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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi SKANIKA: Sistem Komputer dan Teknik Informatika Nusantara Science and Technology Proceedings
Sari, Allan Ruhui Fatmah
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology

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ANALISIS SPEECH-TO-TEXT PADA VIDEO MENGANDUNG KATA KASAR DAN UJARAN KEBENCIAN DALAM CERAMAH AGAMA ISLAM MENGGUNAKAN INTERPRETASI AUDIENS DAN VISUALISASI WORD CLOUD Fahrudin, Tresna Maulana; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela; Lestari, Amanda Ayu Dewi
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 5 No 2 (2022): Jurnal SKANIKA Juli 2022
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1170.948 KB) | DOI: 10.36080/skanika.v5i2.2942

Abstract

Di era revolusi industri 4.0 saat ini, penggunaan media sosial sangat berkembang pesat dengan terjadinya interaksi dan komunikasi antarmanusia dalam dunia maya. Namun, terkadang ditemui adanya pengguna media sosial yang menyalahgunakan untuk kepentingan tertentu, salah satunya ceramah agama yang mengandung kata-kata kasar dan ujaran kebencian. Semakin banyak kekeliruan dalam memahami agama dikarenakan apa yang disampaikan oleh penceramah bukanlah tentang agama itu sendiri, tetapi justru menghasut, menghina dan memprovokasi para pendengarnya untuk tujuan tertentu. Oleh karena itu, penelitian ini mengusulkan analisis speech-to-text pada video yang mengandung kata-kata kasar dan ujaran kebencian dalam ceramah agama islam menggunakan interpretasi audiens dan visualisasi word cloud. Hasil penelitian menunjukkan bahwa sebanyak 3 penceramah agama dan total terdapat 9 video di mana masing-masing video berdurasi 3 menit mengandung kata-kata kasar dan ujaran kebencian.
Social Media Analysis and Topic Modeling: Case Study of Stunting in Indonesia Muhaimin, Amri; Fahrudin, Tresna Maulana; Alamiyah, Syifa Syarifah; Arviani, Heidy; Kusuma, Ade; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10797

Abstract

Purpose: Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6%, and for the future, the government has set a target of up to 14% in 2024. Rapid technological developments and freedom of expression on the internet produce review text data that can be analyzed for evaluation. This study analyzes the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation.Design/methodology/approach: The methodology used in this study is Latent Dirichlet Allocation. The data was collected from twitter with the keyword 'stunting'. After, the data was cleaned and then modeled using the Latent Dirichlet Allocation.Findings/results: The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'children', 'decrease', 'number', 'prevention', and 'nutrition' are among the words that often appear on stunting.Originality/value/state of the art: This study uses the keyword stunting and analyzes it. Social media analytics show that the people of Indonesia are primarily aware of stunting. Also, the Latent Dirichlet Analysis can be used to create the model.
Sentiment Analysis in Social Media: Case Study in Indonesia Muhaimin, Amri; Fahrudin, Tresna Maulana; Alamiyah, Syifa Syarifah; Arviani, Heidy; Kusuma, Ade; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4106

Abstract

Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6% and for the future, the government has set a target of up to 14% in 2024. There have been many government efforts in implementing programs to reduce stunting rates. However, not everything runs optimally. Rapid technological developments and freedom of expression in the internet world produce review text data that can be analyzed for evaluation. This study aims to analyze the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation (LDA). The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'anak', 'turun', 'angka', 'cegah' and 'gizi' are among the words that often appear on the topic of stunting.
Co-Authors Alamiyah, Syifa Syarifah Fahrudin, Tresna Maulana Heidy Arviani Kusuma, Ade Lestari, Amanda Ayu Dewi Lisanthoni, Angela Muhaimin, Amri