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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Informatika Jurnal Komputasi Jiko (Jurnal Informatika dan komputer) Computer Science and Information Technologies Jurnal Komputasi
Muliadi Muliadi
Lambung Mangkurat University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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3D word embedding vector feature extraction and hybrid CNN-LSTM for natural disaster reports identification Mohammad Reza Faisal; Dodon Turianto Nugrahadi; Irwan Budiman; Muliadi Muliadi; Mera Kartika Delimayanti; Septyan Eka Prastya; Imam Tahyudin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.26091

Abstract

Social media contain various information, such as natural disaster reports. Artificial intelligence is used to identify reports from eyewitnesses early for disaster warning systems. The artificial intelligence system includes a text classification model with feature extraction and classification algorithms. Word embedding-based feature extraction is widely used for 1-dimensional (1D) and 2-dimensional (2D) data, suitable for traditional or deep learning algorithms. However, applying feature extraction to 3-dimensional (3D) data for text classification is limited. Previous studies focused on word embedding for 1D, 2D, and 3D outputs with convolutional neural network (CNN). Yet, using 3D data and CNN did not perform well. Despite using CNN and 3D variants, identifying natural disaster reports remains below 80% accuracy. This research aims to improve identifying earthquakes, floods, and forest fires with 3D data and hybrid CNN long short-term memory (LSTM). The study found models with accuracies of 83.38%, 83.72%, and 89.03% for each disaster type. Hybrid CNN LSTM significantly enhanced identification compared to CNN alone, supported by statistical tests with P value less than 0.0001.
Co-Authors Abdul Gafur Andi Farmadi Dina Arifah Dodon Turianto Nugrahadi Dwi Kartini, Dwi Friska Abadi Imam Tahyudin Irwan Budiman Irwan Budiman Maya Yusida Mera Kartika Delimayanti Muhammad Ali Abubakar Muhammad Alkaff Muhammad Alvin Alfando Muhammad I Mazdadi Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muhammad Rizky Adriansyah Radityo Adi Nugroho Rahmat Ramadhani Riana Riana Rudy Herteno Saragih, Triando Hamonangan Septyan Eka Prastya