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All Journal Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Penjaminan Mutu INOVTEK Polbeng - Seri Informatika Integritas: Jurnal Antikorupsi Dinamisia: Jurnal Pengabdian Kepada Masyarakat Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Zonasi: Jurnal Sistem Informasi Journal of Applied Engineering and Technological Science (JAETS) ARSY : Jurnal Aplikasi Riset kepada Masyarakat Journal of Applied Data Sciences J-COSCIS : Journal of Computer Science Community Service Jurnal Penjaminan Mutu Jurnal Pengabdian Masyarakat Variasi IndoAI: Journal of Artificial Intelligence and Computational Logic Journal of Community Outreach in Science & Society
Lucky Lhaura Van FC, Lucky Lhaura
Universitas Lancang Kuning
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Sara Detection on Social Media Using Deep Learning Algorithm Development M. Khairul Anam; Lucky Lhaura Van FC; Hamdani Hamdani; Rahmaddeni Rahmaddeni; Junadhi Junadhi; Muhammad Bambang Firdaus; Irwanda Syahputra; Yuda Irawan
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.5390

Abstract

Social media has become a key platform for disseminating information and opinions, particularly in Indonesia, where SARA (Ethnicity, Religion, Race, and Intergroup) issues can fuel social tensions. To address this, developing an automated system to detect and classify harmful content is essential. This study develops a deep learning model using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to detect SARA-related comments on Twitter. The method involves data collection through web scraping, followed by cleaning, manual labeling, and text preprocessing. To address data imbalance, SMOTE (Synthetic Minority Over-sampling Technique) is applied, while early stopping prevents overfitting. Model performance is evaluated using precision, recall, and F1-score. The results demonstrate that SMOTE significantly improves model performance, particularly in detecting minority-class SARA comments. CNN+SMOTE achieves a accuracy of 93%, and BiLSTM+SMOTE records a recall of 88%, effectively capturing patterns in SARA and non-SARA data. With SMOTE and early stopping, the model successfully manages class imbalance and reduces overfitting. This research supports efforts to curtail hate speech on social media, especially in the Indonesian context, where SARA-related issues often dominate public discourse.
Co-Authors Abd Kadir Mahamad afred suci, afred Afriansyah Ahmad, Sya'banu Alexsander Yandra, Alexsander Anam, M Khairul Anggraini, Keumala Chitra Charisma Islami darmayunata, yuvi Darmayunata, Yuvi Elvira Asril, Elvira Emy Sadjati, Emy Evi Lianda Fajrizal Fajrizal Fajrizal, Fajrizal Fana Wiza, Fana Faridhi, Adrian Fauzi, Abu Amar Febrizal Alfarasy Syam Hamdani Hamdani Handrian Azhar Irwanda Syahputra Irwanda Syahputra Junadhi Junadhi Keumala Anggraini Keumala Anggraini Lisnawita Lisnawita Loneli Costaner Mahamad, Abd Kadir Muhamad Sadar, Muhamad Muhammad Bambang Firdaus Musfawati Musfawati Musfawati Musfawati Musfawati Musfawati Novia Lestari Novia Lestari Pandu Pratama Putra, Pandu Pratama prama widayat, prama Rahmaddeni Rahmaddeni Rebecca La Volla Nyoto Rebecca La Volla Nyoto Saiful Bukhori Saon, Sharifah satria tri nanda, satria tri Sean Marta Efastri, Sean Marta Sharifah Saon Simabura, Charles Sri Maryanti Suharni Syafrika Deni Rizki Syahtriatna Djusar Tiara Vebby Vebby, Vebby Yeni, Refliza Yeza Febriani Yogi Yunefri, Yogi Yuda Irawan Yuvi Yuvi Darmayunata