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.

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

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

Data-Driven Decision Tree Classification for Product Portfolio Design Optimization

2009 ◽  
Vol 9 (4) ◽  
Author(s):  
Conrad S. Tucker ◽  
Harrison M. Kim
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Engineering Design ◽  
Optimization Techniques ◽  
Product Portfolio ◽  
Performance Expectations ◽  
Data Set ◽  
Tree Data ◽  
Portfolio Design ◽  
Product Concepts

The formulation of a product portfolio requires extensive knowledge about the product market space and also the technical limitations of a company’s engineering design and manufacturing processes. A design methodology is presented that significantly enhances the product portfolio design process by eliminating the need for an exhaustive search of all possible product concepts. This is achieved through a decision tree data mining technique that generates a set of product concepts that are subsequently validated in the engineering design using multilevel optimization techniques. The final optimal product portfolio evaluates products based on the following three criteria: (1) it must satisfy customer price and performance expectations (based on the predictive model) defined here as the feasibility criterion; (2) the feasible set of products/variants validated at the engineering level must generate positive profit that we define as the optimality criterion; (3) the optimal set of products/variants should be a manageable size as defined by the enterprise decision makers and should therefore not exceed the product portfolio limit. The strength of our work is to reveal the tremendous savings in time and resources that exist when decision tree data mining techniques are incorporated into the product portfolio design and selection process. Using data mining tree generation techniques, a customer data set of 40,000 responses with 576 unique attribute combinations (entire set of possible product concepts) is narrowed down to 46 product concepts and then validated through the multilevel engineering design response of feasible products. A cell phone example is presented and an optimal product portfolio solution is achieved that maximizes company profit, without violating customer product performance expectations.

A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

2018 ◽  
Vol 22 (3) ◽  
pp. 225-242 ◽  
Author(s):  
K. Mathan ◽  
Priyan Malarvizhi Kumar ◽  
Parthasarathy Panchatcharam ◽  
Gunasekaran Manogaran ◽  
R. Varadharajan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Gini Index ◽  
Mining Method ◽  
Data Mining Method ◽  
Tree Data ◽  
Neural Network Classifiers

scholarly journals PENERAPAN ALGORITMA C4.5 UNTUK PENENTUAN KELAYAKAN PEMBERIAN KREDIT (Studi Kasus : Koperia - Koperasi Warga Komplek Gandaria)

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Teguh Budi Santoso ◽  
Dela Sekardiana
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Classification Model ◽  
Decision Tree Classification ◽  
C4.5 Algorithm ◽  
Credit Worthiness ◽  
Loan Amount

<p><em>Current credit giving in KOPERIA (Koperasi Warga Komplek Gandaria) is still based on an objective process. Difficulties in determining the feasibility of giving credit are often experienced by cooperative managers, so that problems arise in the cooperative is a default payment of credit installments of customers in KOPERIA. This study aims to form a decision tree classification model to determine the customer's credit worthiness. In this study the application of C4.5 Algorithm, based on the Sets and Attributes used in this study, namely, the amount of income divided into 2 categories&gt; 5 million and 3-5 million, the amount of balance divided into three, namely&gt; 3 million, 1-3 million and &lt;1 Million, The Loan Amount is divided into three, namely 1-4 Months, 5-8 months, and 9-12 Months and Requirements with attributes of Business Capital, buying goods and others. In this study determine the appropriate root nodes, the classification results using C4.5 Algorithm shows that the accuracy of 97.5% is obtained, based on the results obtained shows that the c4.5 algorithm is suitable to be used to determine the feasibility of lending customers to KOPERIA.</em></p><p><strong><em>Keywords</em></strong><em>: Data Mining, C4.5 Algorithm</em><em>, loan feasibility</em></p>

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA C4.5 TEHADAP PENGARUH PENJUALAN KOPI PADA PT. JPW INDONESIA

2020 ◽  
Vol 3 (1) ◽  
pp. 40-54
Author(s):  
Ikong Ifongki
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Rules ◽  
Large Data ◽  
Added Value ◽  
Data Set ◽  
Use Of Data ◽  
Decision Tree Classification ◽  
C4.5 Algorithm

Data mining is a series of processes to explore the added value of a data set in the form of knowledge that has not been known manually. The use of data mining techniques is expected to provide knowledge - knowledge that was previously hidden in the data warehouse, so that it becomes valuable information. C4.5 algorithm is a decision tree classification algorithm that is widely used because it has the main advantages of other algorithms. The advantages of the C4.5 algorithm can produce decision trees that are easily interpreted, have an acceptable level of accuracy, are efficient in handling discrete type attributes and can handle discrete and numeric type attributes. The output of the C4.5 algorithm is a decision tree like other classification techniques, a decision tree is a structure that can be used to divide a large data set into smaller sets of records by applying a series of decision rules, with each series of division members of the resulting set become similar to each other. In this case study what is discussed is the effect of coffee sales by processing 106 data from 1087 coffee sales data at PT. JPW Indonesia. Data samples taken will be calculated manually using Microsoft Excel and Rapidminer software. The results of the calculation of the C4.5 algorithm method show that the Quantity and Price attributes greatly affect coffee sales so that sales at PT. JPW Indonesia is still often unstable.

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.

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