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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUITA : Jurnal Informatika POSITIF JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sisfokom (Sistem Informasi dan Komputer) JSiI (Jurnal Sistem Informasi) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Tekno Kompak Building of Informatics, Technology and Science Jurnal Repositor Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Algoritma
S, Vinna Rahmayanti
Universitas Muhammadiyah Malang
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Public Health

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Journal : Building of Informatics, Technology and Science

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Perbandingan Kinerja Pre-Trained Word Embedding Terhadap Performa Klasifikasi Sentimen Ulasan Produk Tokopedia Dengan Long Short-Term Memory(LSTM) Dirfas, Naufal Angling; Nastiti, Vinna Rahmayanti Setyaning
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5634

Abstract

The product review dataset is a rapidly growing and interesting source of data to explore. The increase in the number of internet users and customer shopping habits through online stores has a significant impact on the growth of product review data, especially for online stores in Indonesia, such as Tokopedia. The sample data used amounted to 1079. This research aims to evaluate the performance of three types of pre-trained word embeddings, namely FastText, GloVe, and Word2Vec, in the Long Short-Term Memory (LSTM) model for sentiment classification of product reviews on Tokopedia. An automated sentiment classification system is needed to process many product reviews, making it easier for sellers to know what consumers think of their products. This research contributes by evaluating the impact of various pre-trained word embeddings on the performance of LSTM models in sentiment classification tasks. In addition, this research also aims to measure the effectiveness of LSTM models combined with multiple pre-trained word embeddings. By implementing a deep learning architecture, computers can learn and recognize contextual data stored in review sentences. The research was conducted in three stages: model selection, layer setup, and hyperparameter optimization, to feature in-depth testing of the deep learning architecture used and the appropriate combination of layers and parameters to obtain high sentiment classification performance. The experimental results show that FastText with LSTM provides the best performance with 85.08% accuracy, Word2Vec with 84.62% accuracy, and GloVe with 83.04% accuracy. The main contribution of this research is to present an in-depth test of the product review dataset and provide a deep learning architecture along with a combination of layers and parameters that has the best performance in recognizing sentiment on the product review dataset. This architecture achieves higher performance than the BERT method with CNN and BiLSTM layers.
Co-Authors Abdy Yoga Syantara Achmad Rizal Yogaswara Adnan Burhan Hidayat Kiat Ahmad Junjung Sudrajad AINA, LISTYA NUR Akbi, Denar Regata Amelia Deastu Amelia Dwi Deastu Andriani Eka Pramudita Anhar Aria Maulana Asty Try Yuliandari Bagas Aji Aprian Bagus Wicaksono Basuki, Setio Bella Chintia Merita Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma Dede Nor Alfiansyah Dewi Aprilitasari Diah Risqiwati Dignifo Nauval Muhammady Dini Tri Purwaningsih Dirfas, Naufal Angling Evi Dwi Wahyuni Gian Bagus Sadewa Giffari Zakawaly Gita Indah Marthasari Haidar Aldy Eka Nugraha Hilman Hilman Imsa Nur Dwijayanti Kenanga, Larynt Sawfa Lailiyah, Asmaul Lidya Fankky Oktavia Putri Maryaeni Maryaeni Muhammad Gofur Muhammad Rivaldi Asyhari Muhammad Robby Fathuda Naila, Tia Cahyani Naufal Adiftya Falah Novita Daian Marlissa Nur Hayatin Nuryasin, Ilyas Oktaviani, Chintya Tria Diana Pratama, Ramadan Qori Raditya Damayanti Ramadan Pratama Reikisyifa, Clarissa Sanindita Rifky Ahmad Saputra Riska Septiana Putri Saputri, Indah Setiawan, Muhamad Jodi Sirril Asrory Titin Eka Puspitawati Wahyu Andhyka Kusuma Wildan Amru Hidayat Wildan Suharso Wiyono, Briansyah Setio Yufis Azhar Zamah Sari