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.

A Hybrid Method for Prediction and Assessment Efficiency of Decision Making Units

2013 ◽  
Vol 5 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Iman Rahimi ◽  
Reza Behmanesh ◽  
Rosnah Mohd. Yusuff
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Rules ◽  
Large Data ◽  
Poultry Meat ◽  
Small Data ◽  
Data Set ◽  
Data Mining Techniques ◽  
Decision Making Units

The objective of this article is an evaluation and assessment efficiency of the poultry meat farm as a case study with the new method. As it is clear poultry farm industry is one of the most important sub- sectors in comparison to other ones. The purpose of this study is the prediction and assessment efficiency of poultry farms as decision making units (DMUs). Although, several methods have been proposed for solving this problem, the authors strongly need a methodology to discriminate performance powerfully. Their methodology is comprised of data envelopment analysis and some data mining techniques same as artificial neural network (ANN), decision tree (DT), and cluster analysis (CA). As a case study, data for the analysis were collected from 22 poultry companies in Iran. Moreover, due to a small data set and because of the fact that the authors must use large data set for applying data mining techniques, they employed k-fold cross validation method to validate the authors’ model. After assessing efficiency for each DMU and clustering them, followed by applied model and after presenting decision rules, results in precise and accurate optimizing technique.

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

scholarly journals CUDT: A CUDA Based Decision Tree Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Win-Tsung Lo ◽  
Yue-Shan Chang ◽  
Ruey-Kai Sheu ◽  
Chun-Chieh Chiu ◽  
Shyan-Ming Yuan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
New Technology ◽  
Large Data ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
Tree Algorithm ◽  
Ubiquitous Sensing ◽  
Device Architecture ◽  
Huge Data

Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture), which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5∼55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set.

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 Algoritma C5.0 Untuk Prediksi Kelulusan Pembelajaran Mahasiswa Pada Matakuliah Arsitektur Sistem Komputer

2021 ◽  
Vol 5 (3) ◽  
pp. 1166
Author(s):  
Muchamad Sobri Sungkar ◽  
M Taufik Qurohman
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Extraction Process ◽  
Study Program ◽  
Data Set ◽  
C4.5 Algorithm ◽  
Previous Algorithm ◽  
Process Prediction ◽  
Computer System Architecture

Computer system architecture is one of the subjects that must be taken in the informatics engineering study program. In the study program the graduation of each student in the course is one of the important aspects that must be evaluated every semester. Graduation for each student / I in the course is an illustration that the learning process delivered is going well and also the material presented by the lecturer in charge of the course can be digested by students. Graduation of each student in the course can be predicted based on the habit pattern of the students. Data mining is an alternative process that can be done to find out habit patterns based on the data that has been collected. Data mining itself is an extraction process on a collection of data that produces valuable information for companies, agencies or organizations that can be used in the decision-making process. Prediction of graduation with data mining can be solved by classifying the data set. The C5.0 algorithm is an improvement algorithm from the C4.5 algorithm where the process is almost the same, only the C5.0 algorithm has advantages over the previous algorithm. The results of the C5.0 algorithm are in the form of a decision tree or a rule that is formed based on the entropy or gain value. The prediction process is carried out based on the classification of the C5.0 algorithm by using the attributes of Attendance Value, Assignment Value, UTS Value and UAS Value. The final result of the C5.0 algorithm classification process is a decision tree with rules in it. The performance of the C5.0 algorithm gets a high accuracy rate of 93.33%

scholarly journals Palm oil extraction rate prediction based on the fruit ripeness levels using C4.5 algorithm

2021 ◽  
Vol 13 (2) ◽  
pp. 92-100
Author(s):  
Wahyu Supriyatin
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Palm Oil ◽  
Oil Extraction ◽  
Palm Fruit ◽  
Decision Tree Classification ◽  
C4.5 Algorithm ◽  
Production Value ◽  
High Production

Oil palm plantations are one of the main keys in supporting Indonesia’s economic growth. The rising consumption needs for palm oil products make it necessary to carry out data mining activities to increase CPO production. The maturity factor of palm fruit dramatically affects the quality of the oil extraction content (CPO yield) produced. This study aims to investigate the effect of fruit ripeness on the yield of CPO by using a data mining classification method with a decision tree. The algorithm used to generate decision tree classification is the C4.5 algorithm. The implementation of the C4.5 algorithm in the study was carried out using the Rapid Miner Studio 5.2 tools. The results shows that the yield of CPO is influenced by the attributes of the condition of the long and ripe fruit, the condition of the long and overripe fruit, the normal condition of the fruit and the age of 3-6 years and the condition of the fruit of normal and age of 7-10 years. Decision tree C4.5 algorithm generates 8 rules with 4 rules showing a high production value, which means that the four rules affect the yield of CPO.

scholarly journals Data Mining Application in Predicting Bank Loan Defaulters

2020 ◽  
Vol 9 (4) ◽  
pp. 2733-2744
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Model Building ◽  
Naive Bayes ◽  
Large Data ◽  
Naïve Bayes ◽  
Bank Loan ◽  
Classification Model ◽  
Data Set ◽  
Data Mining Application

Data mining is the key tools for discoveries of knowledge from large data set. Nowadays, most of the organizations using this technology to maintain their data. This paper focuses on the Bank sector in Risk management specifically, detecting Bank loan defaulters through the data mining application to examine the patterns of different attribute which would contribute for detecting and predicting defaulters thus preventing wrong loans. This process can be done without change the current systems and the data. Then it helps to distinguish borrowers who repay loans promptly from those who don’t and avoid wrong loan allotment. In order to show the results of the study Classification model is implemented in order to find interesting patterns among attributes of customer. A total of 20461 sample data were taken by data base admin randomly from 3 consecutive years from the Bank database to build and test the model. In this research we used Classification model of decision tree and Naïve Bayes in Weka 3.7 tool for experiments. Modeling methodology applied to this paper was CIRSP-DM (Cross Industry Standard for Data Mining), which involves business understanding, data understanding, data preparation, model building, evaluation and deployment. Decision tree classifications with J48 implementation with 8 experiments were performed. Two experiments with different parameters were made for Naïve Bayes. Finally, evaluation and analysis of the models were performed then given a best solution to predict the defaulters.

scholarly journals Data mining in the context of urban metabolism: A case study of Geneva and Lausanne, Switzerland

2021 ◽  
Vol 2042 (1) ◽  
pp. 012020
Author(s):  
N S Wiedmann ◽  
A Athanassiadis ◽  
C R Binder
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Large Data ◽  
Mitigation Strategies ◽  
Urban Metabolism ◽  
Multiple Sources ◽  
Data Accessibility ◽  
Current Configuration ◽  
Use Of Data

Abstract The highest share of the global population lives in cities. The current configuration of the latter requires considerable amounts of resource flows causing the degradation of local and global ecosystems. To face the complexity of these challenges, scientists use the concept of urban metabolism (UM), i.e. measuring urban input and output flows from a systemic perspective. This accounting method results in a large data collection from multiple sources that are often not harmonised. Metabolism of Cities Data Hub is an online platform which facilitates data collection, processing and visualisation in order to extract urban metabolism insights. This work highlights the challenges faced when mining urban metabolism data in the case of Lausanne and Geneva, as well as provides insights on how data could be best used from users and providers. Slight differences between the two case studies, in terms of data accessibility and availability where experienced but the main challenges revolved around data copyright, format and availability. As a conclusion, the used tool can enable harmonisation and standardisation of UM data. As such it could contribute to the use of data mining to streamline the environmental monitoring of cities as well as facilitate the creation of mitigation strategies.

scholarly journals Case Studies in Applying Data Mining for Churn Analysis

2017 ◽  
Vol 5 (2) ◽  
pp. 22-33
Author(s):  
Susan Lomax ◽  
Sunil Vadera
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Case Studies ◽  
Decision Tree Learning ◽  
Customer Churn ◽  
Use Of Data ◽  
Churn Analysis ◽  
First Case ◽  
Product Comparison

The advent of price and product comparison sites now makes it even more important to retain customers and identify those that might be at risk of leaving. The use of data mining methods has been widely advocated for predicting customer churn. This paper presents two case studies that utilize decision tree learning methods to develop models for predicting churn for a software company. The first case study aims to predict churn for organizations which currently have an ongoing project, to determine if organizations are likely to continue with other projects. While the second case study presents a more traditional example, where the aim is to predict organizations likely to cease being a subscriber to a service. The case studies include presentation of the accuracy of the models using a standard methodology as well as comparing the results with what happened in practice. Both case studies show the significant savings that can be made, plus potential increase in revenue by using decision tree learning for churn analysis.

Penerapan Algoritma C4.5 pada Analisis Kerusakan Barang Jadi

2016 ◽  
Vol 7 (1) ◽  
pp. 24-28
Author(s):  
Ivan Oktana ◽  
Seng Hansun
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Historical Data ◽  
Large Data ◽  
Data Set ◽  
Damage Data ◽  
Data Explosion ◽  
Index Terms ◽  
Relationship Of ◽  
The Relationship

A Data mining is the activity that includes the collection, the use of historical data to discover regularity, patterns and relationship in a large data set. The usefulness of data mining is to specify a pattern to be found in the data mining task. The presences of data mining is motivated by the problem of data explosion which had been experienced lately these day where many organization or company collect so many years of data (purchasing data, sales data, damage data item, transaction data, and so on). In this paper data mining methods been used to analyze the damage data of finished products, with the goal of producing a pattern of the damage product. Based on the pattern from product’s damage, can be see and the aspects that affect to the damaged product. The purpose of this study is to show information about the relationship of damage data of finished goods using C4.5 algorithms in PT. Kayu Lapis Asli Murni, and display results in the form of a decision tree mining. Index Terms—data mining, C4.5 algorithms, decision tree, damage data of finished products, PT. Kayu Lapis Asli Murni

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