A Survey on Mixed-Attribute Outlier Detection Methods

Rokhman, Nur

doi:10.21512/commit.v13i1.5558

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…In the search for similarity values, most of them use symbolic or continuous data, but in this study, numeric (discrete) data are used. Data that contains numeric and symbolic data is called mixed attribute [10]. Nearest Neighbor was chosen because it is one of the classification and pattern recognition techniques and is widely used in the CBR system [11].…”

Section: Introductionmentioning

confidence: 99%

Reccomendations on Selecting The Topic of Student Thesis Concentration using Case Based Reasoning

Utami¹,

Suyanto²,

Sihabuddin³

2021

Indonesian J. Comput. Cybern. Syst.

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Case Based Reasoning (CBR) is a method that aims to resolve a new case by adapting the solutions contained in previous cases that are similar to the new case. The system built in this study is the CBR system to make recommendations on the topic of student thesis concentration. This study used data from undergraduate students of Informatics Engineering IST AKPRIND Yogyakarta with a total of 115 data consisting of 80 training data and 35 test data. This study aims to design and build a Case Based Reasoning system using the Nearest Neighbor and Manhattan Distance Similarity Methods, and to compare the results of the accuracy value using the Nearest Neighbor Similarity and Manhattan Distance Similarity methods. The recommendation process is carried out by calculating the value of closeness or similarity between new cases and old cases stored on a case basis using the Nearest Neighbor Method and Manhattan Distance. The features used in this study consisted of GPA and course grades. The case taken is the case with the highest similarity value. If a case doesnt get a topic recommendation or is less than the trashold value of 0.8, a case revision will be carried out by an expert. Successfully revised cases are stored in the system to be made new knowledge. The test results using the Nearest Neighbor Method get an accuracy value of 97.14% and Manhattan Distance Method 94.29%.

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Section: Introductionmentioning

confidence: 99%

Reccomendations on Selecting The Topic of Student Thesis Concentration using Case Based Reasoning

Utami¹,

Suyanto²,

Sihabuddin³

2021

Indonesian J. Comput. Cybern. Syst.

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“…In fact, anomalies that appear are not only in numerical data, but also in categorical data. Anomaly detection in numeric and categorical data (mixed attribute data) can be classified into 4 types, namely: categorized, enumerated, combined and mixed [9].…”

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confidence: 99%

Anomaly Detection in Hospital Claims Using K-Means and Linear Regression

Prakosa¹,

Rokhman²

2021

Indonesian J. Comput. Cybern. Syst.

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BPJS Kesehatan, which has been in existence for almost a decade, is still experiencing a deficit in the process of guaranteeing participants. One of the factors that causes this is a discrepancy in the claim process which tends to harm BPJS Kesehatan. For example, by increasing the diagnostic coding so that the claim becomes bigger, making double claims or even recording false claims. These actions are based on government regulations is including fraud. Fraud can be detected by looking at the anomalies that appear in the claim data.This research aims to determine the anomaly of hospital claim to BPJS Kesehatan. The data used is BPJS claim data for 2015-2016. While the algorithm used is a combination of K-Means algorithm and Linear Regression. For optimal clustering results, density canopy algorithm was used to determine the initial centroid.Evaluation using silhouete index resulted in value of 0.82 with number of clusters 5 and RMSE value from simple linear regression modeling of 0.49 for billing costs and 0.97 for length of stay. Based on that, there are 435 anomaly points out of 10,000 data or 4.35%. It is hoped that with the identification of these, more effective follow-up can be carried out.

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