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All Journal JOIV : International Journal on Informatics Visualization Matrix : Jurnal Manajemen Teknologi dan Informatika Technomedia Journal WIDYA LAKSANA Aptisi Transactions on Technopreneurship (ATT) Bhakti Persada Jurnal Aplikasi IPTEKS Jurnal Praksis dan Dedikasi Sosial Just TI (Jurnal Sains Terapan Teknologi Informasi) MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Komang Ayu Triana Indah
Politeknik Negeri Bali
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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BERT Uncased and LSTM Multiclass Classification Model for Traffic Violation Text Classification Komang Ayu Triana Indah; I Ketut Gede Darma Putra; Made Sudarma; Rukmi Sari Hartati; Minho Jo
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 02 (2024): Vol. 15, No. 2 August 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i02.p04

Abstract

The increasing amount of internet content makes it difficult for users to find information using the search function. This problem is overcome by classifying news based on its context to avoid material that has many interpretations. This research combines the Uncased model BiDirectional Encoder Representations from Transformer (BERT) with other models to create a text classification model. Long Short-Term Memory (LSTM) architecture trains a model to categorize news articles about traffic violations. Data was collected through the crawling method from the online media application API through unmodified and modified datasets. The BERT Uncased-LSTM model with the best hyperparameter combination scenario of batch size 16, learning rate 2e-5, and average pooling obtained Precision, Recall, and F1 values of 97.25%, 96.90%, and 98.10%, respectively. The research results show that the test value on the unmodified dataset is higher than on the modified dataset because the selection of words that have high information value in the modified dataset makes it difficult for the model to understand the context in text classification.
Co-Authors Atmaja, I Made Ari Dwi Suta Bawa, I Gusti Ngurah Bagus Catur Elina Rudiastari Gusti Nyoman Ayu Sukerti Gusti Nyoman Ayu Sukerti, Gusti Nyoman Ayu I G. N. B. Caturbawa I Gusti Ayu Astri Pramitari I Ketut Gede Darma Putra I ketut Gede Darma Putra I Komang Sudiarta I Made Sudarma I Nyoman Gede Arya Astawa IB Putra Manuaba IB Putra Manuaba, IB Putra Ida Bagus Putra Manuaba Irma Nuraeni Salsabila Made Pradnyana Ambara, Made Pradnyana Made Sudarma Made Sudarma Minho Jo Minho Jo Putu Gde Sukarata Rudiastari, Elina Rukmi Sari Hartati Rukmi Sari Hartati Rukmi Sari Hartati Sugeng Santoso