Analisis Sentimen Pengguna pada Aplikasi Tokopedia Menggunakan Algoritma Convolutional Neural Network
- Alip Maskhuri Universitas Aisyiyah Yogyakarta, Yogyakarta, Indonesia
-
Tikaridha Hardiani
*
Universitas Aisyiyah Yogyakarta, Yogyakarta,
Indonesia
(*) Corresponding Author
Abstract
The Covid-19 pandemic in 2020 accelerated digital transformation across various sectors, including e-commerce. Tokopedia, Indonesia's largest e-commerce platforms, has experienced significant dynamics in user reviews that can impact its reputation. This study aims to analyze the sentiment of Tokopedia user reviews collected from the Google Play Store and the social media platform X using the Convolutional Neural Network (CNN) algorithm. The research is motivated by the increasing competition in the e-commerce industry, requiring companies to understand consumer sentiment to improve their services. The methodology includes data collection through text mining, data preprocessing, automatic labeling using the Pre-Trained IndoBERT model, and splitting the dataset into training, validation, and testing sets. A total of 15,751 reviews were sentiment with 8,885 classified as negative, 3,860 as neutral, and 3,006 as positive. The CNN algorithm was applied to classify these reviews, and the results showed that the model achieved an accuracy of 83%. The model performed best in recognizing negative sentiment but struggled to distinguish between neutral and positive sentiments due to data imbalance. This study recommends collecting more data to achieve a balanced class distribution and exploring pre-trained models such as IndoBERT or IndoNLU to enhance sentiment analysis accuracy.
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References
Pusat Data dan Sistem Informasi, Perdagangan Digital (E-Commerce) Indonesia Periode 2023, Kementerian Perdagangan Republik Indonesia, 2024. [Online]. Available: https://satudata.kemendag.go.id/ringkasan/produk/perdagangan-digital-e-commerce-indonesia-periode-2023. [Accessed: Dec. 22, 2024]
W. Kurnia, “Sentimen Analisis Aplikasi E-Commerce Berdasarkan Ulasan Pengguna Menggunakan Algoritma Stochastic Gradient Descent,” Jurnal Teknologi dan Sistem Informasi vol. 4, no. 1, pp. 138–143, 2023, doi: 10.33365/jtsi.v4i2.2561.
R. Apriani et al., “Analisis Sentimen Dengan Naïve Bayes Terhadap Komentar Aplikasi Tokopedia,” Jurnal Rekayasa Teknologi Nusa Putra vol. 6, no. 01, 2019, doi : 10.52005/rekayasa.v6i1.86.
J. A. Pramesthi, "Pengaruh BTS sebagai Brand Ambassador Tokopedia terhadap Brand Switching," Preprint, Jun. 2020, doi: 10.13140/RG.2.2.27640.67842/2. [Online]. Available: https://www.researchgate.net/publication/341867917_Pengaruh_BTS_Sebagai_Brand_Ambassador_Tokopedia_Terhadap_Brand_Switching. [Accessed: Dec. 23, 2024]
A. Safira and F. N. Hasan, “Analisis Sentimen Masyarakat Terhadap Paylater Menggunakan Metode Naive Bayes Classifier,” Jurnal Sistem Informasi, vol. 5, no. 1, 2023, doi : 10.31849/zn.v5i1.12856.
S. N. Listyarini and D. A. Anggoro, “Analisis Sentimen Pilkada di Tengah Pandemi Covid-19 Menggunakan Convolution Neural Network (CNN),” Jurnal Pendidikan dan Teknologi Indonesia, vol. 1, no. 7, pp. 261–268, Jul. 2021, doi: 10.52436/1.jpti.60.
F. Rumaisa, Y. Puspitarani, A. Rosita, A. Zakiah, and S. Violina, “Penerapan Natural Language Processing (NLP) Di Bidang Pendidikan,” Jurnal Inovasi Masyarakat, vol. 01, no. 3, 2021 doi: 10.33197/jim.vol1.iss3.2021.799.
M. H. A. Sunata, F. Irwiensyah, and F. N. Hasan, “Analisis Sentimen Calon Presiden 2024 di Media Sosial X Menggunakan Naive Bayes dan SMOTE,” Jurnal Media Informatika Budidarma, vol. 8, no. 3, p. 1313, Jul. 2024, doi: 10.30865/mib.v8i3.7708.
A. Faadilah, “Analisis Sentimen Pada Ulasan Aplikasi Tokopedia di Google Play Store Menggunakan Metode Long Short Term Memory,” Skripsi, Program Studi Matematika, Fakultas Sains dan Teknologi,” UIN Syarif Hidayatullah Jakarta, 2020
S. M. Salsabila, A. Murtopo, A. Nurul Fadhilah, "Analisis Sentimen Pelanggan Tokopedia Menggunakan Metode Naïve Bayes Classifier," Jurnal Minfo Polgan, vol. 11, no. 2, 2022, DOI: 10.33395/jmp.v11i2.11640.
M. I. Putri and I. Kharisudin, “Penerapan Synthetic Minority Oversampling Technique (SMOTE) Terhadap Analisis Sentimen Data Review Pengguna Aplikasi Marketplace Tokopedia,” PRISMA, Prosiding Seminar Nasional Matematika, vol. 5, pp. 759–766, 2022, [Online]. Available: https://journal.unnes.ac.id/sju/prisma/article/download/54577/21107/. [Accessed: Dec. 23, 2024]
A. Syah, F. Nurdiyansyah, and A. Y. Rahman, “Analisis Sentimen Aplikasi Shopee, Tokopedia, Lazada Dan Blibli Menggunakan Leksikon Dan Random Forest,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3S1, Oct. 2024, doi: 10.23960/jitet.v12i3S1.5155.
I. Saputra et al., “Analisis Sentimen Pengguna Marketplace Bukalapak dan Tokopedia di Twitter Menggunakan Machine Learning,” Faktor Exacta, vol. 13, no. 4, p. 200, Feb. 2021, doi: 10.30998/faktorexacta.v13i4.7074.
R. Maulana et al., “Komparasi Algoritma Naive Bayes Dan K-Nearest Neighbor Pada Analisis Sentimen Terhadap Ulasan Pengguna Aplikasi Tokopedia,” Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika, vol. 17, no. 2, doi: 10.47111/JTI.
A. Ernawati, A. Ofta Sari, S. Nurhaliza Sofyan, M. Iqbal, and R. Farta Wijaya, “Implementasi Algoritma Naïve Bayes dalam Menganalisis Sentimen Review Pengguna Tokopedia pada Produk Kesehatan,” Bulletin of Information Technology (BIT), vol. 4, no. 4, pp. 533–543, 2023, doi: 10.47065/bit.v3i1.
A. Mustofa and R. Novita, “Klasifikasi Sentimen Masyarakat Terhadap Pemberlakuan Pembatasan Kegiatan Masyarakat Menggunakan Text Mining Pada Twitter,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 1, Jun. 2022, doi: 10.47065/bits.v4i1.1628.
T. Ridwansyah, “Implementasi Text Mining Terhadap Analisis Sentimen Masyarakat Dunia Di Twitter Terhadap Kota Medan Menggunakan K-Fold Cross Validation Dan Naïve Bayes Classifier,” Kajian Ilmiah Informatika dan Komputer, vol. 2, no. 5, pp. 178–185, 2022, doi : 10.30865/klik.v2i5.362.
T. Hardiani, “Analisis Clustering Kasus Covid 19 di Indonesia Menggunakan Algoritma K-Means,” Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), vol. 11, no. 2, pp. 156–165, Aug. 2022, doi: 10.23887/janapati.v11i2.45376.
R. D. Himawan dan E. Eliyani, “Perbandingan Akurasi Analisis Sentimen Tweet terhadap Pemerintah Provinsi DKI Jakarta di Masa Pandemi,” Jurnal Edukasi dan Penelitian Informatika, vo. 07, no. 1, 2021, doi: 10.26418/jp.v7i1.41728.
P. L. Parameswari and Prihandoko, “Penggunaan Convolutional Neural Network Untuk Analisis Sentimen Opini Lingkungan Hidup Kota Depok Di Twitter,” Jurnal Ilmiah Teknologi dan Rekayasa, vol. 27, no. 1, pp. 29–42, 2022, doi: 10.35760/tr.2022.v27i1.4671.
N. L. P. C. Savitri, R. A. Rahman, R. Venyutzky, and N. A. Rakhmawati, “Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 7, no. 1, Apr. 2021, doi: 10.28932/jutisi.v7i1.3216.
B. A. Yuniarossy et al., “Analisis Sentimen Terhadap Isu Feminisme Di Twitter Menggunakan Model Convolutional Neural Network (CNN),” Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika, vol. 5, no. 1, 2024, doi: 10.46306/lb.v5i1.585.
G. Y. Sitio, S. A. Rumapea, P. Lumbanraja “Analisis Sentimen Pemindahan Ibu Kota Negara Di Media Sosial Twitter Menggunakan metode Convolutional Neural Network (CNN),” Jurnal Ilmiah Teknik Informatika, vol 03. No. 02, 2023, [Online]. Available: https://ejurnal.methodist.ac.id/index.php/methotika/article/view/2680. [Accessed: Dec. 23, 2024]
A. S. Simbolon, N. I. Pangaribuan, and N. M. Aruan, “Analisis Sentimen Aplikasi E-Learning Selama Pandemi Covid-19 Dengan Menggunakan Metode Support Vector Machine Dan Convolutional Neural Network,” Seminastika, vol. 3, no. 1, pp. 16–25, Nov. 2021, doi: 10.47002/seminastika.v3i1.236.
E. Y. Hidayat and D. Handayani, “Penerapan 1D-CNN untuk Analisis Sentimen Ulasan Produk Kosmetik Berdasar Female Daily Review,” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 8, no. 3, pp. 153–163, Jan. 2023, doi: 10.25077/teknosi.v8i3.2022.153-163.
S. Sartini, “Analisis Sentimen Twitter Bahasa Indonesia Menggunakan Algoritma Convolutional Neural Network,” Skripsi, Program Studi Pendidikan Teknik Informatika dan Komputer, Fakultas Teknik, Universitas Negeri Semarang, 2020.
K. I. Gunawan and J. Santoso, “Multilabel Text Classification Menggunakan SVM dan Doc2Vec Classification Pada Dokumen Berita Bahasa Indonesia,” Journal of Information System,Graphics, Hospitality and Technology, vol. 3, no. 01, pp. 29–38, Apr. 2021, doi: 10.37823/insight.v3i01.126.
R. Merdiansah, S. Siska and A. Ali Ridha, “Analisis Sentimen Pengguna X Indonesia Terkait Kendaraan Listrik Menggunakan IndoBERT,” Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI, vol. 7, no. 1, pp. 221–228, 2024, doi : 10.55338/jikomsi.v7i1.2895.
N. T. Adam, Z. A. Tyas, and T. Hardiani, “Deteksi Gestur Sistem Isyarat Bahasa Indonesia Menggunakan Metode Deep learning SSD MobileNet V2 FPNLite,” Sainteks, vol. 21, no. 2, p. 129, Oct. 2024, doi: 10.30595/sainteks.v21i2.24006.
W. Astriningsih, “Identifikasi Multi Aspek dan Sentimen Analisis pada Review Hotel Menggunakan Deep learning,” Yogyakarta,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 10, no. 03, 2023. [Online]. Available: https://jurnal.mdp.ac.id/index.php/jatisi/article/download/5321/1603/. [Accessed: Dec. 23, 2024]
T. Hardiani and E. P. Silmina, “Comparative Analysis of K-Means and K-Medoids Algorithms in New Student Admission,” International Journal of Informatics and Computation, vol. 6, no. 2, pp. 1–11, Dec. 2024 doi : 10.35842/ijicom.v6i2.91.
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