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Berti Sari Br Sembiring
Program Studi Sistem Informasi, STMIK Kristen Neumann Indonesia, Jl. Jamin Ginting KM 10,5 Medan
Computer Science & IT

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Combination of Euclidean Distance on X-Means Algorithm in Data Grouping Berti Sari Br Sembiring
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (268.856 KB)

Abstract

Grouping can use clustering to group data based on the similarity between the data, so that the data with the closest resemblance is in one cluster while the different data is in another group. The X-Means algorithm is the development of K-Means. The weakness of X-Means is that in determining the distance matrix, the distance matrix is ​​an important factor that depends on the X-Means algorithm data set. The resulting distance matrix value will affect the performance of the algorithm. The results of the study are: testing with variations in the number of centroids (K) with values ​​of 2,3,4,5,6,7,8,9,10. The author concludes that the number of centroids 3 and 4 has a better iteration value compared to the number of centroids that are getting higher and lower based on the iris dataset with the jarax matrix Manhattan Distance. From the test results with the X-Means cluster point, calculate the Euclidean Distance distance with 100 iris data reaching the 9th iteration, while with 100 iris data by calculating the Manhattan Distance distance it reaches the 10th iteration. Meanwhile, in determining the cluster point using the X-Means method from 100 data iris reaches its 7th iteration.
Application of X-Means Method for Grouping Early Childhood Diseases Berti Sari Br Sembiring; Mahdianta Pandia; Natalina Br Sitepu
INFOKUM Vol. 10 No. 1 (2021): Desember, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (319.081 KB)

Abstract

Grouping can use clustering to group data based on the similarity between the data, so that the data with the closest resemblance is in one cluster while the different data is in another group. The X-Means algorithm is the development of K-Means. The weakness of X-Means is that in determining the distance matrix, the distance matrix is ​​an important factor that depends on the X-Means algorithm data set. The resulting distance matrix value will affect the performance of the algorithm. The results of the study are: testing with variations in the number of centroids (K) with values ​​of 2,3,4,5,6,7,8,9,10. The author concludes that the number of centroids 3 and 4 has a better iteration value compared to the number of centroids that are getting higher and lower based on the iris dataset with the jarax matrix Manhattan Distance. From the test results with the X-Means cluster point, calculate the Euclidean Distance distance with 100 iris data reaching the 9th iteration, while with 100 iris data by calculating the Manhattan Distance distance it reaches the 10th iteration. Meanwhile, in determining the cluster point using the X-Means method from 100 data iris reaches its 7th iteration.
DECISION SUPPORT SYSTEM FOR PROVIDING WORK ALLOWANCES AND PUNISHMENTS TO EMPLOYEES USING THE AHP AND SMART METHOD Berti Sari Br Sembiring; Mahdianta Pandia; Fransisca Br Sebayang; Harianta Sembiring
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (283.121 KB)

Abstract

From the results of the study it can be concluded, among others: The decision support system that was built was very helpful to speed up data processing in decision making for the provision of work benefits and punishments to employees. The SMART method is a suitable method to be applied in decision making by sharing alternatives, especially determining the provision of work benefits and punishments to employees quickly and precisely. The level of accuracy of the test results using the SMART method is 100%. The decision support system application that is built is dynamic in terms of determining criteria and weighting. So, it can be changed according to the needs of the company in providing work benefits and punishments
DECISION SUPPORT SYSTEM APPLICATION FOR SELECTION OF SCHOLARSHIP RECIPIENTS USING MOORA METHOD Berti Sari Br Sembiring; Mahdianta Pandia; Devanta Abraham Tarigan
INFOKUM Vol. 10 No. 5 (2022): December, Computer and Communication
Publisher : Sean Institute

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Abstract

Scholarships can be said as funding that does not come from self-funding or parents, but is provided by the government, private companies, embassies, universities, educational or research institutions, or also from the office where one works because of one's achievements can be given the opportunity to increase resource capacity. human resources through education. As for the results of the research, it was obtained by students who received scholarships at STMIK Neumann with a value of 3.0665 ranking 1
Co-Authors Devanta Abraham Tarigan Fransisca Br Sebayang Harianta Sembiring Mahdianta Pandia Natalina Br Sitepu