Implementation of K-means algorithm in data analysis

Nasyuha, Asyahri Hadi; Zulham,; Rusydi, Ibnu

doi:10.12928/telkomnika.v20i2.21986

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…The novelty inherent in this research lies in the successful integration of two distinct data mining methodologies-namely, the K-means method and the C4.5 method-culminating in what is denoted as the hybrid data mining method. Unlike prior studies that predominantly utilized a singular method [26], [27] or amalgamated it with others [23], the innovation here resides in the fusion of the K-means and C4.5 methods. Remarkably, this marks a departure from conventional approaches, where such a combination was not hitherto explored by researchers.…”

Section: Research Framework Detailsmentioning

confidence: 99%

A hybrid data mining for predicting scholarship recipient students by combining K-means and C4.5 methods

Hendri,

Andrianof,

Robianto

et al. 2024

IJEECS

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This scholarly investigation delves into the strong desire for academic scholarships within the student body, especially prominent among socioeconomically disadvantaged individuals. The study aims to formulate a hybrid data mining paradigm by synergizing the K-means and C4.5 methodologies. K-means is applied for clusterization, while C4.5 facilitates prediction and decision tree instantiation. The research unfolds in sequential phases, commencing with data input and progressing through meticulous pre-processing, encompassing data selection, cleaning, and transformation. The novelty lies in successfully integrating the K-means and C4.5 methodologies, culminating in the hybrid data mining method. The dataset comprises 200 students seeking scholarships, revealing effective stratification into three clusters—cluster 0, cluster 1, and cluster 2—with 119, 48, and 33 students, respectively. The K-means method proves highly suitable, especially when combined with C4.5, for predicting scholarship recipients. A subset of 81 students from clusters 1 and 2 undergoes predictive modeling using C4.5, resulting in a commendable 85% accuracy, with 17 accurate forecasts and 3 minor inaccuracies. This research significantly enhances scholarship selection efficiency, particularly benefiting socioeconomically disadvantaged students.

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Section: Research Framework Detailsmentioning

confidence: 99%

A hybrid data mining for predicting scholarship recipient students by combining K-means and C4.5 methods

Hendri,

Andrianof,

Robianto

et al. 2024

IJEECS

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“…Algoritma K-means merupakan salah satu algoritma dengan partitional, karena K-Means didasarkan pada penentuan jumlah awal kelompok dengan mendefinisikan nilai centroid awalnya [12], [13]. Algoritma K-means menggunakan proses secara berulang-ulang untuk mendapatkan basis data cluster.…”

Section: Algoritma K-meansunclassified

“…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

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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“…Some of the solutions that can be solved by data mining are in the fields of markets and financial management, telecommunications, finance, astronomy, and other fields. Knowledge Discovery in Databases (KDD) is the entire process of searching for and identifying patterns in data, where the patterns found are valid, can be useful and can be understood (Nasyuha et al, 2022). KDD relates to integration techniques and scientific discovery, inteIDRretation, and visualization of patterns of data (Ikhwan, 2018).…”

Section: Introductionmentioning

confidence: 99%

Implementation of Classification Decision Tree and C4.5 Algorithm in selecting Insurance Products

Redjeki,

Damayanti,

Hudianti

et al. 2024

SinkrOn

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Every insurance customer will receive a policy card, as a sign that the person is included in the insurance and is obliged to pay the insurance premium, the amount of which has been determined by the company in accordance with the agreement. Premium payments are Insurance's biggest source of income. Unfavorable economic conditions often cause customers not to pay their premiums by the specified time limit, resulting in a delay in completing the recording of premium income. This research aims to find out the right type of insurance product for prospective customers. The research method used is Classification Decision Tree. Classification Decision Tree is a research method used to examine existing facts systematically based on research objects, existing facts to be collected and processed into data, then explained based on theory so that in the end it produces a conclusion. This research is for selecting the right type of insurance product for prospective customers based on the age and income categories of prospective customers. Insurers must be more careful, especially in selecting prospective customers, and in determining the right type of insurance product for prospective customers so that the power in selecting the right type of insurance product for prospective customers is right at the intended target.

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Implementation of K-means algorithm in data analysis

Cited by 5 publications

References 20 publications

A hybrid data mining for predicting scholarship recipient students by combining K-means and C4.5 methods

A hybrid data mining for predicting scholarship recipient students by combining K-means and C4.5 methods

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

Implementation of Classification Decision Tree and C4.5 Algorithm in selecting Insurance Products

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