Predicting Relegation Clubs in Italian Serie A with Method based C4.5 Decision Tree Algorithm

Syukmana, F.; Wahyudi, Ervan; Gata, Windu; Wahono, Hermanto; Febianto, N. I.; Kuntoro, Antonius Yadi; Nitra, Ridatu Oca; Effendi, Lasman; Saputra, Dedi; Sulaeman, O. R.

doi:10.1088/1742-6596/1471/1/012016

Cited by 5 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The decision tree algorithm being evaluated at this point is the C4.5 algorithm [7] and the Random Forest [22]. What is being done in each model is to make a fold and use the best number of folds to assess the validity using 5-fold cross-validation in the model [23,24]. The C4.5 algorithm and the Random Forest recursively visit each decision node, selecting the optimal branch until no more branches are generated.…”

Section: K-fold Cross Validationmentioning

confidence: 99%

Comparison of Decision Tree Classification Methods and Gradient Boosted Trees

Dikananda¹,

Jumini²,

Tarihoran³

et al. 2022

TEM Journal

View full text Add to dashboard Cite

The purpose of this research is to analyze the C4.5 and Random Forest algorithms for classification. The two methods were compared to see which one in the classification process was more accurate. The case is the success of university students at one of the private universities. Data is obtained from the https://osf.io/jk2ac data set. The attributes used were gender, student, average evaluation (NEM), reading session, school origin, and presence as input and success as a result (label). The process of analysis uses Rapid Miner software with the same test parameters (k-folds = 2, 3, 4, 5) with the same type of sample (stratified sample, linear sample, shuffled sampling). The first result shows that the sample type test k-fold (stratified sampling) achieved an average accuracy of 55.76 percent (C4,5) and 5618 percent (Random Forest). The second result showed that the kfold (linear sampling) sample test achieved an average precision of 58.06 percent (C4.5) and 6506 percent. (Random Forest).The third result shows that the k-fold test with the sampling type has averaged 58.68 per cent (C4,5) and 60,76 per cent (shuffled sampling) precision (Random Forest). From the three test results, in the case of student success at a private university, the Random Forest method is better than C4.5.

show abstract

Section: K-fold Cross Validationmentioning

confidence: 99%

Comparison of Decision Tree Classification Methods and Gradient Boosted Trees

Dikananda¹,

Jumini²,

Tarihoran³

et al. 2022

TEM Journal

View full text Add to dashboard Cite

show abstract

“…Dalam penelitian ini dataset yang akan dipilih adalah data sekunder yang telah disediakan oleh situs penyedia repository. Berikut langkahlangkah seleksi data kecelakaan [21]:…”

Section: A Selectionunclassified

Integrasi Metode Decision Tree dan SMOTE untuk Klasifikasi Data Kecelakaan Lalu Lintas

Franseda

Kurniawan

Anggraeni

et al. 2020

justin

View full text Add to dashboard Cite

Kecelakaan lalu lintas merupakan suatu peristiwa yang tidak dapat diprediksi dengan pasti dan dapat mengakibatkan korban jiwa, korban luka ringan, korban luka berat atau kerugian materil seperti benda berharga. Permasalahan ini terjadi di seluruh dunia, tidak terkecuali Australia Selatan yang merupakan salah satu wilayah di Australia. Tercatat bahwa wilayah tersebut memiliki total kecelakaan yang memakan korban 4.953 pada tahun 2018. Oleh karena itu, dibutuhkan analisis untuk mengantisipasi kecelakaan agar tidak terulang kembali kejadian dengan faktor yang sama. Salah satu solusi untuk permasalahan ini yaitu diperlukan metode klasifikasi untuk mengelompokkan faktor-faktor yang mempengaruhi kecelakaan lalu lintas. Metode klasifikasi yang digunakan sebagai pengolah data adalah metode Decision Tree. Metode pada permasalahan ketidakseimbangan kelas menggunakan metode Synthetic MinorityOver-sampling Technique (SMOTE). Untuk proses dalam meningkatkan evaluasi pada penelitian ini menggunakan proses Knowledge Discovery in Database (KDD). Pengujian dilakukan dengan tiga desain model yaitu Split Validation Decision Tree dan SMOTE diperoleh akurasi 69.23%. Pengujian menggunakan Cross Validation Decision Tree dan SMOTE diperoleh akurasi 63.56%. Pengujian menggunakan Decision Tree dan SMOTE Split Data diperoleh akurasi 71.12% dengan perbandingan 1:9. Sehingga, setelah ketiga desain model tersebut dibandingkan, maka Decision Tree dan SMOTE Split Data mendapatkan akurasi yang paling baik. Selain itu diperoleh pula presisi 89.71% (3:7) dan area under curve (AUC) sebesar 0.773 (1:9). Penelitian ini masuk kedalam kategori fair classification (cukup).Traffic accidents are events that cannot be predicted with certainty and can result in casualties, minor injuries, serious injuries, or material losses such as valuable objects. This problem occurs throughout the world, including South Australia which is one of the regions in Australia. It is recorded that the area had a total of 4,953 casualties in 2018. Therefore an analysis is needed to anticipate the accident so that it does not happen again with the same factors. One solution to this problem is the classification method needed to classify the factors that affect traffic accidents. The classification method used for data processing is the Decision Tree method. The method for class imbalance problems uses the method of Synthetic Minority Over-sampling Technique (SMOTE). For the process of increasing evaluation in this study using the Knowledge Discovery in Database (KDD) process. The test was carried out with three model designs namely Split Validation Decision Tree and SMOTE model design obtained an accuracy of 69.23%. Testing using Cross Validation Decision Tree and SMOTE obtained an accuracy of 63.56%. Testing using the Decision Tree and SMOTE Split Data obtained an accuracy 71.12% with ratio of 1:9. So, after the three design models are compared, the split Decision Tree and SMOTE Split Data gets the best accuracy. Also, a precision of 89.71% (3:7) and area under curve (AUC) were obtained of 0.773 (1:9). This research belongs to the fair classification category.

show abstract

“…This relates to the fundamental nature of data mining, which can be used as a reference for analysis to discover unrealized yet significant and meaningful knowledge, patterns, and information (Liao et al, 2022;Maimon & Rokach, 2005;Priyasadie & Isa, 2021;Ramadani et al, 2023). Due to the numerous classifications of the imported data, the decision tree approach is required to break down previously massive data sets into smaller record sets by applying a series of decision rules that can be used to predict or clarify an occurrence (Resti et al, 2023;Rusyana et al, 2023;Syukmana et al, 2020;Yang et al, 2020). In this research, implementing the C4.5 data mining method is anticipated to become an alternate decision support system for creating the necessary data.…”

Section: Introductionmentioning

confidence: 99%

Blood Donation Classification with Decision Tree Method using C4.5 Algorithm

Pangaribuan,

Putra

2024

Int. J. Multidiscipline. Approach. Res. Sci.

View full text Add to dashboard Cite

Donating blood is an altruistic act driven by concern for others and personal commitment to health. It is crucial for patients needing transfusions due to excessive bleeding. However, there has been a decline in blood donations globally. To address this, the medical community needs a method to predict whether a donor will donate again, enabling proactive measures to ensure an adequate blood supply. This study utilizes data from the University of California, Irvine (UCI) Machine Learning Repository, specifically the Blood Transfusion Service Data Set, employing the Decision Tree method with the C4.5 algorithm. C4.5, an improvement over Iterative Dichotomiser 3 (ID3), can handle missing values, pruning, and continuous data. The aim is to classify blood donor eligibility accurately. The aim of this study is to explore how the utilization of the C4.5 algorithm in decision tree classification can predict whether an individual will donate blood again or not. The analysis identifies five key attributes—Recency, Frequency, Monetary, Time (Months), and Decision—as determinants of repeat donation likelihood. Using a confusion matrix to assess accuracy, the C4.5 algorithm achieved a 77.68% accuracy, with an error rate of 22.32%, a sensitivity of 30.19%, and a specificity of 92.40%.

show abstract

Predicting Relegation Clubs in Italian Serie A with Method based C4.5 Decision Tree Algorithm

Cited by 5 publications

References 5 publications

Comparison of Decision Tree Classification Methods and Gradient Boosted Trees

Comparison of Decision Tree Classification Methods and Gradient Boosted Trees

Integrasi Metode Decision Tree dan SMOTE untuk Klasifikasi Data Kecelakaan Lalu Lintas

Blood Donation Classification with Decision Tree Method using C4.5 Algorithm

Contact Info

Product

Resources

About