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Journal Paper Format International Journal of Advanced Science and Technology Vol. 29, No. 5, (2020), pp. 4497 - 4506 Mining Data Analysis Using CRISP-DM to Implement Successfully Multipurpose Financing at Astra Credit Companies (ACC) Branch Bogor Erwin Husein1, Emil. R. Kaburuan*2, Mauritsius Tuga3 Information Systems Management Department, BINUS Graduate Program – Master of Information Systems Management, Bina Nusantara University, Jakarta, Indonesia 11480 [email protected], [email protected], [email protected] Abstract The growing need for life to encourage the emergence of a finance company. This is due to financing has become one of the solutions to help meet the needs of a person's life. Astra Credit Companies (ACC) is a finance company that provides financing in the form of a multipurpose loan. To optimize financing made by the company, required an analysis of customer segmentation. It is intended to allow better targeted financing deals. The focus in this study is used to finance the purchase of secondhand vehicles types. The analysis was conducted by classifying customers based on job data, vehicle type, the installment, the tenor, credit quality and payment. The technique used is the technique of applying clustering with K-means clustering algorithm. The method used is the CRISP- DM. Results from this study is the formation of three groups where each group has its own characteristics. To make offers multipurpose financing through guarantees manifold motor vehicle reg car, the company can offer to potential customers who already do segmentation so easy to do marketing. Keywords: Financing, Clustering, K-means clustering, CRISP-DM 1. Introduction The growing need for life to encourage the emergence of a finance company. This is due to financing has become one of the solutions to help meet the needs of a person's life. Astra Credit Companies (ACC)Astra Credit Companies (ACC) is a finance company Astra, which consists of a combination of four finance company Astra, PT Astra Sedaya Finance, PT Swadharma Bhakti Sedaya Finance, PT Astra Auto Finance, PT Staco aesthetic Sedaya Finance as well as a company engaged in the field of services billing, PT Pratama Sadya Sadhana, ACC stands at ACC stands at 15 July 1982. ACC Network spread across almost all major cities in Indonesia. The ACC currently has 75 branch offices in 59 cities in Indonesia and is still growing. With many branches and a network owned by the ACC, we focus group research on one of the branches from 75 branches owned ACC today. Branch that we select must be representative of the large branches owned by ACC, with the following criteria: • The branch had OSA (Outstanding Amount) over one trillion rupiah. • The branch has several customers is now over ten thousand customers. • The branch has several employees over 50 people. Multipurpose product is part of a financing product that is owned by ACC. Multipurpose product allows customers to get funds directly to ensure only their four- wheeled vehicle, with the intended use of the funds for any purpose. The loan term up to 36 months (3 years) with a loan amount ranging from only 20 million rupiah up to ISSN: 2005-4238 IJAST 4497 Copyright ⓒ 2020 SERSC International Journal of Advanced Science and Technology Vol. 29, No. 5, (2020), pp. 4497 - 4506 hundreds of millions of rupiah per unit funded, multi-purpose products can penetrate the numbers up to 4,000 units per month financing. To optimize financing made by the company, required an analysis of customer segmentation. It is intended to allow better targeted financing deals. Clustering is one of data mining techniques that can be used to perform customer segmentation analysis at Astra Credit Companies (ACC). Grouping customers based on the similarity criteria, customers have many similarities will be grouped into one cluster, while a different customer to put in another cluster. The algorithms used in this study is a k-means clustering algorithm. K-means clustering algorithm is simple and effective algorithm to find clusters in the data [1]. The model used is the Cross-Industry Standard Process for Data Mining (CRISP-DM), which consists of six stages, namely business understanding, understanding the data, the data preparation, modeling, evaluation, and deployment. In his research entitled Credit Risk Analysis Motorcycles on PT.X Finance with a case study branches Gresik and Lamongan), [2] using the k-means clustering algorithm to determine the indicators that influence the credit risk. In addition, the k-means clustering algorithm also used for grouping customers based on indicators of age, employment sector, percent DP, income, type of motor, the motor condition, and the price on the road (OTR). K-means cluster algorithm is also used by [3] in his research entitled Analysis Segmentation, Targeting, Positioning Car Financing at. Adira Dinamika Multifinance, Tbk. Manado branch, In this research, customer segmentation analysis will be done Astra Credit Companies (ACC) in Bogor branch using k-means clustering algorithm. The analysis is focused on the financing of the type of motor vehicle purchases. The analysis was conducted by classifying customers based on indicators of age, the number of installments per month, income, tenor, pricing On the Road (OTR), the down payment, type of job, home status, and type of vehicle. With their customer segmentation analysis is expected to facilitate the process of financing deals. Financing offer can be aimed at prospective customers in accordance with the segment. 2. Literature Review A. Data Mining Data mining is the process of finding a correlation, patterns, and trends that have meaning. The discovery process is done by sorting large amounts of data stored in the repository using statistical pattern recognition technology and engineering and math [1]. According to [1], there are six functions in data mining, which is a function description, estimation, prediction, classification, clustering, and association. Below is an explanation of each function that exist in data mining: 1) Function Description (Description) The results of data mining models can describe the pattern as much as possible clear and in accordance with the interpretation and explanation of the intuitive. 2) Function Estimation (Estimation) Estimation function like the function classification, but the difference is in the target variable estimation function not form a category, which is numeric. Estimation function is used to estimate the unknown of a set of data such as the average population and the population variance. 3) Function Prediction (Prediction) ISSN: 2005-4238 IJAST 4498 Copyright ⓒ 2020 SERSC International Journal of Advanced Science and Technology Vol. 29, No. 5, (2020), pp. 4497 - 4506 Prediction function like the function classification and estimation, but the difference prediction is used to estimate the result of things that have happened based on existing data. 4) Function Classification (Classification) In the classification functions, such as target variable categories that are usually partitioned into several classes such as low grade, medium, and high. Data mining models to test the large data sets where each data contains information about the target and predictor variables. In the new data, the algorithm classified the data is based on knowledge gained from the results of the test data set. 5) Function Grouping (Clustering) Grouping function is a function that performs grouping data, observations, and a case into a class with characteristics similar objects. Grouping function is used to categorize the entire data into a relatively homogeneous group in which the similarity of data within the group is maximized while existing outside the group is minimized. 6) Functions Association (Association) Association function is used to find the rules to measure the relationship between two or more attributes. The rules are in the form of if-then and the level of support (support) and the level of trust (confidence) associated with these regulations. B. K-means Clustering Algorithm K-means algorithm is used to assess the quality of the partition so that objects that are in one group are like each other but have no resemblance to the object in the other group. K-means algorithm is an objective function that aims for a high commonality and similarity intra inter group low. Grouping using centroid-based partitioning technique in which the centroid of each group represents the characteristics of the group. Conceptually, the centroid is the center point. Centroid can be defined in various ways, such as by calculating the mean or medoid of objects or points entered the group. Differences between objects are then measured by calculating the Euclidean distance between two points. According to [1,4] there are four steps in the k-means clustering algorithm. The following is an explanation of the four steps: 1) Determine the number of groups. 2) Selecting data records as many as the number of groups at random as the initial centroid value. 3) Determining the center point (centroid) which is the nearest group and enter the data record as a member of the group nearest its center point. This step is performed to all the existing data record. After that, calculate the ratio between the mount of Variation Between Cluster (BCV) with Within Cluster Variation (WCV). If the ratio calculation result is greater than the previous rate, then the algorithm proceeds to the next step. But if not, then the algorithm is stopped. 4) Renewing the centroid by calculating the average of the data in each group and repeat step 3. Calculation BCV represented by centroid Euclidean distance between groups. If the object (p1, p2, p3, ..., pn) has three attributes, namely x, y, and z, and these objects will be grouped into three groups (C1, C2, C3) with each centroid- it is m1, m2 and m3. Thus, the calculation of Euclidean distance can be seen in equation (1) and the calculation of BCV can be seen in equation (2).
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