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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA IKRA-ITH Informatika : Jurnal Komputer dan Informatika JURIKOM (Jurnal Riset Komputer) Bulletin of Computer Science Research Jurnal Informatika, Komputer dan Bisnis (JIKOBIS) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Media Journal of General Computer Science (MJGCS) Jurnal Pengabdian Masyarakat Unama
Pareza Alam Jusia, Pareza Alam
STIKOM Dinamika Bangsa, Jambi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Comparative analysis of word embedding features to improve the performance of deep learning models on social media data Jasmir, Jasmir; Alam Jusia, Pareza; Arvita, Yulia; Gunardi, Gunardi
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9200

Abstract

In this study, we apply various deep learning methods incorporating word embedding features to evaluate their impact on improving classification performance in sentiment analysis. The methods employed include conditional random field (CRF), bidirectional long short term memory (BLSTM), and convolutional neural network (CNN). Our experiments utilize social media data from restaurant review. By testing different iterations of these deep learning techniques with various word embedding features, we found that the BLSTM algorithm achieved the highest accuracy of 80.00% before integrating word embedding features. After incorporating word embeddings, the BLSTM with the word2vec feature achieved an accuracy of 87.00%. Notably, the CNN showed a significant improvement with the FastText feature. Considering all evaluation metrics—accuracy, precision, recall, and F1-score—the BLSTM algorithm consistently demonstrated the best performance across different word embeddings.
Co-Authors ., Amroni Abdul Rahim Agus Nugroho, Agus Agus Siswanto Ali Sadikin Amroni Arvita, Yulia Aulia, M. Farhan Beny Dodi Sandra Errissya Rasywir Febrina Mardayani, Risa Gunardi Gunardi Gusriyanti, Dwi Ayu Halim, Rina Hartiwi, Yessi Hendrawan Hendrawan Herti Yani, Herti Irfan, Fadhel Muhammad ISW, Ibnu Sani Wijaya Jasmir Jasmir, Jasmir Juliana Juliana Kurniabudi, Kurniabudi Mery MULYADI NOVIANTO, YUDI Nurul Marwiyah, Siska OPHELIA, CHANDY Pahlevi, M. Riza Paramitha, Cindy Pardamean Simanjuntak, Daniel Sintong Pebrianto, Feri Putra, Rafi Septiandi Reflika, Mutiara Riza Pahlevi, Riza ROBY SETIAWAN Rofi’i, Imam SAYID ACHMAD MAULANA Sharipuddin Sharipuddin, Sharipuddin Stefani, Dina Maria Tesaloniki, Viola Wardani, Muhammad WIJAYA, IBNU SANI WILLY RIYADI Yovi Pratama