PERBANDINGAN KLASIFIKASI ALGORITMA C5.0 DENGAN CLASSIFICATION AND REGRESSION TREE (STUDI KASUS: DATA SOSIAL KEPALA KELUARGA MASYARAKAT DESA TELUK BARU KECAMATAN MUARA ANCALONG TAHUN 2019)

Decision tree is a algorithm used as a reasoning procedure to get answers from problems are entered. Many methods can be used in decision trees, including the C5.0 algorithm and Classification and Regression Tree (CART). This research aims to determine the classification results of the C5.0 and CART algorithms and to determine the comparison of the accuracy classification results from these two methods. The variables used in this research are the average monthly income (Y), employment (X1), number of   family members (X2), last education  (X3)  and  gender  (X4).  After analyzing the results obtained that the accuracy rate of C5.0 algorithm is 79,17% while the accuracy rate of CART is 84,63%. So it can be said that the CART method is a better method in classifying the average income of the people of Teluk Baru Village in Muara Ancalong District in 2019 compared to the C5.0 algorithm metho

Deteksi Dini Gejala Kanker Serviks Menggunakan CART (Classification And Regression Tree)

Sebanyak 76,6% pasien kanker serviks di Indonesia ketika terdeteksi kanker serviks ternyata sudah memasuki stadium lanjut karena kanker serviks tidak memiliki gejala apapun pada stadium awal sehingga sangat penting untuk mengenal dan mengetahui gejala kanker serviks sejak dini agar segera melakukan penanganan medis yang tepat. Melalui sistem deteksi dini gejala kanker serviks diharapkan pengguna sistem dapat mengetahui hasil prediksi dari gejala yang sedang dialami dimana gejala tersebut menjadi faktor resiko kanker serviks sehingga dapat menentukan langkah selanjutnya yang harus ditangani jika mengalami gejala kanker serviks. Penelitian ini menerapkan algoritma CART yang akan mencari semua kemungkinan variabel dan nilai dari data set untuk menemukan split yang paling baik dan menghasilkan ketepatan klasifikasi yang tinggi. Sistem dibangun dengan menggunakan data set gejala kanker serviks dari penelitian sebelumnya yang melewati tahapan feature selection, diskritisasi 2 bin, dan konversi extensi file. Pengukuran performansi sistem dilakukan dengan teknik split percentage dan cross validation dengan nilai akurasi dan F-Score yang diperoleh bernilai cukup bagus mencapai lebih dari 98% dan menunjukkan bahwa model yang dihasilkan algoritma CART untuk melakukan deteksi dini gejala kanker serviks memiliki kemungkinan yang besar untuk memprediksi dengan benar. Kata Kunci: klasifikasi, CART, akurasi, F-score, kanker serviks

The process and utility of classification and regression tree methodology in nursing research

Aim: This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Background: Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Design: Discussion paper. Data sources: English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984-2013. Discussion: Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Implications for Nursing Research: Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Conclusion: Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions

Prediksi Google Search Engine Result Page (SERP) Menggunakan Classification and Regression Tree (CART)

Pertumbuhan pesat internet beberapa tahun terakhir memunculkan berbagai macam media online seperti website, blog, dan social media. Dari waktu ke waktu jumlah website yang ada di dunia semakin banyak. Website menjadi salah satu media informasi, hiburan, promosi dan lain-lain. Salah satu indikator dari suksesnya sebuah website adalah trafik. Trafik dapat berasal dari berbagai macam sumber, yang paling dominan adalah trafik yang berasal dari search engine. Penelitian dalam tugas akhir ini bertujuan untuk mencari parameter penting sebuah halaman web dalam Google search engine result page (SERP). Metode yang digunakan dalam penelitian ini adalah Classification and Regression Trees (CART) untuk mendapatkan parameter-parameter yang berpengaruh terhadap peringkat hasil pencarian suatu halaman web pada Google SERP. Data yang digunakan adalah hasil pencarian 25 kata kunci atau keyword yang masing-masing hasil pencarian halaman web tersebut memiliki parameter-parameter. Parameter dari data tersebut lalu dimodelkan dengan Classification and Regression Trees dengan bantuan software Matlab. Dari hasil matlab diperoleh 2 parameter yaitu Page Authority dan Domain Authority. Kata kunci: SERP, CART, PA, D

Credit Scoring Menggunakan Algoritma Classification and Regression Tree (CART)

Credit Scoring, adalah proses penilaian permohonan kredit yang dilakukan oleh lembaga kreditur/ pihak yang memberikan kredit kepada debitur selaku penerima kredit tersebut. Keluaran dari credit scoring adalah layak atau tidaknya calon debitur tersebut untuk menerima kredit. Tahapan ini adalah tahapan yang paling penting didalam proses kredit. Kesalahan di dalam tahapan ini akan berdampak besar pada keseluruhan tahapan pemberian kredit, dan secara global berpengaruh terhadap lembaga itu sendiri. Bidang ilmu dari teknologi informasi, yang bisa membantu credit scoring adalah data mining. Salah satu algoritma yang bisa digunakan di dalam data mining adalah Classification And Regresion Tree (CART). Penggunaan algoritma ini untuk credit scoring akan menghemat waktu, USAha dan biaya serta dengan cepat, tepat dan efektif menganalisis kelayakan calon debitur. Model yang dibentuk dari algoritma CART di domain credit scoring memberikan rata-rata tingkat akurasi sebesar 75,20 % dan dikategorikan sebagai fair classification

A SUMMARY OF Classification and Regression Tree WITH APPLICATION

Classification and regression tree (CART) is a non-parametric methodology that was introduced first by Breiman and colleagues in 1984. CART is a technique which divides populations into meaningful subgroups that allows the identification of groups of interest. CART as a classification method constructs decision trees. Depending on information that is available about the dataset, a classification tree or a regression tree can be constructed. The first part of this paper describes the fundamental principles of tree construction, pruning procedure and different splitting algorithms. The second part of the paper answers the questions why or why not the CART method should be used or not. The advantages and weaknesses of the CART method are discussed and tested in detail. Finally, CART is applied to an example with real data, using the statistical software R. In this paper some graphical and plotting tools are presented

NCART: Neural Classification and Regression Tree for Tabular Data

Deep learning models have become popular in the analysis of tabular data, as they address the limitations of decision trees and enable valuable applications like semi-supervised learning, online learning, and transfer learning. However, these deep-learning approaches often encounter a trade-off. On one hand, they can be computationally expensive when dealing with large-scale or high-dimensional datasets. On the other hand, they may lack interpretability and may not be suitable for small-scale datasets. In this study, we propose a novel interpretable neural network called Neural Classification and Regression Tree (NCART) to overcome these challenges. NCART is a modified version of Residual Networks that replaces fully-connected layers with multiple differentiable oblivious decision trees. By integrating decision trees into the architecture, NCART maintains its interpretability while benefiting from the end-to-end capabilities of neural networks. The simplicity of the NCART architecture makes it well-suited for datasets of varying sizes and reduces computational costs compared to state-of-the-art deep learning models. Extensive numerical experiments demonstrate the superior performance of NCART compared to existing deep learning models, establishing it as a strong competitor to tree-based models

A multifactorial approach for understanding fall risk in older people

OBJECTIVE: To identify the interrelationships and discriminatory value of a broad range of objectively measured explanatory risk factors for falls. DESIGN: Prospective cohort study with 12-month follow-up period. SETTING: Community sample. PARTICIPANTS: Five hundred community-dwelling people aged 70 to 90. MEASUREMENTS: All participants underwent assessments on medical, disability, physical, cognitive, and psychological measures. Fallers were defined as people who had at least one injurious fall or at least two noninjurious falls during a 12-month follow-up period. RESULTS: Univariate regression analyses identified the following fall risk factors: disability, poor performance on physical tests, depressive symptoms, poor executive function, concern about falling, and previous falls. Classification and regression tree analysis revealed that balance-related impairments were critical predictors of falls. In those with good balance, disability and exercise levels influenced future fall risk-people in the lowest and the highest exercise tertiles were at greater risk. In those with impaired balance, different risk factors predicted greater fall risk-poor executive function, poor dynamic balance, and low exercise levels. Absolute risks for falls ranged from 11% in those with no risk factors to 54% in the highest-risk group. CONCLUSIONS: A classification and regression tree approach highlighted interrelationships and discriminatory value of important explanatory fall risk factors. The information may prove useful in clinical settings to assist in tailoring interventions to maximize the potential benefit of falls prevention strategies

Differences in Risk Factors for Rotator Cuff Tears between Elderly Patients and Young Patients

It has been unclear whether the risk factors for rotator cuff tears are the same at all ages or differ between young and older populations. In this study, we examined the risk factors for rotator cuff tears using classification and regression tree analysis as methods of nonlinear regression analysis. There were 65 patients in the rotator cuff tears group and 45 patients in the intact rotator cuff group. Classification and regression tree analysis was performed to predict rotator cuff tears. The target factor was rotator cuff tears; explanatory variables were age, sex, trauma, and critical shoulder angle≥35°. In the results of classification and regression tree analysis, the tree was divided at age 64. For patients aged≥64, the tree was divided at trauma. For patients aged<64, the tree was divided at critical shoulder angle≥35°. The odds ratio for critical shoulder angle≥35° was significant for all ages (5.89), and for patients aged<64 (10.3) while trauma was only a significant factor for patients aged≥64 (5.13). Age, trauma, and critical shoulder angle≥35° were related to rotator cuff tears in this study. However, these risk factors showed different trends according to age group, not a linear relationship