Sistem Pakar Pemilihan Calon Debitur Kredit Motor Dengan Algoritma C4.5 Pada PT.Federal International Finance

Abstrak: Kredit adalah kepercayaan yang memungkinkan satu pihak untuk memberikan uang atau sumber daya kepada pihak lain di mana pihak kedua mempunyai perjanjian untuk segera mengembalikan uang pihak pertama di kemudian hari, Dalam definisi istilah yang pertama dan paling umum, kredit mengacu pada kesepakatan untuk membeli barang atau jasa dengan janji tegas untuk membayarnya nanti, meskipun begitu dapat dilihat masih banyak konsumen yang memiliki kredit macet seperti tunggakan dan hal lainnya yang mengakibatkan tidak dapat melanjutkan pembayaran kredit.Dalam proses pembiayaan yang dilakukan oleh FIFGROUP, terjadi kendala yaitu masih banyak konsumen yang mengalami kredit macet yang mengakibatkan sepeda motor harus ditarik oleh perusahaan. oleh sebab itu ,penelitian ini dibuat untuk menjadi salah satu solusi dari permasalahan yang dihadapi oleh FIFGROUP,yaitu dengan cara diminimalisir dengan pengimplementasian metode algoritma C4.5, yaitu pembuatan aplikasi system pakar yang dapat melakukan analisa terhadap data-data rekapitulasi pembayaran konsumen yang selanjutnya dapat dijadikan acuan terhadap seleksi calon debitur yang mengajukan kredit.sistem pakar yang dipadukan dengan data mining berupa algoritma C4.5 diharapkan dapat digunakan untuk membantu memecahkan permasalahan dalam berbagai bidang, salah satunya adalah klasifikasi calon debitur kredit.   Kata kunci: algoritma C4.5, sistem pakar, debitor kredit motor   Abstract: Credit is an alternative for some people in making purchases, especially the purchase of motorized vehicles. Credit has also become a source of income for several banks or private companies and agencies that lease credit services to consumers, although it can be seen that there are still many consumers who have bad credit such as arrears and other things that result in being unable to continue credit payments. by FIFGROUP, there is an obstacle, namely that there are still many consumers who experience bad credit which causes motorbikes to be pulled by the company. Therefore, this research was made to be one of the solutions to the problems faced by FIFGROUP, namely by minimizing it by implementing the C4.5 algorithm method, which is the creation of an expert system application that can analyze data on consumer payment recapitulation which can then be obtained. used as a reference for the selection of prospective debtors who apply for credit. The expert system combined with data mining in the form of the C4.5 algorithm is expected to be used to help solve problems in various fields, one of which is the classification of potential credit debtors..   Keywords: C4.5 algorithm, expert system, motor credit debto

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Marketing relations and communication infrastructure development in the banking sector based on big data mining

Purpose: The article aims to study the methodological tools for applying the technologies of intellectual analysis of big data in the modern digital space, the further implementation of which can become the basis for the marketing relations concept implementation in the banking sector of the Russian Federation‘ economy. Structure/Methodology/Approach: For the marketing relations development in the banking sector in the digital economy, it seems necessary: firstly, to identify the opportunities and advantages of the big data mining in banking marketing; secondly, to identify the sources and methods of processing big data; thirdly, to study the examples of the big data mining successful use by Russian banks and to formulate the recommendations on the big data technologies implementation in the digital marketing banking strategy. Findings: The authors‘ analysis showed that big data technologies processing of open online and offline sources of information significantly increases the data amount available for intelligent analysis, as a result of which the interaction between the bank and the target client reaches a new level of partnership. Practical Implications: Conclusions and generalizations of the study can be applied in the practice of managing financial institutions. The results of the study can be used by bank management to form a digital marketing strategy for long-term communication. Originality/Value: The main contribution of this study is that the authors have identified the main directions of using big data in relationship marketing to generate additional profit, as well as the possibility of intellectual analysis of the client base, aimed at expanding the market share and retaining customers in the banking sector of the economy.peer-reviewe

Application of artificial neural network in market segmentation: A review on recent trends

Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table

Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection

Currently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer’s measurement equipment. These types of losses are called non-technical losses (NTLs), and these losses are usually greater than the losses that are due to the distribution infrastructure (technical losses). Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is based on the knowledge and expertise of the inspectors and that uses text mining, neural networks, and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques were used to extract information from samples, and this information was translated into rules, which were joined to the rules that were generated by the knowledge of the inspectors. This system was tested with real samples that were extracted from Endesa databases. Endesa is one of the most important distribution companies in Spain, and it plays an important role in international markets in both Europe and South America, having more than 73 million customers

Consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/

Operations research in consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking both because of the amount of money being lent and the impact of such credit on the global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring,-the way of assessing risk in consumer finance- and what is meant by a credit score. It then outlines ten challenges for Operational Research to support modelling in consumer finance. Some of these are to developing more robust risk assessment systems while others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer financ

Support Vector Machines for Credit Scoring and discovery of significant features

The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit scoring for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default. 1