Perbandingan Metode Klasifikasi Data Mining untuk Nasabah Bank Telemarketing

Subarkah, Pungkas; Pambudi, Enggar Pri; Hidayah, Septi Oktaviani Nur

doi:10.30812/matrik.v20i1.826

Cited by 7 publications

(8 citation statements)

References 7 publications

(8 reference statements)

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“…Data mining adalah sebuah proses menganalisa data untuk mengetahui suatu pola dari kelompok data yang tersembunyi [6]. Yang bertujuan untuk menemukan, menggali pengetahuan dari data atau informasi yang kita miliki.…”

Section: Data Miningunclassified

“…Untuk melakukan proses dengan menggunakan algoritma K-Means maka dibutuhkan data, dari proses penelitian yang dilakukan maka didapatkan data yang akan dikelompokan dari penilaian kepuasan penangan dan pelayanan Pasien adalah sebagi berikut: a. C1 = rata-rata (6,7,8,15,24,31,39,47,57,60,65,68,74,78,79,84,88,93,96,100,101,103,107,111,112,114, 112) = ( 3,50; 3,50 ;4,33) b. C2 = rata-rata (5,12,16,17,25,26,38,49,55,58,59,81,113,117) = (0,54;2,54;2,30) c. C3 = rata-rata (1,2,3,4,9,10,13,14,18,19,20,21,22,23,27,…”

Section: Hasil Dan Pembahasanunclassified

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Klasterisasi Data Penanganan dan Pelayanan Kesehatan Masyarakat dengan Algoritma K-Means

Syahra¹,

Habibie

Nasution³

et al. 2022

Jur. Ris. Kom.

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Quality public health services are one of the characteristics of the country's successful development in the health sector. The Health Office has formulated a number of methods to determine the level of progress of health development at the center to the sub-districts. Every year the Lubuk Pakam Health Office collects public health data for processing so that it produces a ranking of regions with the predicate of healthy districts/cities. Data mining is a process used to extract and identify useful information and obtain some important information from data in analyzing public health data. Furthermore, the algorithm that will be used for data mining management in the case of analyzing public health data and used for cluster formation is the K-Means algorithm. The results obtained in the data grouping there are categories of patient assessment levels Very Satisfied, Satisfied, and Dissatisfied. From the results of the K-Means method, it can be concluded to improve services and health care as for the results of grouping the level of satisfaction

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Section: Data Miningunclassified

Section: Hasil Dan Pembahasanunclassified

Klasterisasi Data Penanganan dan Pelayanan Kesehatan Masyarakat dengan Algoritma K-Means

Syahra¹,

Habibie

Nasution³

et al. 2022

Jur. Ris. Kom.

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“…Data that have gone through the Knowledge Discovery in Database (KDD) process is also known as control data (data-driven). With the rapid development of data mining, many studies are used to predict a case, especially banking (Subarkah, Pambudi, & Hidayah, 2020).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Approach for Credit Risk Analysis Using C4.5 Algorithm

Iqbal,

Efendi

2023

ComTech

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Credit risk is bad credit, resulting in bank losses due to non-receipt of disbursed funds and unacceptable interest income. However, credit services still have to be done to achieve profit. The absence of an approach that can assist in making policies to reduce credit risk makes the risk opportunities even more significant. So, data processing techniques are needed that produce information to be used as the basis for policies in triggering credit risk with data mining. The research presented an application of data mining as a credit risk approach considering the ability of data mining techniques to extract data into useful information with the C4.5 algorithm. The research used a sample of 30 data banks with 6 factors (credit growth, net interest margin, type of bank, capital ratio, company size, and bank compliance level). Credit risk was evaluated by making a decision tree and a RapidMiner test application. The results show that credit growth is the main factor causing credit risk, followed by bank compliance level, net interest margin, and capital ratio. Based on the results obtained, the C4.5 algorithm can be used in analyzing credit risk with results that are easy to understand and can be used as useful information for banks.

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“…Data Mining is solving problems by analyzing the data contained in the database. The data is stored electronically, and the data search is done automatically, like on a computer [1][2][3]. The K-Means method is a data mining method widely used in grouping data.…”

Section: Introductionmentioning

confidence: 99%

The Application of Numerical Measure Variations in K-Means Clustering for Grouping Data

Buaton,

Solikhun

2023

matrik

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The K-Means Clustering algorithm is commonly used by researchers in grouping data. The main problem in this study was that it has yet to be discovered how optimal the grouping with variations in distance calculations is in K-Means Clustering. The purpose of this research was to compare distance calculation methods with K-Means such as Euclidean Distance, Canberra Distance, Chebychev Distance, Cosine Similarity, Dynamic TimeWarping Distance, Jaccard Similarity, and Manhattan Distance to find out how optimal the distance calculation is in the K-Means method. The best distancecalculation was determined from the smallest Davies Bouldin Index value. This research aimed to find optimal clusters using the K-Means Clustering algorithm with seven distance calculations based on types of numerical measures. This research method compared distance calculation methods in the K-Means algorithm, such as Euclidean Distance, Canberra Distance, Chebychev Distance, Cosine Smilirity, Dynamic Time Warping Distance, Jaccard Smilirity and Manhattan Distance to find out how optimal the distance calculation is in the K-Means method. Determining the best distance calculation can be seen from the smallest Davies Bouldin Index value. The data used in this study was on cosmetic sales at Devi Cosmetics, consisting of cosmetics sales from January to April 2022 with 56 product items. The result of this study was a comparison of numerical measures in the K-Means Clustering algorithm. The optimal cluster was calculating the Euclidean distance with a total of 9 clusters with a DBI value of 0.224. In comparison, the best average DBI value was the calculation of the Euclidean Distance with an average DBI value of 0.265.

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Perbandingan Metode Klasifikasi Data Mining untuk Nasabah Bank Telemarketing

Cited by 7 publications

References 7 publications

Klasterisasi Data Penanganan dan Pelayanan Kesehatan Masyarakat dengan Algoritma K-Means

Klasterisasi Data Penanganan dan Pelayanan Kesehatan Masyarakat dengan Algoritma K-Means

Data-Driven Approach for Credit Risk Analysis Using C4.5 Algorithm

The Application of Numerical Measure Variations in K-Means Clustering for Grouping Data

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