The Application of Classification and Regression Trees Algorithm in the Production Data of Mounter

2011 ◽  
Vol 383-390 ◽  
pp. 4312-4317
Author(s):  
Lu Zhang ◽  
Yan Ling Shang
Keyword(s):  
Data Mining ◽  
Cost Efficiency ◽  
Rapid Development ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Production Data ◽  
Useful Knowledge ◽  
Database Technology ◽  
Classification And Regression ◽  
The Cost

With the rapid development of database technology and the explosive data growth, we urgently need a new smart technology to help us translate data into useful knowledge and information; so data mining generate. In this paper, We use the classification and regression trees algorithm to mine the data supplied by a factory and acquire some knowledge which can promote the efficiency of the production and reduce the cost. It proved that this method can improve production and reduce the cost efficiency.

scholarly journals Perbandingan Metode Klasifikasi Data Mining untuk Nasabah Bank Telemarketing

2020 ◽  
Vol 20 (1) ◽  
pp. 139-148
Author(s):  
Pungkas Subarkah ◽  
Enggar Pri Pambudi ◽  
Septi Oktaviani Nur Hidayah
Keyword(s):  
Data Mining ◽  
Cross Validation ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Naïve Bayes ◽  
University Of California ◽  
Classification And Regression ◽  
F Measure

 Bank merupakan perusahaan yang memiliki data yang besar yang tersimpan di dalam database dan diolah menghasilkan sebuah informasi yang saling berkaitan tentang nasabah. Bank, harus memiliki ide dan terobosan baru guna mengetahui kendala pada nasabah telemarketing yang ingin melakukan deposito pada Bank tersebut, agar Bank terhindar dari ancaman krisis keuangan. Penelitian ini menguji keberhasilan Bank telemarketing dengan cara melakukan klasifikasi keputusan nasabah dengan menerapkan data mining. Metode yang di gunakan algoritma Classification and Regression Trees (CART) dan naive bayes menggunakan dataset diambil dari University of California Irvine (UCI) Repository Learning. Adapun metode validasi dan evaluasi yang digunakan yaitu 10-cross validation dan confusion matrix. Hasil akurasi pada algoritma CART yaitu 89.51% dengan nilai precision 87%, Recall 89% dan F-Measure 88% dan pada algoritma naive bayes mendapatkan nilai akurasi sebesar 86.88% dengan nilai precision 87%, Recall 86% dan F-Measure 87%. Dari hasil tersebut dapat disimpulkan bahwa algoritma CART lebih baik dalam memprediksi keputusan nasabah telemarketing tepat dalam penawaran deposito.

scholarly journals Komparasi Akurasi Algoritme CART Dan Neural Network Untuk Diagnosis Penyakit Diabetes Retinopathy

2021 ◽  
Vol 7 (1) ◽  
pp. 121
Author(s):  
Pungkas Subarkah ◽  
Muhammad Marshal Abdallah ◽  
Septi Oktaviani Nur Hidayah
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Cross Validation ◽  
Confusion Matrix ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Diabetes Retinopathy ◽  
Classification And Regression ◽  
F Measure ◽  
Diabetes Melitus

Penyakit Diabetes Retinopathy atau DR adalah salah satu komplikasi mikrovaskular diabetes melitus dengan angka prevalensi yang cukup tinggi yang bisa menyebabkan kematian. Penderita DR hingga saat ini masih sulit disembuhkan karena mayoritas penderita melakukan pemeriksaan di saat kondisi penyakit telah memasuki tahap berbahaya, hal ini dikarenakan sifat dari penyakit DR ini tidak menunjukkan gejala yang terlihat bila masih pada tahap awal. Penelitian ini menguji  diagnosis penyakit diabetes retinopathy dengan melakukan klasiifikasi menggunakan metode data mining. Metode yang digunakan ialah algoritme Classification And Regression Trees (CART) dan Algoritme Neural Network menggunakan dataset diambil dari UCI Repository Learning diperoleh daro Universitas Debreen, Hongaria. Adapun metode validasi dan evaluasi yang digunakan dalam penelitian ini yaitu 10-cross validation dan confusion matrix. Hasil dari akurasi pada algoritme CART yaitu 63.4231% dengan nilai precision 0.64%, Recall 0.634%, dan F-Measure 0.634%  dan algoritme Neural Network mendapatkankan nilai akurasi sebesar 72.285% dengan nilai precision 0.723%, Recall 0.723%, dan F-Measure 0.723%. Dari hasil tersebut dapat disimpulkan bahwa algoritme Neural Network lebih baik dalam mendiagnosis penyakit diabetes retinopathy. Kata kunci— Klasifikasi, Diagnosis, Diabetes Retinopathy, Algoritme, CART, Neural Network 

scholarly journals Data Mining untuk Klasifikasi dengan Algoritma Cart (Classification and Regression Trees)

2018 ◽  
Vol 17 (3) ◽  
pp. 114-124
Author(s):  
Rini Astuti
Keyword(s):  
Data Mining ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Classification And Regression

Banyaknya data hasil keluaran dari sebuah sistem informasi dapat menumpuk tidak berguna selama bertahun-tahun, dapat menjadi informasi yang lebih bermakna dengan menggunakan data mining. Data mining merupakan proses mengekstrak sekumpulan data yang dapat menambah nilai informasi berdasarkan keteraturan atau kecenderungan pola tertentu.Salah satu teknik proses pada data mining adalah klasifikasi. Klasifikasi bertujuan untuk mengelompokkan kumpulan data berdasarkan pola atau kriteria tertentu sesuai dengan kebutuhan. Algoritma CART (Classification And Regression Trees) mengelompokkan data dengan cara membagi dua (biner) sehingga menghasilkan sebuah pohon keputusan.

scholarly journals ARTIFICIAL INTELLIGENCE DALAM PENANGANAN KOMPLAIN PELANGGAN PADA INDUSTRI COATING

ICIT Journal ◽  
2015 ◽  
Vol 1 (1) ◽  
pp. 86-97
Author(s):  
Langgeng Listiyoko ◽  
Listina Nadhia N ◽  
Indri Handayani
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Knowledge Base ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Knowledge Engineer ◽  
Customer Complaint ◽  
Customer Care ◽  
Classification And Regression

Banyak perusahaan saat ini memikirkan cara untuk memelihara hubungan baik mereka dengan para pelanggannya. Bukan hanya masalah loyalitas, meraih lebih banyak pelanggan juga menjadi tantangan besar. Calon pelanggan mencari perusahaan dengan reputasi yang bagus dengan cara melihat keberadaan customer care atau layanan pelanggan. Apabila terjadi masalah / komplain maka tim inilah yang selanjutnya bertanggung jawab. Bagaimana tim ini bekerja menyelesaikan masalah akan menjadi pertimbangan khusus untuk pelanggan apakah akan melakukan transaksi selanjutnya atau tidak. Teknologi informasi berperan dalam membangun sistem ini yaitu dengan implementasi Artificial Intelligence. Salah satu metode dari konsep data mining yang sesuai adalahpohon keputusan CART (Classification And Regression Trees). Diperlukan data latihan yang diperoleh dari setiap cabang untuk selanjutnya digunakan perekayasa pengetahuan untuk membangun knowledge base. Kata kunci : customer complaint handler, artificial intelligence, CART, datawarehouse,knowledge base, knowledge engineer

Preoperative hematocrit and platelet count are associated with blood loss during spinal fusion for children with neuromuscular scoliosis

2021 ◽  
pp. 175045892096263
Author(s):  
Margaret O Lewen ◽  
Jay Berry ◽  
Connor Johnson ◽  
Rachael Grace ◽  
Laurie Glader ◽  
...  
Keyword(s):  
Platelet Count ◽  
Blood Loss ◽  
Spinal Fusion ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Neuromuscular Scoliosis ◽  
Independent Variables ◽  
Estimated Blood Loss ◽  
Laboratory Results ◽  
Classification And Regression

Aim To assess the relationship of preoperative hematology laboratory results with intraoperative estimated blood loss and transfusion volumes during posterior spinal fusion for pediatric neuromuscular scoliosis. Methods Retrospective chart review of 179 children with neuromuscular scoliosis undergoing spinal fusion at a tertiary children’s hospital between 2012 and 2017. The main outcome measure was estimated blood loss. Secondary outcomes were volumes of packed red blood cells, fresh frozen plasma, and platelets transfused intraoperatively. Independent variables were preoperative blood counts, coagulation studies, and demographic and surgical characteristics. Relationships between estimated blood loss, transfusion volumes, and independent variables were assessed using bivariable analyses. Classification and Regression Trees were used to identify variables most strongly correlated with outcomes. Results In bivariable analyses, increased estimated blood loss was significantly associated with higher preoperative hematocrit and lower preoperative platelet count but not with abnormal coagulation studies. Preoperative laboratory results were not associated with intraoperative transfusion volumes. In Classification and Regression Trees analysis, binary splits associated with the largest increase in estimated blood loss were hematocrit ≥44% vs. <44% and platelets ≥308 vs. <308 × 109/L. Conclusions Preoperative blood counts may identify patients at risk of increased bleeding, though do not predict intraoperative transfusion requirements. Abnormal coagulation studies often prompted preoperative intervention but were not associated with increased intraoperative bleeding or transfusion needs.

scholarly journals Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model

2021 ◽  
Vol 13 (12) ◽  
pp. 2300
Author(s):  
Samy Elmahdy ◽  
Tarig Ali ◽  
Mohamed Mohamed
Keyword(s):  
Machine Learning ◽  
Regional Scale ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Groundwater Potential ◽  
Machine Learning Algorithms ◽  
Conditioning Factors ◽  
Potential Mapping ◽  
Classification And Regression ◽  
Groundwater Potential Mapping

Mapping of groundwater potential in remote arid and semi-arid regions underneath sand sheets over a very regional scale is a challenge and requires an accurate classifier. The Classification and Regression Trees (CART) model is a robust machine learning classifier used in groundwater potential mapping over a very regional scale. Ten essential groundwater conditioning factors (GWCFs) were constructed using remote sensing data. The spatial relationship between these conditioning factors and the observed groundwater wells locations was optimized and identified by using the chi-square method. A total of 185 groundwater well locations were randomly divided into 129 (70%) for training the model and 56 (30%) for validation. The model was applied for groundwater potential mapping by using optimal parameters values for additive trees were 186, the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps are effective at enhancing the mapping of groundwater potential over a very regional scale obtained using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert.

Fault Diagnosis of Automobile ECUs with Data Mining Technologies

2010 ◽  
Vol 40-41 ◽  
pp. 156-161 ◽  
Author(s):  
Yang Li ◽  
Yan Qiang Li ◽  
Zhi Xue Wang
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Data Stream ◽  
Electronic Control Unit ◽  
Rapid Development ◽  
Reliability And Validity ◽  
Control Unit ◽  
Use Of Data ◽  
The Cost

With the rapid development of automotive ECUs(Electronic Control Unit), the fault diagnosis becomes increasingly complicated. And the link between fault and symptom becomes less obvious. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on data mining technologies. By making full use of data stream, we firstly extract fault symptom vectors by processing data stream, and then establish a diagnosis decision tree through the ID3 decision tree algorithm, and finally store the link rules between faults and the related symptoms into historical fault database as a foundation for the fault diagnosis. The database provides the basis of trend judgments for a future fault. To verify this approach, an example of diagnosing faults of entertainment ECU is showed. The test result testifies the reliability and validity of this diagnostic method and reduces the cost of ECU diagnosis.

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