scholarly journals Analisis Kinerja Algoritma J48 Decision Tree untuk Pengambilan Keputusan Beli/Jual pada Saham PT Harum Energi Tbk. (HRUM)

2019 ◽  
Vol 1 (3) ◽  
pp. 244-253
Author(s):  
Nur Mishbah Hayat ◽  
Agung Budi Prasetijo ◽  
Risma Septiana
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Stock Prices ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Knowledge Analysis ◽  
Testing Data ◽  
Type Data ◽  
Selling Decisions ◽  
J48 Decision Tree

The problem that is often faced by investors in selling / buying stocks is the difficulty in analyzing a dataset of stock prices in large quantities.This analysis aims to predict the rise or fall of stock prices based on data obtained. To assist investors in determining buying / selling decisions on stock analysis based on technical and equipped with classification techniques in data mining. This study analyzes the performance of the J48 Decision Tree algorithm in the Waikato Environmental Software for Knowledge Analysis (WEKA) version 3.8.2 for PT. Harum Energi Tbk. (HRUM). The results showed in the testing data, the percentage of testing on data without normalization was higher by 87.3 (non-aggressive) and 88.8 (aggressive) compared to normalized data 84.2 (non-aggressive) and 85 (aggressive ). The biggest stock profit generated is in non-aggressive type data without normalized by 48.75 or Rp. 48,750.00.

An Analysis of Factors Influencing Foreign Language Self-Efficacy Based on C5.0 Decision Tree Algorithm in Data Mining

2014 ◽  
Vol 538 ◽  
pp. 460-464
Author(s):  
Xue Li
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Foreign Language ◽  
Language Learning ◽  
Learning Strategies ◽  
Self Efficacy ◽  
Foreign Language Learning ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C5.0 Decision Tree

Based on inter-correlation and permeability among disciplines, the author makes an attempt to apply the information science to cognitive linguistics to provide a new perspective for the study of foreign languages. The correlation between self-efficacy and such four factors as anxiety, learning strategies, motivation and learners’ past achievement is analyzed by means of data mining and the extent to which the above factors affect self-efficacy in language learning is explored in this paper. The paper employs the decision tree algorithm in SPSS Clementine. C5.0 decision tree algorithm is adopted to analyze data in the study. The results are elicited from the researches carried out in this paper. The increased anxiety is bound to weaken learners’ motivation over time. It is obvious that learners have low self-efficacy. It is very important to employ strategies in foreign language learning. Ignorance of using learning strategies may result in unplanned learning with unsatisfactory achievements in spite of more efforts involved. Self-efficacy in foreign language learning may be weakened accordingly. Learners’ past achievement is a reference dimension in measuring self-efficacy with weaker influence.

scholarly journals CUDT: A CUDA Based Decision Tree Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Win-Tsung Lo ◽  
Yue-Shan Chang ◽  
Ruey-Kai Sheu ◽  
Chun-Chieh Chiu ◽  
Shyan-Ming Yuan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
New Technology ◽  
Large Data ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
Tree Algorithm ◽  
Ubiquitous Sensing ◽  
Device Architecture ◽  
Huge Data

Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture), which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5∼55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set.

scholarly journals Network Software Vulnerability Identifier using J48 decision tree algorithm

2021 ◽  
Vol 9 (8) ◽  
pp. 1889-1892
Author(s):  
Shankar Murthy J
Keyword(s):  
Decision Tree ◽  
Cyber Security ◽  
Decision Tree Algorithm ◽  
Software Vulnerability ◽  
Tree Algorithm ◽  
Software Vulnerabilities ◽  
Security Issues ◽  
Modern Era ◽  
Vulnerability Classifications ◽  
J48 Decision Tree

Abstract: Software vulnerabilities are the primary causes of different security issues in the modern era. When vulnerability is exploited by malicious assaults, it substantially jeopardizes the system's security and may potentially result in catastrophic losses. As a result, automatic classification methods are useful for successfully managing software vulnerabilities, improving system security performance, and lowering the chance of the system being attacked and destroyed. In the software industry and in the field of cyber security, the ever-increasing number of publicly reported security flaws has become a major source of concern. Because software security flaws play such a significant part in cyber security attacks, relevant security experts are conducting an increasing number of vulnerability classification studies, this project can predict the software vulnerability means the software's in the device are authorized or not and who scan the system multiple times, to identify the vulnerability j48 decision tree algorithm was used. Keywords: Malicious assaults, catastrophic losses, Security flaws, Cyber security, Vulnerability Classifications.

scholarly journals Early prediction of systemic lupus erythematosus using hybrid K-Means J48 decision tree algorithm

2017 ◽  
Vol 7 (1.3) ◽  
pp. 28
Author(s):  
S. Gomathi ◽  
V. Narayani
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Decision Tree ◽  
Lupus Erythematosus ◽  
Clustering Algorithm ◽  
Early Stage ◽  
Early Prediction ◽  
Decision Tree Algorithm ◽  
Systemic Lupus ◽  
Tree Algorithm ◽  
J48 Decision Tree

The objective of the paper is to propose an enhanced algorithm for the prediction of chronic, autoimmune disease called Systemic Lupus Erythematosus (SLE). The Hybrid K-means J48 Decision Tree algorithm (HKMJDT) has been proposed for the effective and early prediction of the SLE. The reason for combining both the clustering and classification algorithms is to obtain the best accuracy and to predict the disease in the early stage. The performance of algorithms such as Naïve Bayes, decision tree, random forest, J48 and Hoeffding tree has been combined with K-means clustering algorithm and compared in an effort to find the best algorithm for diagnosing SLE disease. The results of the statistical evaluation with the comparative study show that the effectiveness of different classification techniques depends on the nature and intricacy of the dataset used. K-means combined with J48 algorithm shows the best accuracy rate of 82.14% on the pre-processed data. The work-flow has been proposed to show the execution of the algorithm.

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.

Sign in / Sign up

Export Citation Format

Share Document