Comparative Study of Classification Method on Customer Candidate Data to Predict its Potential Risk

Sadikin, Mujiono; Alfiandi, Fahri

doi:10.11591/ijece.v8i6.pp4763-4771

Cited by 20 publications

(19 citation statements)

References 6 publications

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“…The goal of this phase is to emphasise useful image information while reducing unwanted data. Furthermore, the data collected have many error or noise, so that it needs to perform the data preprocessing operation [20,21].…”

Section: Pre-processingmentioning

confidence: 99%

Indonesian license plate recognition with improved horizontal-vertical edge projection

Nurhaida

Nududdin

Ramayanti

2021

IJEECS

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<p>License plate recognition (LPR) is one of the classical problems in the field of object recognition. Its application is very crucial in the automation of transportation system since it helps to recognise a vehicle identity, which information is stored in the license plate. LPR usually consists of three major phases: pre-processing, license plate localisation, optical character recognition (OCR). Despite being classical, its implementation faced with much more complicated problems in the real scenario. This paper proposed an improved LPR algorithm based on modified horizontal-vertical edge Projection. The method uses for detecting and localising the region of interest. It is done using the horizontal and vertical projection of the image. Related works proved that the modified horizontal-vertical edge projection is the simplest method to be implemented, yet very effective against Indonesian license plate. However, its performance gets reduced when specular reflection occurs in the sample image. Therefore, morphological operations are utilised in the pre-processing phase to reduce such effects while preserving the needed information. Eighty sample images which captured using various camera configurations were used in this research. Based on the experimental results, our proposed algorithm shows an improvement compared with the previous study and successfully detect 71 license plates in 80 image samples which results in 88.75% accuracy.</p>

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Section: Pre-processingmentioning

confidence: 99%

Indonesian license plate recognition with improved horizontal-vertical edge projection

Nurhaida

Nududdin

Ramayanti

2021

IJEECS

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“…Classification is one of the data mining techniques that is mainly used to analyze a given dataset and takes each instance of it and assigns this instance to a particular class such that classification error will be least [11]. C4.5 algorithm is one classification used algorithms and produced a decision on the developed by ross Quinlan in 1979 [12].…”

Section: Literature Reviewmentioning

confidence: 99%

The Implementation of Classification Algorithm C4.5 in Determining the Illness Risk Level for Health Insurance Company in Indonesia

Angkasa¹,

Fitrianah²

2020

IJCA

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Fundamental thing on health insurance is how to manage all contributions fee from membership insurance, so it can use for finance health services. In this writer's case, the problem of health insurance is when registered membership insurance, there's no validation or adjustment about fee insurance with a history of illness from the applicant. That thing will be increasing financial cost if insurance does not use another approach from health services like promotive and preventive services for manage illness registered membership for health insurance, so that can be suppress financing of health services. Based on data on health insurance, they can do classification processing data and combined with algorithm C 4.5 for proses classification. Classification that has been used for mapping the level of risk illness membership in health insurance. Result from this research using a ten-fold cross-validation / confusion matrix with accuracy 99,87%.

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“…Classification is also called supervised learning because data classes are predetermined [5]. Classification method can be used for various cases, as M. Sadikin and F. Alfiandi did in his research conducting a comparative study of classification method to predict risk potential on customer candidate data [6]. The classification process has two phases, wherein the training data are analyzed by the classification algorithm, and the second phase is classification process, wherein the test data are used to estimate the accuracy of the classification model or classifier [7].…”

Section: A Classificationmentioning

confidence: 99%

Prediction Analysis of Kartu Jakarta Pintar (KJP) Awardees in Vocational High School XYZ Using C4.5 Algorithm

Damiaza¹,

Fitrianah²

2020

IJMLC

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The purpose of this paper is to analyze data and predict students who have the potential to acquire Kartu Jakarta Pintar (KJP) scholarships. KJP is a financial assistance program from the government of Special Capital Region of Jakarta granted to Jakarta citizens who are poor to allow them to receive proper education until graduating from senior high school or vocational high school. One problem arising in its implementation is the provision of KJP that is not on target, which is due to uneven distribution of information and inaccurate decision making. To overcome this problem, it is necessary to analyze the data regarding previous KJP awardees, using data mining techniques. The method applied in the current study is a decision tree with C4.5 algorithm. Decision tree is used to help simplify the decision-making process, so that decisions can interpret the solution of the problem. Thus, based on data analysis of KJP awardees, the level of accuracy will be obtained, which proves the C4.5 algorithm can be used as an alternative method in determining prospective KJP awardees.

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Comparative Study of Classification Method on Customer Candidate Data to Predict its Potential Risk

Cited by 20 publications

References 6 publications

Indonesian license plate recognition with improved horizontal-vertical edge projection

Indonesian license plate recognition with improved horizontal-vertical edge projection

The Implementation of Classification Algorithm C4.5 in Determining the Illness Risk Level for Health Insurance Company in Indonesia

Prediction Analysis of Kartu Jakarta Pintar (KJP) Awardees in Vocational High School XYZ Using C4.5 Algorithm

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