KOMBINASI METODESIMPLEADDITIVEWEIGHTING (SAW)DENGANALGORITMA NEAREST NEIGHBOR UNTUK REKRUITMEN KARYAWAN

Fricles Ariwisanto Sianturi, Martua Sitorus

Abstract


The SAW (Simple Additive Weight) method is often also known as the weighted sum method. The basic concept of the SAW method is to find a weighted sum of the performance ratings for each alternative on all attributes (Fishburn, 2011), the SAW method requires the decision matrix normalization process (X) to a scale that can be compared with all existing alternative ratings. The SAW method is the most well-known and most widely used method in dealing with a Multiple Attribute Decision Making (MADM) situation. MADM itself is a method used to find optimal alternatives from a number of alternatives with certain criteria.

Algortima K-Nearest Neighbor (K-NN) is a method for classifying objects based on learning data that is the closest distance to the object. K-Nearest Neighbor is based on the concept of 'learning by analogy'. Learning data is described by n-dimensional numeric attributes. Each learning data presents a point, marked with c, in n-dimensional space. Nearest Neighbor algorithm classification method that groups new data into several data / neighbors (neighbors) closest. So by combining the two algorithms will get the results in hiring employees at the company. In studies that have often been conducted on employee recruitment have been found, but in this study is to combine the SAW (Simple Additive Weightinh) method and the existing algorithm in Data Mining, the Nearest Neighbor algorithm. The final result in this study is to see the results of a combination of calculations SAW (Simple Additive Weight) with the Nearest Neighbor algorithm, the final results seen are the results of the work process in the calculation of the two methods.

 

Keywords: Decision Support System; Simple Additive Weight (SAW); Nearest Neighbort


Full Text:

PDF

References


Fricles Ariwisanto Sianturi. Implementasi Sistem Pendukung Keputusan Kenaikan Jabatan Guru Dengan Model Profile Matching Pada Sekolah Sma Swasta Raksana Medan. Mantik Penusa. 2015;18(2):44-52. http://e-jurnal.pelitanusantara.ac.id/index.php/mantik/article/view/43.

Alwi, Hasan. Dkk. (2003) Kamus Besar Bahasa Indonesia, Balai Pustaka, Jakarta.

Antonius Agung, Titin Winarti, Vensy Vydia. 2013. Sistem Pendukung Keputusan Penyeleksian Karyawan di PT. Ploss Asia Menggunakan Metode Fuzzy Tahani dan Microsoft Visual Basic 6.0. JurnalTransit : Volume 1 Nomor 3.

Manalu E, Sianturi FA, Manalu MR. Penerapan Algoritma Naive Bayes Untuk Memprediksi Jumlah Produksi Barang Berdasarkan Data Persediaan Dan Jumlah Pemesanan Pada Cv . Papadan Mama Pastries. Mantik Penusa. 2017;1(2):16-21. http://e-jurnal.pelitanusantara.ac.id/index.php/mantik/article/view/257.

Setiabudi S, Erwin. 2012. Sistem Penunjang Keputusan Untuk Penerimaan Karyawan Baru Pada PT. Pupuk Kalimantan Timur. Yogyakarta STMIK AMIKOM.

Sianturi FA, Informatika T, Utara S. Penerapan Algoritma Apriori Untuk Penentuan Tingkat. 2018;2(1):50-57. http://e-jurnal.pelitanusantara.ac.id/index.php/mantik/article/view/330.

Sianturi FA, Sinaga B, Hasugian PM, Informatika T, Utara S. Fuzzy Multiple Attribute Decisison Macking Dengan Metode Oreste Untuk Menentukan Lokasi Promosi. 2018;3(1):63-68. http://e-jurnal.pelitanusantara.ac.id/index.php/JIPN/article/view/289.

Antonius Agung, Titin Winarti, Vensy Vydia. 2013. Sistem Pendukung Keputusan Penyeleksian Karyawan di PT. Ploss Asia Menggunakan Metode Fuzzy Tahani dan Microsoft Visual Basic 6.0. JurnalTransit : Volume 1 Nomor 3.

Boedy, Cged. 2012. Pengertian, Kelebihan, dan Keurangan K-nearest Neighbor (K-NN).

Setiabudi S, Erwin. 2012. Sistem Penunjang Keputusan Untuk Penerimaan Karyawan Baru Pada PT. Pupuk Kalimantan Timur. Yogyakarta STMIK AMIKOM.

Sianturi FA, Sinaga B, Hasugian PM, Informatika T, Utara S. Fuzzy Multiple Attribute Decisison Macking Dengan Metode Oreste Untuk Menentukan Lokasi Promosi. 2018;3(1):63-68. http://e-jurnal.pelitanusantara.ac.id/index.php/JIPN/article/view/28


Refbacks

  • There are currently no refbacks.


Lisensi Creative Commons

Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial-TanpaTurunan 4.0 Internasional.

 

 LOKASI:


STMIK PELITA NUSANTARA
Jl. St.Iskandar Muda No.1 - Medan
Email : lppm.pelitanusantara@yahoo.com

E-ISSN : 2580-9741(Online)

P-ISSN : 2088-3943(Print)