DOI: https://doi.org/10.21456/vol13iss2pp112-119

Analisis Sentimen Terhadap Pengaruh Minat Belanja Berdasarkan Komentar di Marketplace Menggunakan Metode Recurrent Neural Network (RNN)

*Gerry Santos Lasatira  -  Universitas Kristen Satya Wacana, Indonesia
Kristoko Dwi Hartomo  -  Universitas Kristen Satya Wacana, Indonesia
Irwan Sembiring  -  Universitas Kristen Satya Wacana, Indonesia
Open Access Copyright (c) 2023 JSINBIS (Jurnal Sistem Informasi Bisnis)

Citation Format:
Abstract

Product reviews on the marketplace can provide useful information if they are properly processed. Product review analysis can be performed by merchants to obtain information that can be used to evaluate products and services. It is not enough to look at the number of stars in product review analysis activities; it is also necessary to look at the entire contents of the review comments to determine the intent of the review. This can be done manually in small quantities, but in large quantities, the system is more efficient. In order to understand the intent of the reviews, a system that can effectively analyze many reviews is required. Using the Recurrent Neural Network (RNN) method, this study aims to analyze sentiment on the influence of shopping interest based on comments in the marketplace. The RNN model is trained to recognize positive and negative sentiments using data from the marketplace. The sentiment analysis results will be used to assess the impact on user shopping interest in the marketplace. Sentiment analysis was performed in this study using the RNN method in the GRU/LSTM training model with epochs. The researcher determined the epoch to achieve high accuracy. The data used for model training and testing is separated into training and testing data before it is used. A comparison of 80% of training data and 20% of test data is used to split data. This study uses a training model with 77 epochs and a batch size of 128 to create a system that automatically calculates comment sentiment in the marketplace with a 100% accuracy value and determines positive and negative sentiments.

Note: This article has supplementary file(s).

Fulltext View|Download |  common.other
Untitled
Subject
Type Other
  Download (1MB)    Indexing metadata
Keywords: RNN Method, Comment Data, Marketplace, GRU/LSTM, Wordcloud

Article Metrics:

Article Info
Section: Research Articles
Language : ID
Recent articles
Peran Digital Entrepreneurship Mindset: Keputusan Adopsi Platform Digital Bagi Pelaku Bisnis Prediksi Penjualan Bisnis Rumah Properti Dengan Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA) Pengukuran Prestasi Belajar Mahasiswa Berdasarkan Prediksi Nilai Menggunakan General Linear Model Frontmatter (Front Cover, Editorial Team, Indexing, and Table of Contents) Preface Backmatter (Publication Ethics, Copyright Transfer Agreement for Publishing Form) More recent articles
Most cited articles
Sistem Informasi Penyebaran Penyakit Demam Berdarah Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation Pengukuran Kesadaran Keamanan Informasi Dan Privasi Pada Pengguna Smartphone Android Di Indonesia Data Clustering Menggunakan Metodologi CRISP-DM Untuk Pengenalan Pola Proporsi Pelaksanaan Tridharma Perancangan Model Data Flow Diagram Untuk Mengukur Kualitas Website Menggunakan Webqual 4.0 Implementasi Algoritma K-Nearest Neighbor Sebagai Pendukung Keputusan Klasifikasi Penerima Beasiswa PPA dan BBM More cited articles
  1. Ahn, H. K. dan Park, N. (2021) ‘Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors’, Energies, 14(2), pp. 1–17. Doi: 10.3390/En14020436
  2. Hartomo, K. D. dan Nataliani, Y. (2021) ‘A New Model For Learning-Based Forecasting Procedure By Combining K-Means Clustering And Time Series Forecasting Algorithms’, Peerj Computer Science, 7, P. E534. Doi: 10.7717/Peerj-Cs.534
  3. Ivanedra, K. dan Mustikasari, M. (2019) ‘Implementasi Metode Recurrent Neural Network Pada Text Summarization Dengan Teknik Abstraktif’, Jurnal Teknologi Informasi Dan Ilmu Komputer, 6(4), P. 377. Doi: 10.25126/Jtiik.2019641067
  4. Limbong, J.J.A., Sembiring, I. dan Hartomo, K.D., (2019) ‘Analisis Klasifikasi Sentimen Ulasan Pada E-commerce Shopee Berbasis Word Cloud Dengan Metode Naive Bayes Dan K-Nearest Neighbor Analysis Of Review Sentiment Classification On E-commerce Shopee Word Cloud Based With Naïve Bayes And K-Nearest Neighbor Meth’, Jurnal Teknologi Informasi Dan Ilmu Komputer (Jtiik), 9(2), pp. 347–356. Doi: 10.25126/Jtiik.2s02294960
  5. Khafidatul, I. And Indra, K. (2020) ‘Pengaruh Ulasan Produk, Kemudahan, Kepercayaan, Dan Harga Terhadap Marketplace Shopee Di Mojokerto’, Jurnal Manajemen, 6(1), Pp. 31–42. Available At: Http://Www.Maker.Ac.Id/Index.Php/Maker
  6. Lestari, M., Purnomo, H. D. And Sembiring, I. (2021) ‘Pengaruh E-Payment Trust Terhadap Minat Transaksi Pada E-marketplace Menggunakan Framework Technology Acceptance Model (Tam) 3’, Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(5), P. 977. Doi: 10.25126/Jtiik.2021855212
  7. Muktafin, E. H., Kusrini, K. And Luthfi, E. T. (2020) ‘Analisis Sentimen Pada Ulasan Pembelian Produk Di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing’, Jurnal Eksplora Informatika, 10(1), pp. 32–42. Doi: 10.30864/Eksplora.V10i1.390
  8. Nasir, J. A., Khan, O. S., dan Varlamis, I. (2021) ‘Fake News Detection: A Hybrid CNN-RNN Based Deep Learning Approach’, International Journal Of Information Management Data Insights, 1(1). Doi: 10.1016/J.Jjimei.2020.100007
  9. Ningrum, A.A., Syarif, I., Gunawan, A.I., Satriyanto, E. dan Muchtar, R. (2021) ‘Algoritma Deep Learning-LSTM Untuk Memprediksi Umur Transformator’, Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(3), P. 539. Doi: 10.25126/Jtiik.2021834587
  10. Nurul, M., Soewarno, N. dan Isnalita, I. (2019) ‘Pengaruh Jumlah Pengunjung, Ulasan Produk, Reputasi Toko Dan Status Gold Badge Pada Penjualan Dalam Tokopedia’, E-Jurnal Akuntansi, 28(3), p. 1855. Doi: 10.24843/Eja.2019.V28.I03.P14
  11. Putra, A. K., Nyoto, R. D. dan Sasty, P. H. (2017) ‘Rancang Bangun Aplikasi Marketplace Penyedia Jasa Les Private Di Kota Pontianak Berbasis Web’, Jurnal Sistem Dan Teknologi Informasi (Justin), 5(1), pp. 22–26. Available At: Http://Jurnal.Untan.Ac.Id/Index.Php/Justin/Article/Viewfile/17991/15281
  12. Selle, N., Yudistira, N. And Dewi, C. (2022) ‘Perbandingan Prediksi Penggunaan Listrik Dengan Menggunakan Metode Long Short Term Memory (LSTM) Dan Recurrent Neural Network(RNN)’, Jurnal Teknologi Informasi Dan Ilmu Komputer, 9(1), P. 155. Doi: 10.25126/Jtiik.2022915585
  13. Tarkus, D., Sompie, S. R. U. A. dan Jacobus, A. (2020) ‘Implementasi Metode Recurrent Neural Network Pada Pengklasifikasian Kualitas Telur Puyuh’, Jurnal Teknik Informatika, 15(2)

Last update:

No citation recorded.

Last update: 2024-11-20 20:16:33

No citation recorded.