FUZZY POSSIBILISTIC C-MEANS (FPCM) CLUSTERING UNTUK IDENTIFIKASI KELUHAN UTAMA PELANGGAN INDIHOME PADA DATA TWEETS - Taufik Aji Putra; Iut Tri Utami; Ardiana Alifatus Sa'adah (Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro, 2025)
Customer complaints reveal product or service issues and can drive improvements to enhance satisfaction. Many companies use Twitter as a platform to interact with customers, making handling complaints through social media crucial for building a positive image and maintaining customer loyalty. IndiHome Regional 4 faces challenges in identifying main complaints due to a high volume of complaints on Twitter. Cluster analysis groups similar complaints, aiding the identification process. Text mining converts textual data into numerical format, streamlining complaint processing. Fuzzy Possibilistic C-Means Clustering, a fuzzy-based method, enables data membership across clusters with varying degrees of membership. By adopting relative (fuzzy) and absolute (possibilistic) membership, more accurate data placement is achieved. Data consists of IndiHome Regional 4 customer complaint tweets received via the Twitter channel