Analisis Sentimen Pada Rating Aplikasi Shopee Menggunakan Metode Decision Tree Berbasis SMOTE

Christian Cahyaningtyas; Yessica Nataliani; Indrastanti Ratna Widiasari

doi:10.24246/aiti.v18i2.173-184

Authors

Christian Cahyaningtyas christian cahyaningtyas
Yessica Nataliani Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Salatiga
Indrastanti Ratna Widiasari Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Salatiga

DOI:

https://doi.org/10.24246/aiti.v18i2.173-184

Keywords:

Sentiment Analysis, Rating, Decision Tree

Abstract

Text mining research that performs the categorization procedure on text documents is known as sentiment analysis. Sentiment analysis is the process of extracting a person's ideas, emotions, and evaluations expressed about a certain issue using natural language approaches. Researchers used the Decision Tree approach to do sentiment analysis on the Shopee application rating. The goal of this research is to find out how accurate this Shopee app is and what users think about it. The accuracy value of 99.91 percent, AUC (Area Under Curve) 0.999, recall 99.88 percent, and precision value 99.98 percent were obtained using the Decision Tree method using SMOTE (Synthetic Minority Oversampling Technique). The accuracy of the Decision Tree method without SMOTE is 99.89 percent, the AUC (Area Under Curve) is 0.950, the recall is 99.88 percent, and the precision is 99.98 percent. Based on the findings of the current study, it can be concluded that SMOTE has an effect on value accuracy and AUC (Area Under Curve), but has no effect on recall and precision values, and that the results are the same whether SMOTE is used or not. The accuracy value achieved differs by 0.02 percent, whereas the AUC differs by 0.049

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References

D. H. Jayani, “Jumlah Pengunjung Tokopedia Kalahkan Shopee Pada Kuartal I-2021,” 2021. https://databoks.katadata.co.id/datapublish/2021/06/11/jumlah-pengunjung-tokopedia-kalahkan-shopee-pada-kuartal-i-2021#.

N. Herlinawati, Y. Yuliani, S. Faizah, W. Gata, and Samudi, “Analisis Sentimen Zoom Cloud Meetings di Play Store Menggunakan Naïve Bayes dan Support Vector Machine,” CESS (Journal Comput. Eng. Syst. Sci., vol. 5, no. 2, pp. 293–298, 2020, doi: 10.24114/cess.v5i2.18186.

M. Rezki, D. N. Kholifah, M. Faisal, Priyono, and R. Suryadithia, “Analisis Review Pengguna Google Meet dan Zoom Cloud Meeting Menggunakan Algoritma Naïve Bayes,” J. Infortech, vol. 2, no. 2, pp. 264–270, 2020, doi: 10.31294/infortech.v2i2.9286.

A. R. Sentiaji and A. M. Bachtiar, “Analisis Sentimen Terhadap Acara Televisi Berdasarkan Opini Publik,” J. Ilm. Komput. dan Inform., pp. 1–6, 2014.

R. Sari, “Analisis Sentimen Pada Review Objek Wisata Dunia Fantasi Menggunakan Algoritma K-Nearest Neighbor (K-Nn),” EVOLUSI J. Sains dan Manaj., vol. 8, no. 1, pp. 10–17, 2020, doi: 10.31294/evolusi.v8i1.7371.

M. R. Fajriansyah and Siswanto, “Analisis Sentimen Pengguna Twitter Terhadap Partai Politik Pendukung Calon Gubernur Di Jakarta Menggunakan Algoritma C4 . 5 Decision Tree Learning,” SKANIKA, vol. 1, no. 2, pp. 697–703, 2018, [Online]. Available: https://jom.fti.budiluhur.ac.id/index.php/SKANIKA/article/view/278.

Y. A. Singgalen, “Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review,” J. Inf. Syst. Informatics, vol. 3, no. 2, pp. 278–302, 2021.

R. Puspita and A. Widodo, “Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS,” J. Inform. Univ. Pamulang, vol. 5, no. 4, pp. 646–654, 2020, doi: 10.32493/informatika.v5i4.7622.

B. Laurensz and E. Sediyono, “Analisis Sentimen Masyarakat terhadap Tindakan Vaksinasi dalam Upaya Mengatasi Pandemi Covid-19,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 10, no. 2, pp. 118–123, 2021.

R. B. Trianto, A. Triyono, and D. M. P. Arum, “Klasifikasi Rating Otomatis pada Dokumen Teks Ulasan Produk Elektronik Menggunakan Metode N-gram dan Naïve Bayes,” J. Inform. Univ. Pamulang, vol. 5, no. 3, pp. 295–301, 2020, doi: 10.32493/informatika.v5i3.6110.

L. H. Tampubolon, M. A. Fauzi, and Y. A. Sari, “Prediksi Rating pada Ulasan Produk Kecantikan menggunakan Metode SO-CAL in an Inheritance-based,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 6, pp. 5765–5771, 2019.

K. G. Prasetyo and S. M. Pahlevi, “Analisis Perbandingan Algoritma Decision Tree Dengan Support Vector Machine Untuk Mendeteksi Kompetensi Mahasiswa Konsentrasi Informatika Komputer Studi Kasus : Politeknik Lp3I Jakarta , Kampus Depok,” J. Lentera ICT, vol. 5, no. 2, pp. 11–26, 2019.

N. T. Romadloni, I. Santoso, and S. Budilaksono, “Perbandingan Metode Naive Bayes , Knn Dan Decision Tree Terhadap Analisis Sentimen Transportasi KRL Commuter Line,” J. IKRA-ITH Inform., vol. 3, no. 2, pp. 1–9, 2019.

R. Siringoringo, “Klasifikasi Data Tidak Seimbang Menggunakan Algoritma SMOTE dan k-Nearest Neighbor,” J. ISD, vol. 3, no. 1, pp. 44–49, 2018.

L. D. Perwara, F. A. Bachtiar, and Indriati, “Penerapan Algoritma Decision Tree C4 . 5 Untuk Deteksi Fraud Pada Kartu Kredit dengan Oversampling Synthetic Minority Technique ( SMOTE ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 8, pp. 2664–2669, 2020.

A. Franseda, W. Kurniawan, A. Sita, and W. Gata, “Integrasi Metode Decision Tree dan SMOTE untuk Klasifikasi Data Kecelakaan Lalu Lintas,” JUSTIN (Jurnal Sist. dan Teknol. Informasi), vol. 08, no. 3, pp. 282–290, 2020, doi: 10.26418/justin.v8i3.40982.

D. A. Ningtyas, M. Wahyudi, and N. Nurajijah, “Klasifikasi Siswa SMK Berpotensi Putus Sekolah Menggunakan Algoritma Decision Tree, Support Vector Machine Dan Naive Bayes,” J. Khatulistiwa Inform., vol. 7, no. 2, pp. 85–90, 2019, doi: 10.31294/jki.v7i2.6839.