technology and data mining technology has been widely used in the field of education. The decision tree method is one of the data mining methods; it does not require any assumption, can intelligent classification to a large amount of data directly. According to certain rules to find hidden and valuable information.Use the idea of learners as the center as guiding principle, using decision tree algorithm of data mining technology to build student information management system, selects typical C4.5 algorithm among the decision tree methods, Take mass information about employment of graduation in university students' information management system as an example to generate the decision tree, collect potential rules and factors in favor of graduat employment, so as to guide the education and management.

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