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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) snimed Jurnal Teknoin Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Perfecting a Video Game with Game Metrics Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Linguistik Komputasional Jurnal Informatika dan Rekayasa Elektronik Journal of Computer System and Informatics (JoSYC) Automata
Ahmad Fathan Hidayatullah, Ahmad Fathan
Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

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Topic Modelling pada Sentimen Terhadap Headline Berita Online Berbahasa Indonesia Menggunakan LDA dan LSTM Chairullah Naury; Naury, Chairullah; Fudholi, Dhomas Hatta; Hidayatullah, Ahmad Fathan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 1 (2021): Januari 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i1.2556

Abstract

The online mass media is the source of the fastest and up-to-date information. A model that can provide mapping will help in sorting out information more precisely. In this study, the authors applied topic modeling to the results of sentiment analysis on online news headlines in Indonesian. Sources of data in this study were obtained from online mass media in Indonesian. The data collected were analyzed for sentiment using the Long Short-term Memory (LSTM) method, in order to obtain news headlines with positive, negative, and neutral sentiments. The classification obtained from the results of the sentiment analysis process is continued with the topic modeling process using the Latent Dirichlet Allocation (LDA) method and visualized in the form of wordcloud and intertopic distance map (pyLDAVis) to determine the relationship between one topic and another. The result of sentiment analysis is a model with 71.13% of accuracy level and the results of topic modeling are in the form of some topics that are easy to interpret.
Co-Authors Adityo Permana Wibowo Ahmad Rafie Pratama Amiin Majiid Nugroho Anas Satria Lombu Annisa Zahra Azmiardhy Zulkifli Farmadiansyah Cahyaningtyas, Siwi Chairullah Naury Chanifah Indah Ratnasari Dantik Puspita Sari, Dantik Puspita Dhomas Hatta Fudholi Dimas Bintang Prasetyo Fayruz Rahma Febrianto Eko Saputro Fudholi, Dhomas Hatta Habib Faizal Fadli Kartika Rizqi Nastiti Kartika Rizqi Nastiti Muhammad Fakhri Despawida Aulia Putra Muhammad Fakhri Despawida Aulia Putra Muhammad Rifqi Ma'arif Nassharih Abdulloh Naury, Chairullah Nuli Giarsyani Pamungkas, Rheza Daffa Prasetyo, Alan Dwi PRATIWI, INTAN Rheza Daffa Pamungkas Ridho Rahmadi Satrio Wisnugroho Septia Rani Siwi Cahyaningtyas Yurio Windiatmoko