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Journal : CESS (Journal of Computer Engineering, System and Science)

Weighting Comparative Analysis Using Fuzzy Logic and Rank Order Centroid (ROC) in the Simple Additive Weighting (SAW) Method Alfin Ghazali; Poltak Sihombing; Muhammad Zarlis
CESS (Journal of Computer Engineering, System and Science) Vol 7, No 1 (2022): January 2022
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (425.128 KB) | DOI: 10.24114/cess.v7i1.27758

Abstract

Decision Support System Method which is often referred to as the weighted addition method, one of which is Simple Additive Weighting. But the value of the weights in this system is not officially the calculation used. Therefore, usually a number of researchers combine this method with other methods to be more precise and accurate in supporting their decisions. In this study, the authors compare the results of the SAW method between the weighting based on the Fuzzy Logic method and the weighting based on the Rank Order Centroid (ROC) method. The case studied was the number of student satisfaction with learning outcomes during the Covid-19 pandemic. The results obtained are the number of students who are declared satisfied with learning during the Covid-19 pandemic as many as 6 students for the weighting of the Fuzzy Logic method and 5 students for the weighting of the Rank Order Centroid (ROC) method.
Feature Selection Using Eigen Vector to Improve K-Means Clustering Nugroho Syahputra; Muhammad Zarlis; Syahril Efendi
CESS (Journal of Computer Engineering, System and Science) Vol 7, No 2 (2022): July 2022 - In Process
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v7i2.35449

Abstract

Banyaknya jumlah atribut data set dari proses pengelompokan data dengan K-Means Clustering dapat mempengaruhi besaran jumlah iterasi yang dihasilkan. Pada riset ini, Eigen Vector digunakan untuk melakukan seleksi fitur pada data set. Data set yang telah diseleksi selanjutnya dilakukan proses clustering dengan K-Means Clustering. Data set yang digunakan pada riset ini adalah Wine Quality Dataset yang diperoleh dari UCI Machine Learning Repository, dengan 11 atribut ,4898 records data dan 7 kelas atribut. Hasil dari riset ini menunjukkan bahwa rata-rata jumlah iterasi yang diperoleh dari perbandingan pengujian dengan menggunakan K-Means tanpa seleksi fitur yaitu diperoleh rata-rata sebesar 21 iterasi, sedangkan K-Means dengan seleksi fitur Eigen Vector diperoleh rata-rata sebesar 15 iterasi. Evaluasi clustering dihitung menggunakan Davies-Bouldin Index (DBI). Nilai DBI pada K-Means Clustering tanpa seleksi fitur yaitu sebesar 0.746667, sedangkan pada K-Means Clustering dengan Eigen Vector yang telah diseleksi sebanyak 5 atribut diperoleh nilai rata-rata DBI masing-masing sebesar 0.664316.