Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Lucky Surya Haryadi
Universitas Kristen Maranatha
Computer Science & IT

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

Found 1 Documents
Search

 1 
Prediksi Penyebaran Informasi di Twitter dengan Metode Pembelajaran Mesin dengan Fitur Linimasa Lucky Surya Haryadi; Bernard Renaldy Suteja
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3324

Abstract

Abstract — Social Media Network has been an important information source, and the information propagation within the network gave an impact on politics, marketing, and entertainment industry. Our aim is to predict a tweet whether the information will be propagated further. The previous research has focused on analyzing this task with a wide range of learning methods and features, such as content and account features. Timeline features are proposed as features that can further predict information propagation and as we compared the performance with content and account features. The dataset consists of 43.229 tweets, we predict the information propagation with logistic regression, support vector machines, and random forest learning method with these features. Our result indicates that the timeline feature can be a good candidate for predicting information propagation and the random forests learning method consistently performs better. From the training result, we further calculate feature importance. Recently tweets, engagement with another user and previous liked tweets on the timeline features contributed to more popular tweets.
Co-Authors Bernard Renaldy Suteja