scholarly journals Penerapan Decision Tree Menggunakan Algoritma C4.5 Untuk Deteksi Demam Berdarah Pada RS. IMC Bintaro

2019 ◽  
Vol 5 (1) ◽  
pp. 75-86
Author(s):  
Farid Fadli ◽  
Belsana Butar Butar
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Processing ◽  
Death Rates ◽  
A Value ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Tree Data ◽  
Classification Category ◽  
Tree Method

Abstract: According to the WHO report in 2004, Indonesia is the largest country with the highest number of sufferers and death rates due to dengue fever. If it is not handled properly, the postponed treatment can be fatal. In this study, the authors used the kepuutsan tree method with C4.5 algorithm to process patient data to predict whether patients experienced bloody help regarding existing indications with the help of Rapidminer software. The results of data processing using Rapidminer were evaluated and validated with a confussion matrix and AUC curve, the results of data processing using the C4.5 algorithm had an accuracy of 72% and AUC had a value of 0.758 with a fair classification category. Keywords: Algorithm C4.5, Decision Tree, Data Mining

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.

The Water Conservancy Water Electricity Construction Engineering Professional Result Analysis of Application Base on C4.5 Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 703-707
Author(s):  
Hu Yong
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Construction Engineering ◽  
Improvement Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Selection Decision ◽  
Decision Tree Method ◽  
Result Analysis ◽  
Tree Method

Aimed at the student the result problem, give student the result data scoops out the model. The decision tree method is a very valid classification method, in the data that scoop out. According to student the result data characteristics, adopted the C4.5 decision tree algorithm. C4.5 algorithm is the improvement algorithm of the decision trees core algorithm ID3, it construct in brief, the speed compare quickly, easy realization. Selection decision belongs to sex, scoop out the result enunciation, that algorithm can be right to get student the result data classification, and some worthy conclusion, provide the decision the analysis.

scholarly journals Penerapan Metode Algoritma C4.5 dalam Menganalisa Pegajuan Kredit pada Koperasi Jasa Keuangan Syariah Kelurahan Limau Manis Selatan

2018 ◽  
Vol 7 (1) ◽  
pp. 28-42
Author(s):  
Febri Hadi
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Processing ◽  
Financial Services ◽  
Rapid Development ◽  
Simple Representation ◽  
Classification Technique ◽  
C4.5 Algorithm ◽  
Processing Techniques ◽  
Credit Analysis

The development of data processing techniques at this time has experienced rapid development. The Decision tree is a simple representation of a classification technique that is the process of teaching a function of purpose that maps each set of first attributes of a class defined previously. The decision tree can determine the hidden relationship between a number of potential target variables. In lending to customers, credit analysis is required for lending. The analysis of the kerdit can be done by utilizing data mining in the form of C4.5 algorithm. The C4.5 algorithm is used to provide credit decisions in order for the Sharia financial services cooperative to quickly analyze the credit application by members of a cooperative. The purpose of this research is to apply C4.5 algorithm method in analyzing credit application at Koperasi Jasa Keuangan Syariah Kelurahan Limau Manis Selatan.

scholarly journals METODE ALGORITMA DECISION TREE C4.5 UNTUK ANALISIS KELAYAKAN KREDIT NASABAH PADA BSM KCP KEMANG PRATAMA

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Dyah Wulandari ◽  
Nur Lutfiyana ◽  
Heny Sumarno
Keyword(s):  
Decision Tree ◽  
Profit Sharing ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Customer Data ◽  
A Value ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Credit Worthiness

Abstract - Credit is the provision of money or equivalent claims, based on agreements or agreements on loans between banks and other parties which require the borrowing party to repay the debt after a certain period of time with the amount of interest, compensation or profit sharing. From the credit customer data available at BSM KCP Kemang Pratama still has Non Performing Financing (NPF) or Bad Credit.In analyzing a credit sometimes an analyst does an inaccurate analysis, so there are some customers who are less able to make credit payments, resulting in bad credit. So the researchers conducted an analysis using the C4.5 decision tree algorithm and Rapid Miner application for determining credit worthiness. From the analysis of credit customer data using the C4.5 decision tree algorithm method, the feasibility of credit recipient customers is very effective and produces a value of accuracy on Rapid Miner 5.3 of 80%, Precision of 100% and Recall of 0% so as to minimize the risk.Keywords— Credit, C4.5 Algorithm, Rapid Miner, Value AccuracyAbstrak - Kredit merupakan penyediaan uang atau tagihan yang dapat disamakan dengan hal itu, berdasarkan persetujuan atau kesepakatan pinjaman-pinjaman antara bank dengan pihak lain yang mewajibkan pihak peminjam untuk melunasi utangnya setelah jangka waktu tertentu dengan jumlah bunga, imbalan atau pembagian hasil keuntungan. Dari data nasabah kredit yang ada pada BSM KCP Kemang Pratama masih memiliki Non Performing Financing (NPF) atau Kredit Macet. Dalam menganalisa sebuah kredit terkadang seorang analis melakukan analisa tidak akurat, sehingga ada beberapa nasabah yang kurang mampu dalam melakukan pembayaran kredit, dan pada akhirnya mengakibatkan kredit macet. Peneliti melakukan analisis menggunakan algoritma decision tree C4.5 dan aplikasi Rapid Miner untuk penentuan kelayakan pemberian kredit. Dari analisis data nasabah kredit menggunakan metode Algoritma decision tree C4.5 menghasilkan kelayakan nasabah penerima kredit sangat efektif dan menghasilkan nilai akurasi pada Rapid Miner 5.3 sebesar 80%, Precision sebesar 100% dan Recall sebesar 0% sehingga dapat meminimalisir resiko yang terjadi.Kata kunci— Kredit, Algoritma C4.5, Rapid Miner, Nilai Akurasi

scholarly journals Prediksi Mahasiswa Berpotensi Non Aktif Menggunakan Data Mining dalam Decision Tree dan Algoritma C4.5

2020 ◽  
pp. 23-29
Author(s):  
Nur Yanti Lumban Gaol
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Dropping Out ◽  
Mining Science ◽  
Prediction Rules ◽  
Tertiary Institutions ◽  
Student Data ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
Tree Method

Non-active students are students who do not attend the lecture process and do not pay tuition administration fees within two semesters or more. Reports on students who are not active will have an impact on the quantity of tertiary institutions. Students who are not registered in non-active students will potentially be expelled or dropped out. For this reason, this research was conducted to explore information on potentially non-active students by applying data mining science with the Decision Tree method and C4.5 algorithm. The tested data were sourced from Triguna Dharma Medan College of Information and Computer Management (STMIK). The results of the study get prediction rules for student data that are potentially non-active with a very good degree of accuracy. So this research can be used to avoid students dropping out unilaterally.

scholarly journals Prediksi Mahasiswa Berpotensi Non Aktif Menggunakan Data Mining dalam Decision Tree dan Algoritma C4.5

2019 ◽  
Vol 1 (4) ◽  
pp. 40-46
Author(s):  
Nur Yanti Lumban Gaol
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Dropping Out ◽  
Mining Science ◽  
Prediction Rules ◽  
Tertiary Institutions ◽  
Student Data ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
Tree Method

Non-active students are students who do not attend the lecture process and do not pay tuition administration fees within two semesters or more. Reports on students who are not active will have an impact on the quantity of tertiary institutions. Students who are not registered in non-active students will potentially be expelled or dropped out. For this reason, this research was conducted to explore information on potentially non-active students by applying data mining science with the Decision Tree method and C4.5 algorithm. The tested data were sourced from Triguna Dharma Medan College of Information and Computer Management (STMIK). The results of the study get prediction rules for student data that are potentially non-active with a very good degree of accuracy. So this research can be used to avoid students dropping out unilaterally.

scholarly journals C4.5 Versus Other Decision Trees: A Review

2015 ◽  
Vol 4 (3) ◽  
pp. 173-182
Author(s):  
Salih Özsoy ◽  
Gökhan Gümüş ◽  
Savriddin KHALILOV
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Study Data ◽  
Classification Algorithms ◽  
Mining Method ◽  
Decision Tree Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Tree Algorithms ◽  
The One

In this study, Data Mining, one of the latest technologies of the Information Systems, was introduced and Classification a Data Mining method and the Classification algorithms were discussed. A classification was applied by using C4.5 decision tree algorithm on a dataset about Labor Relations from http://archive.ics.uci.edu/ml/datasets.html. Finally, C4.5 algorithm was compared to some other decision tree algorithms. C4.5 was the one of the successful classifier.

scholarly journals Prediksi Target Sistem Productivity Services menggunakan Penerapan Algoritma C4.5 pada PT. Sanggar Sarana Baja Jakarta

2021 ◽  
Vol 5 (1) ◽  
pp. 1-13
Author(s):  
Anis Rahmawati ◽  
Syifa Nur Rakhmah ◽  
Lusa Indah Prahartiwi
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Service Provider ◽  
Target Level ◽  
C4.5 Algorithm ◽  
Service Productivity ◽  
Tree Data ◽  
Efficiency And Productivity ◽  
Productivity System ◽  
Accuracy Validation

AbstractThere are many ways that each service provider company does, especially services to win the competition, among others, by increasing service productivity targets. One service provider company that is committed to increasing service productivity targets is PT. Sanggar Sarana Baja. This study aims to predict service productivity system targets using the application of Algortima C4.5 at PT. Sanggar Sarana Baja. The attributes of working time input in this study include area, performance, efficiency, and productivity. In this study, it was found that the results obtained came from several input attributes which resulted in a causal relationship in classifying the results of service productivity targets at PT. Sanggar Sarana Baja. This research is expected to help PT. Sanggar Sarana Baja in increasing customer satisfaction to retain customers and increase profits of PT. Sanggar Sarana Baja. Based on the classification results using the C4.5 Algorithm, it shows that the accuracy reaches 95.00%, which indicates that the C4.5 algorithm is suitable for measuring the target level at PT. Sanggar Sarana Baja. Keywords: Accuracy, Validation, Decision Tree, Data mining, KDD, C4.5 Algorithm, Services Companies, Target Services Productivity Systems  Banyak cara yang dilakukan oleh masing - masing perusahaan penyedia jasa, khususnya servis untuk memenangkan persaingan, antara lain dengan meningkatkan target produktivitas servis. Salah satu perusahaan penyedia jasa servis yang berkomitmen dalam meningkatkan target produktivitas servis adalah PT. Sanggar Sarana Baja. Penelitian ini bertujuan untuk memperdiksi target sistem produktivitas servis menggunakan penerapan Algoritma C4.5 pada PT. Sanggar Sarana Baja. Atribut masukan waktu kerja dalam penelitian ini mencangkup daerah, kinerja, efisiensi, dan produktivitas.  Dalam penelitian ini, didapatkan bahwa hasil yang didapatkan berasal dari beberapa atribut masukan menghasilkan hubungan sebab -akibat dalam mengklasifikasikan hasil dari target produktivitas servis pada PT. Sanggar Sarana Baja. Penelitian ini diharapkan dapat membantu pihak PT. Sanggar Sarana Baja dalam meningkatkan kepuasan konsumen untuk mempertahankan pelanggan dan meningkatkan laba PT. Sanggar Sarana Baja tersebut. Berdasarkan hasil klasifikasi menggunakan Algoritma C4.5 menunjukkan bahwa diperoleh akurasi mencapai 95,00%, yang menunjukkan bahwa algoritma C4.5 cocok digunakan untuk mengukur tingkat target pada PT. Sanggar Sarana Baja. Kata kunci: Akurasi, Validasi, Decision Tree, Data mining, KDD, Algoritma C4.5, Perusahaan Jasa, Target Sistem Productivity ServicesReferensi[1]        Yulia and N. Azwanti, “Data Mining Prediksi Besarnya Penggunaan Listrik Rumah Tangga di Kota Batam Dengan Menggunakan Algoritma C4.5,” Semin. Nas. Ilmu Sos. dan Teknol., vol. 1, no. 1, pp. 175–180, 2018.[2]      R. Novita, “Teknik Data Mining?: Algoritma C 4 . 5,” pp. 1–12, 2016.

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

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