Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Nur Sa'diyah
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Implementasi Algoritme Fuzzy C-Means dengan Particle Swarm Optimization (FCMPSO) untuk Pengelompokan Proses Berpikir Siswa dalam Proses Belajar Nur Sa'diyah; Ahmad Afif Supianto; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 6 (2020): Juni 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The current learning process can be carried out using a variety of learning media, one of which is called Monsakun, a learning media about simple arithmetic word problems. Learning activities undertaken by students at Monsakun will be stored in the datalog. The datalog is a representation of students' thought processes while studying with Monsakun. The thought process that students do when studying at Monsakun certainly varies from one student to another student. Therefore, clustering students who have a tendency to think similarly into the same group is needed in order to facilitate the teaching staff in handling and providing appropriate feedback on the learning constraints of their students. This study aims to utilize datalog from Monsakun learning media to get groups of students' thought processes in the learning process using the Fuzzy C-Means algorithm that is optimized with Particle Swarm Optimization (FCMPSO). Based on the results of the implementation that has been carried out using 12 data assignments at Monsakun, the best results are group formation dominated by 2 clusters. The optimum parameter values ​​have different results for each data assignment, and there is only the same optimum value for all data assignments on the learning factor parameters.
Co-Authors Ahmad Afif Supianto Candra Dewi