Improvements of Fuzzy C-Means Clustering Performance using Particle Swarm Optimization on Student Grouping based on Learning Activity in a Digital Learning Media Ahmad Afif Supianto Informatics Engineering Universitas Brawijaya Malang, Indonesia afif.supianto@ub.ac.id Retno Indah Rokhmawati Information Technology Education Universitas Brawijaya Malang, Indonesia retnoindahr@ub.ac.id Satrio Hadi Wijoyo Information Technology Education Universitas Brawijaya Malang, Indonesia satriohadi@ub.ac.id Nur Sa’diyah Informatics Engineering Universitas Brawijaya Malang, Indonesia nursadiyah@student.ub.ac.id Satrio Agung Wicaksono Information Technology Education Universitas Brawijaya Malang, Indonesia satrio@ub.ac.id Yusuke Hayashi Learning Engineering Laboratory Hiroshima University Hiroshima, Japan hayashi@lel.hiroshima-u.ac.jp Candra Dewi Informatics Engineering Universitas Brawijaya Malang, Indonesia dewi_candra@ub.ac.id Hanifah Muslimah Az-Zahra Information Technology Education Universitas Brawijaya Malang, Indonesia hanifah.azzahra@ub.ac.id Tsukasa Hirashima Learning Engineering Laboratory Hiroshima University Hiroshima, Japan tsukasa@lel.hiroshima-u.ac.jp ABSTRACT e field of learning media has been developing rapidly in recent years, especially in an effort to support students’ learning process. e amount of recorded learning process data has also significantly increased. e recorded data represents the students’ thinking process in building a solution for a problem. e sheer size of the recorded data proves to be quite a challenge in an effort to mine the students’ thinking process, especially when done manually. Additionally, to group the recorded data into clusters is also another form of challenge that needs to be faced. In general, the entire process of mining students’ thinking paerns aims to utilize the data to gather hidden information which can also be used to give appropriate and proper feedback to the students. is Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permied. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. SIET '20, November 16–17, 2020, Malang, Indonesia © 2020 Association for Computing Machinery. ACM ISBN 978-1-4503-7605-1/20/09…$15.00 hps://doi.org/10.1145/3427423.3427449 paper aims to employ the Fuzzy C-Means and Particle Swarm Optimization (FCMPSO) method to cluster students based on their learning activity to a digital learning media and compare its performance to original Fuzzy C-Means (FCM) method. Particle Swarm Optimization (PSO) algorithm is proposed to optimize the performance of the FCM algorithm, in which this algorithm is inherently sensitive towards centroid on the initial clustering process that utilizes the Silhouee coefficient as an evaluation method. Based on the experiments that have been done to 12 assignments, each assignment forms a different number of optimal clusters. is shows that each student faces and uses different strategies to solve their assignments. e formed groups are dominated by two major clusters, namely the high- performance students, and the low-performance students. Additionally, the adaptation of PSO to FCM improves the clustering quality significantly based on the observed average Silhouee coefficient. CCS CONCEPTS • Computing methodologies ~ Machine learning ~ Learning paradigms ~ Unsupervised learning ~ Cluster analysis KEYWORDS clustering, fuzzy c-means, particle swarm optimization, silhouee coefficient, digital learning media 239