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Mayang Ruza
Universitas Dinamika Bangsa
Computer Science & IT

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Implementasi Algoritma K-Means Clustering Untuk Mengetahui Minat Pembeli di Agen Buah Melon Yudi Nanda Ghina; Najmul Laila; Marrylinteri Istoningtyas; Mayang Ruza; Errissya Rasywir; Maria Rosario Borroek; Yovi Pratama
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 2 No 2 (2022): JAKAKOM Vol 2 No 2 September 2022
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1032.104 KB) | DOI: 10.33998/jakakom.2022.2.2.116

Abstract

Abstrak - Penelitian ini dilakukan untuk mempelajari K-Means Clustering dalam pencarian knowledge (pengetahuan). Tujuan dari penelitian ini kemungkinan dapat membantu pihak Agen Buah Yudi untuk menentukan cluster minat pembeli banyak, sedang, dan sedikit diletakkan sesuai urutan minat pembeli buah yang bayak, sedang, dan sedikit di Agen Buah Yudi. Untuk itu dalam metode K-Means Clustering dimungkinkan adanya solusi dan analisa terhadap pengolahan data dan parameter-parameter yang menjadi acuan untuk mengambil keputusan. Di dalam metode ini terdapat langkah-langkah penyelesaian masalah. Adapun tools bantu untuk mengimplementasikan metode tersebut adalah Weka akan mengolah data secara tersusun atas operator-operator yang langsung didapatkan hasil secara akurat selanjutnya pada tahapan terakhir akan didapatkan knowledge baru. Kata kunci : Buah Melon, klasifikasi, Algoritma, K-means Clustering, Weka Abstract - This research was conducted to study K-Means Clustering in the search for knowledge (knowledge). The purpose of this research may be to help the Yudi Fruit Agent to determine the interest of many, medium, and few buyers according to the order of high, medium, and little interest of the Yudi Fruits Agent. For this reason, in the K-Means Clustering method, it is possible to provide solutions and analyzes for data processing and the parameters that become the reference for making decisions. In this method there are steps to solve the problem. As for the tools to implement this method, Weka will process data in an organized manner consisting of operators which immediately get accurate results, then in the last stage new knowledge will be obtained. Keywords : Buah Melon, klasifikasi, Algoritma, K-means Clustering, Weka
Sistem Pendukung Keputusan untuk Menentukan Karyawan Terbaik dengan Metode TOPSIS pada PT. Sumbertama Nusa Pertiwi mayang ruza; ibnu sani wijaya; eddy suratno
Jurnal Manajemen Informatika JAMIKA Vol 13 No 2 (2023): Jurnal Manajemen Informatika (JAMIKA)
Publisher : Program Studi Manajemen Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jamika.v13i2.9901

Abstract

PT. Sumbertama Nusa Pertiwi is a company engaged in palm oil plantations and processing. In carrying out the operations of PT. Sumbertama Nusa Pertiwi gives awards to employees by selecting the best employees every year. This research aims to design a decision support system to identify the best employees at PT. Sumbertama Nusa Pertiwi by giving a rating to each employee using the Technique for Other Reference by Similarity to Ideal Solution (TOPSIS) method. The results of this research are a decision support system using the TOPSIS method with ten criteria, namely work performance, attitudes and ethics, motivation, initiative, loyalty, responsibility, discipline, honesty, leadership, and safeguarding company assets. As an alternative, there are five employees, namely Sutrisno, M. Ahyar, Lamidi, Sabar, and Iskandar. With the first rank, namely Sabar with a value of 0.61886, Iskandar with a value of 0.537845, M. Ahyar with a value of 0.529544, Sutrisno with a value of 0.484994, and finally Lamidi with a value of 0.449489. So, the aim of designing a decision support system using the TOPSIS method is to make it easier for companies to determine the ranking of the best employees at PT. Sumbertama Nusa Pertiwi.
Co-Authors Eddy Suratno Errissya Rasywir Ibnu Sani Wijaya Maria Rosario Borroek Marrylinteri Istoningtyas Najmul Laila Nanda Ghina Yovi Pratama