Analisis Sentimen Terhadap Ulasan Aplikasi Shopee di Google Play Store Menggunakan Metode TF-IDF dan Long Short-Term Memory)

DOI: https://doi.org/10.33650/jeecom.v6i2.8713

Authors (s)


(1)  Musfiroh Musfiroh   (Universitas Nurul Jadid)  
        Indonesia
(2) * Abu Tholib   (Universitas Nurul Jadid)  
        Indonesia
(3)  Zainal Arifin   (Universitas Nurul Jadid)  
        Indonesia
(*) Corresponding Author

Abstract


Pengunjung Shopee semakin meningkat dari tahun 2022 hingga 2023. Karena peningkatan itu, semakin banyak pengguna yang berkomentar negatif atau positif. Maka, mengetahui sentimen pengguna pada aplikasi Shopee dapat mengetahui perilaku pelanggan dan meningkatkan penjualan. Penelitian ini menggunakan metode TF-IDF dan algoritma LSTM. Adapun tahapan penelitian seperti scrapping data yang menggunakan ulasan pengguna aplikasi Shopee di Google Play Store sebanyak 3565 data. Lalu data dikategorikan menjadi tiga kelas: positif, netral, dan negatif. Proses preprocessing meliputi Tokenization, Normalization, Stopword, dan Stemming. Selanjutnya dilakukan proses train data dan data test sebesar 8:2. Lalu melakukan vektorisasi dengan TF-IDF, melatih model dengan penggabungan TF-IDF dan LSTM (Long Short-Term Memory), serta menggunakan metrics untuk mengevaluasi model dan visualisasi menggunakan word cloud. menghasilkan akurasi sebesar 83% dengan nilai loss (kerugian) sebesar 0.1385. Model memiliki kemampuan cukup baik dalam memprediksi kelas negatif dan positif tetapi kurang efektif untuk kelas netral karena data yang kurang seimbang. 

Keywords

Analisis Sentimen, LSTM, Shopee, Text Mining, TF-IDF



Full Text: PDF



References


A. Ahdiat, “5 E-Commerce dengan Pengunjung Terbanyak Kuartal I 2023,” Katadata. Accessed: Apr. 25, 2024. [Online]. Available: https://databoks.katadata.co.id/datapublish/2023/05/03/5-e-commerce-dengan-pengunjung-terbanyak-kuartal-i-2023

I. S. K. Idris, Y. A. Mustofa, and I. A. Salihi, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM),” Jambura Journal of Electrical and Electronics Engineering, vol. 5, no. 1, pp. 32–35, Jan. 2023, doi: 10.37905/jjeee.v5i1.16830.

R. Wahyudi et al., “Analisis Sentimen pada review Aplikasi Grab di Google Play Store Menggunakan Support Vector Machine,” JURNAL INFORMATIKA, vol. 8, no. 2, 2021, [Online]. Available: http://ejournal.bsi.ac.id/ejurnal/index.php/ji

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.

E. Hasibuan and E. A. Heriyanto, “ANALISIS SENTIMEN PADA ULASAN APLIKASI AMAZON SHOPPING DI GOOGLE PLAY STORE MENGGUNAKAN NAIVE BAYES CLASSIFIER,” JTS, vol. 1, no. 3.

Y. Tian, Y. Zhang, and H. Zhang, “Recent advances in stochastic gradient descent in deep learning,” Mathematics, vol. 11, no. 3, p. 682, 2023.

S. S. Bafjaish, “Comparative analysis of Naive Bayesian techniques in health-related for classification task,” Journal of Soft Computing and Data Mining, vol. 1, no. 2, pp. 1–10, 2020.

F. N. Fajri and S. Syaiful, “Klasifikasi Nama Paket Pengadaan Menggunakan Long Short-Term Memory (LSTM) Pada Data Pengadaan,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 3, Dec. 2022, doi: 10.47065/bits.v4i3.2635.

Srivatsavaya Prudhviraju, “LSTM — Implementation, Advantages and Diadvantages,” Medium. Accessed: Mar. 23, 2024. [Online]. Available: https://medium.com/@prudhviraju.srivatsavaya/lstm-implementation-advantages-and-diadvantages-914a96fa0acb

M. I. Syafaah and L. Lestandy, “Emotional Text Classification Using TF-IDF (Term Frequency-Inverse Document Frequency) And LSTM (Long Short-Term Memory),” 2022. [Online]. Available: https://atapdata.ai/dataset/192/HIMPUNAN_DATA_E

C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data,” Front Energy Res, vol. 9, p. 652801, 2021.

A. Tholib and M. Kom, “Implementasi Algoritma Machine Learning Berbasis Web dengan Framework Streamlit,” Pustaka Nurja, 2023.

M. Z. Yumarlin, J. E. Bororing, S. Rahayu, and J. A. Putra, “Analisis Sentimen Pengguna Apliaksi Shopee Menggunakan Metode Naive Bayes Classifier dan K-NN,” Smart Comp: Jurnalnya Orang Pintar Komputer, vol. 12, no. 3, pp. 745–753, 2023.

N. Aula, M. Ula, and L. Rosnita, “ANALISIS SENTIMEN REVIEW CUSTOMER TERHADAP PERUSAHAAN EKSPEDISI JNE, J&T EXPRESS DAN POS INDONESIA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) ANALYSIS OF CUSTOMER REVIEW SENTIMENT TO JNE, J&T EXPRESS AND POS INDONESIA EXPEDITION COMPANIES USING SVM METHOD,” Journal of Informatics and Computer Science, vol. 9, no. 1, 2023.

Rianto, A. B. Mutiara, E. P. Wibowo, and P. I. Santosa, “Improving the accuracy of text classification using stemming method, a case of non-formal Indonesian conversation,” J Big Data, vol. 8, pp. 1–16, 2021.

A. R. Lubis and M. K. M. Nasution, “Twitter Data Analysis and Text Normalization in Collecting Standard Word,” Journal of Applied Engineering and Technological Science (JAETS), vol. 4, no. 2, pp. 855–863, 2023.

K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.

H. Benhar, A. Idri, and J. L. Fernández-Alemán, “Data preprocessing for heart disease classification: A systematic literature review,” Comput Methods Programs Biomed, vol. 195, p. 105635, 2020, doi: https://doi.org/10.1016/j.cmpb.2020.105635.

S. Shekhar, “Twitter sentiment analysis: An Arabic text mining approach based on COVID-19,” International Research Journal of Engineering and Technology, 2021, [Online]. Available: www.irjet.net

M. E. Rianto, M. Maulidiansyah, and A. Tholib, “Implementasi AI Chatbot Sebagai Support Assistant Website Universitas Nurul Jadid Menggunakan Algoritma Long Short-Term Memory (LSTM),” Journal of Electrical Engineering and Computer (JEECOM), vol. 6, no. 1, pp. 267–275, 2024.

M. A. Rosid, A. S. Fitrani, I. R. I. Astutik, N. I. Mulloh, and H. A. Gozali, “Improving Text Preprocessing For Student Complaint Document Classification Using Sastrawi,” IOP Conf Ser Mater Sci Eng, vol. 874, no. 1, p. 012017, Jun. 2020, doi: 10.1088/1757-899X/874/1/012017.

S. Sarica and J. Luo, “Stopwords in technical language processing,” PLoS One, vol. 16, no. 8, pp. e0254937-, Aug. 2021, [Online]. Available: https://doi.org/10.1371/journal.pone.0254937

F. Nur Fajri, A. Tholib, and W. Yuliana, “Application of Machine Learning Algorithm for Determining Elective Courses in Informatics Study Program,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 8, no. 3, Dec. 2022, doi: 10.28932/jutisi.v8i3.3990.

M. M. Hoque, “An analytical approach to analyze the popular word search from nineteen-year news dataset using Natural language processing technique,” Centria University of Applied Sciences, 2023.

A. R. Lubis, M. K. M. Nasution, O. S. Sitompul, and E. M. Zamzami, “The effect of the TF-IDF algorithm in times series in forecasting word on social media,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, no. 2, pp. 976–984, Apr. 2021, doi: 10.11591/ijeecs.v22.i2.pp976-984.

G. S. N. Murthy, S. R. Allu, B. Andhavarapu, M. Bagadi, and M. Belusonti, “Text based sentiment analysis using LSTM,” Int. J. Eng. Res. Tech. Res, vol. 9, no. 05, 2020.

A. Tholib, N. K. Agusmawati, and F. Khoiriyah, “Prediksi Harga Emas Menggunakan Metode Lstm Dan Gru,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 11, no. 3, 2023.

X. Luo and K. Tang, “Funny3 at SemEval-2020 Task 7: Humor Detection of Edited Headlines with LSTM and TFIDF Neural Network System,” in Proceedings of the Fourteenth Workshop on Semantic Evaluation, Stroudsburg, PA, USA: International Committee for Computational Linguistics, 2020, pp. 1013–1018. doi: 10.18653/v1/2020.semeval-1.132.

U. O. R. Permatasari, W. J. Shudiq, and M. Jasri, “Prediksi Kelayakan Mahasiswa sebagai Penerima Beasiswa Bank Indonesia pada Tahap Seleksi Administrasi di Universitas Nurul Jadid Menggunakan Algoritma K Nearest Neighbor,” Journal of Electrical Engineering and Computer (JEECOM), vol. 6, no. 1, pp. 252–260, 2024.

A. Puji Astuti, S. Alam, and I. Jaelani, “Komparasi Algoritma Support Vector Machine dengan Naive Bayes Untuk Analisis Sentimen Pada Aplikasi BRImo,” Jurnal Bangkit Indonesia, vol. 11, no. 2, pp. 1–6, Oct. 2022, doi: 10.52771/bangkitindonesia.v11i2.196.


Dimensions, PlumX, and Google Scholar Metrics

10.33650/jeecom.v6i2.8713


Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Musfiroh Musfiroh, Abu Tholib, Zainal Arifin

 
This work is licensed under a Creative Commons Attribution License (CC BY-SA 4.0)

Journal of Electrical Engineering and Computer (JEECOM)
Published by LP3M Nurul Jadid University, Indonesia, Probolinggo, East Java, Indonesia.