Enhancing TikTok Account Performance with Data Pattern Identification

Wilem Musu; Nadia Lempan; Indra Samsie

doi:10.31154/cogito.v10i1.647.667-679

Authors

Wilem Musu Teknik Informatika, Universitas Dipa Makassar
Nadia Lempan Bisnis Digital, Universitas Dipa Makassar
Indra Samsie Sistem Informasi, Universitas Dipa Makassar

DOI:

https://doi.org/10.31154/cogito.v10i1.647.667-679

Keywords:

Data Science Techniques, Data Pattern Identification, Tiktok Account Performance, Statistics, Machine Learning

Abstract

TikTok is a social media platform widely used to share information through short videos to achieve goals and interests in business, education, politics, government, and personal existence. Every activity on this platform is recorded and presented privately to each account owner. However, this data has not been utilized optimally to improve account performance. This research aims to offer a data analysis concept that integrates statistical and machine learning approaches to identify data patterns in each user's data collection, enabling the improvement of account performance. The approach utilizes Linear Regression, k-means, and Decision Tree methods. The results obtained show that the concept of identifying data patterns in TikTok account data has successfully developed a predictive model for video posts that can potentially increase total viewership, video plays, and audience engagement. This is achieved through optimizing video components such as captions, text, hashtags, sound genre, and video type. The outcome yielded a classification model that can predict capable component content to enhance account performance.

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