Comparison of C4.5 and C5.0 Algorithm Classification Tree Models for Analysis of Factors Affecting Auction

Fajri, Mohammad; Utami, Iut Tri; Maruf, Muh.

doi:10.29244/ijsa.v6i1p13-22

Cited by 3 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Decision trees are also able to produce simple decisions from complex ones by turning them into simple ones, and decision trees are very easy to understand when processing small data, which is a method without reducing the quality of the results obtained by using the criteria for each node (Hendra et al, 2020). Fajri et al (2022) The C5.0 algorithm is a more advanced version of the ID3 and C4.5 algorithms. When constructing a decision tree, the algorithm will use the highest information gain value as the root for the next node.…”

Section: Improved Accuracy In Data Mining Decision Tree Classificatio...mentioning

confidence: 99%

Improved Accuracy In Data Mining Decision Tree Classification Using Adaptive Boosting (Adaboost)

Riansyah

Suwilo

Wang

2023

SinkrOn

View full text Add to dashboard Cite

The Decision Tree algorithm is a data mining method algorithm that is often applied as a solution to a problem for a classification. The Decision Tree C5.0 algorithm has several weaknesses, including: the C5.0 algorithm and several other decision tree methods are often biased towards modeling whose features have many levels, some problems for the model can occur such as over-fit or under-fit challenges, big changes to decision logic can result in small changes to data training, C5.0 can experience modeling inconvenience, data imbalance causes low accuracy in C5.0 algorithm. The boosting algorithm is an iterative algorithm that gives different weights to the distribution of training data in each iteration. Each iteration of boosting adds weight to examples of misclassification and decreases weight to examples of correct classification, thereby effectively changing the distribution of the training data. One example of a boosting algorithm is adaboost. The purpose of this research is to improve the performance of the Decision Tree C5.0 classification method using adaptive boosting (adaboost) to predict hepatitis disease using the Confusion matrix. Tests that have been carried out with the Confusion Matrix use the Hepatitis dataset in the Decision Tree C5.0 classification which has an accuracy rate of 80.58% with a classification error rate of 19.15%. Whereas in the Decision Tree C5.0 classification Adaboost has a higher accuracy rate of 82.98%, a classification error rate of 17.02%. This difference is caused by the adaboost algorithm, because the adaboost algorithm is able to change a weak classifier into a strong classifier by increasing the weight of the observations, and adaboost is also able to reduce the classifier error rate.

show abstract

Section: Improved Accuracy In Data Mining Decision Tree Classificatio...mentioning

confidence: 99%

Improved Accuracy In Data Mining Decision Tree Classification Using Adaptive Boosting (Adaboost)

Riansyah

Suwilo

Wang

2023

SinkrOn

View full text Add to dashboard Cite

show abstract

“…The algorithm continues splitting the data until it reaches leaf nodes where the majority class or the predicted class label is assigned. The process of constructing the tree model begins by finding the root node [13]. The calculation process starts by determining the entropy value of each attribute using the following formula: After finding the entropy values of all attributes, the next step is to calculate the splitinfo value and gain ratio value.…”

Section: C50 Algorithmmentioning

confidence: 99%

Implementation of ADABOOST Algorithm on C50 for Improving the Performance of Liver Disease Classification

Anwar Sanusi,

Chrystia Aji Putra,

Fawwaz Ali Akbar

2023

J. Electr. Eng. Comput. Sci.

View full text Add to dashboard Cite

The liver is a vital organ that is important for humans because it plays a role in regulating hormone cycles, neutralizing toxins, and controlling the composition of blood. Liver disease is a common ailment worldwide. Often, this disease occurs without specific symptoms (asymptomatic). Therefore, liver disease is known as a "silent killer," and it is necessary to quickly and accurately diagnose and treat liver diseases. Data mining technology can be useful for rapidly detecting liver diseases from laboratory diagnosis results. One classification algorithm that can be used is the C50 algorithm. This algorithm is an improvement over the previous C45 algorithm, with several advantages such as efficient memory usage and more concise tree results. However, the C50 algorithm may experience overfitting on complex medical data, requiring the boosting process using AdaBoost. The AdaBoost algorithm can make the C50 algorithm more susceptible to overfitting. Another advantage of the AdaBoost algorithm is its ability to handle imbalanced datasets in terms of target labels. This research used 583 data from UCI machine learning with two target labels: liver and normal. The research results show that the C50 algorithm is capable of identification with an accuracy rate of 74.58%. Furthermore, the C50 algorithm's accuracy can be maximized by AdaBoost to reach 86.44%

show abstract

“…Previous research that has been carried out includes research in a comparison of the C4.5, KNN, and Naive Bayes algorithms for determining the classification model for the person in charge of the BSI Entrepreneur Center in 2018. This research uses primary data of 300 records consisting of 12 attributes using the C.45, KNN and Naive Bayes algorithm methods to classify employees according to existing criteria (Fajri et al, 2022;Findawati et al, 2019;Kurnia et al, 2021;Santoso et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Implementation of C4.5 and K-Nearest Neighbor to Predict Palm Oil Fruit Production on Local Plantations

Rahmahwati,

Kirana

2023

jppipa, pendidikan ipa, fisika, biologi, kimia

View full text Add to dashboard Cite

The agricultural sector, especially oil palm plantations, has an important role in the Indonesian economy. Oil palm farming has increased the welfare of farmers and provided employment. In this study, researchers used the C4.5 and K-Nearest Neighbor algorithms in the RapidMiner application to analyze oil palm fruit production data after going through the data preprocessing process. The C4.5 algorithm produces a decision tree with an accuracy of 94.12%, while the KNN algorithm achieves an accuracy of 82.53%. Based on these results, it can be concluded that the C4.5 algorithm has a higher accuracy in classifying oil palm fruit production based on existing attributes.

show abstract

Comparison of C4.5 and C5.0 Algorithm Classification Tree Models for Analysis of Factors Affecting Auction

Cited by 3 publications

References 3 publications

Improved Accuracy In Data Mining Decision Tree Classification Using Adaptive Boosting (Adaboost)

Improved Accuracy In Data Mining Decision Tree Classification Using Adaptive Boosting (Adaboost)

Implementation of ADABOOST Algorithm on C50 for Improving the Performance of Liver Disease Classification

Implementation of C4.5 and K-Nearest Neighbor to Predict Palm Oil Fruit Production on Local Plantations

Contact Info

Product

Resources

About