Sentiment Analysis of Shopee Food Application User Satisfaction Using the C4.5 Decision Tree Method

Fersellia Fersellia; Ema  Utami; Ainul  Yaqin

doi:10.33395/sinkron.v8i3.12531

Authors

Fersellia Universitas AMIKOM Yogyakarta, Indonesia
Ema Utami Universitas AMIKOM Yogyakarta, Indonesia
Ainul Yaqin Universitas AMIKOM Yogyakarta, Indonesia

DOI:

10.33395/sinkron.v8i3.12531

Keywords:

Shopee Food, Sentiment Analysis, Decision Tree C4.5, Smote, Undersampling, Combine Oversampling & Undersampling

Abstract

Sentiment analysis on public opinion regarding the shopee food application is an interesting topic in the context of evaluating service quality in the shopee food application. In this digital era, user opinion has a very important role in shaping public perception of the application. Therefore, sentiment analysis is needed to understand user opinion about the shopee food application. This study uses Decision Tree C4.5 to analyze public sentiment on the use of the Shopee Food application on Twitter users. However, beforehand it is necessary to overcome the problem of data imbalance which is common in datasets, where the number of positive, negative, and neutral sentiments is not balanced. To overcome this problem, three different techniques are used, namely SMOTE, undersampling, and a combination of oversampling and undersampling. The results of this study indicate that the SMOTE technique provides better results in overcoming data imbalances and increasing prediction accuracy. With an accuracy of 0.88. the SMOTE technique can provide more accurate sentiment predictions than the undersampling technique and the combination of oversampling and undersampling. This is because SMOTE can synthetically expand the number of minority samples, thereby preventing the loss of information and maintaining variation in the dataset. In conclusion, sentiment analysis on the Shopee Food application on Google Play using the Decision Tree C4.5 algorithm and the SMOTE technique can overcome data imbalances with a prediction accuracy of 0.88. This technique is more efficient than the undersampling technique and the combination of oversampling and undersampling. These results can provide developers with valuable insights to improve app quality and user satisfaction.

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References

Agastya, I. Made Artha. 2018. “Pengaruh Stemmer Bahasa Indonesia Terhadap Peforma Analisis Sentimen Terjemahan Ulasan Film.” Jurnal Tekno Kompak 12(1):18. doi: 10.33365/jtk.v12i1.70.

Bias, Introduced Sentiment. 2021. “JURNAL RESTI Sentiment Classification for Film Reviews by Reducing Additional.” (10):4–10.

Bibi, Raheela. 2019. “Sentiment A Nalysis for Urdu N Ews Tweets U Sing Decision T Ree.” 2019 IEEE 17th International Conference on Software Engineering Research, Management and Applications (SERA) 66–70.

Bisri, Achmad, and Rinna Rachmatika. 2019. “Integrasi Gradient Boosted Trees Dengan SMOTE Dan Bagging Untuk Deteksi Kelulusan Mahasiswa.” Jurnal Nasional Teknik Elektro Dan Teknologi Informasi (JNTETI) 8(4):309. doi: 10.22146/jnteti.v8i4.529.

Budiman, Irwan, and Retma Ramadina. 2015. “Penerapan Fungsi Data Mining Klasifikasi Untuk Prediksi Masa Studi Mahasiswa Tepat Waktu Pada Sistem Informasi Akademik Perguruan Tinggi.” Ijccs x, No.x(1):1–5.

Gurusamy, Vairaprakash, and Kannan S. 2014. “Preprocessing Techniques for Text Mining.” International Journal of Computer Science & Communication Networks 5(1):7–16.

Lia Hananto, April, Shofa Sofiah Hilabi, and Detrie Noviani. 2022. “Design of Customer Satisfaction Application at BCA Kcp Rengasdengklok Using C.45 Algorithm Method.” Buana Information Technology and Computer Sciences (BIT and CS) 3(1):11–16. doi: 10.36805/bit-cs.v3i1.2048.

Mufidah, Farah Syadza, Sri Winarno, Farrikh Alzami, Erika Devi Udayanti, and Ramadhan Rakhmat Sani. 2022. “Analisis Sentimen Masyarakat Terhadap Layanan Shopeefood Melalui Media Sosial Twitter Dengan Algoritma Naïve Bayes Classifier.” JOINS (Journal of Information System) 7(1):14–25. doi: 10.33633/joins.v7i1.5883.

Nurzahputra, Aldi, and Much Aziz Muslim. 2016. “Analisis Sentimen Pada Opini Mahasiswa Menggunakan Natural Language Processing.” Seminar Nasional Ilmu Komputer (Snik):114–18.

Pattiiha, Franly Salmon, and Hendry Hendry. 2022. “Perbandingan Metode K-NN, Naïve Bayes, Decision Tree Untuk Analisis Sentimen Tweet Twitter Terkait Opini Terhadap PT PAL Indonesia.” JURIKOM (Jurnal Riset Komputer) 9(2):506. doi: 10.30865/jurikom.v9i2.4016.

Prasetyo, Karno Ganjar, and Said Mrza Pahlevi. 2019. “Analisis Perbandingan Algoritma Decision Tree Dengan Support Vector Machine Untuk Mendeteksi Kompetensi Mahasiswa Konsentrasi Informatika Komputer Studi Kasus : Politeknik Lp3I Jakarta, Kampus Depok.” Lentera ICT 5(5):11–26.

Pratama, Gama. 2020. “130-64-555-1-10-20200901.” 1:21–34.

Rofiqi, Moh Afif, Abd. Charis Fauzan, Afivatu Pratama Agustin, and Ahmad Agung Saputra. 2019. “Implementasi Term-Frequency Inverse Document Frequency (TF-IDF) Untuk Mencari Relevansi Dokumen Berdasarkan Query.” ILKOMNIKA: Journal of Computer Science and Applied Informatics 1(2):58–64. doi: 10.28926/ilkomnika.v1i2.18.

Sari, Dita Novita, Dita Novita Sari, Firda Adelia, Fita Rosdiana, Belsana Butar Butar, and Muhadi Hariyanto. 2020. “Analisa Sentimen Terhadap Review Produk Kecantikan Menggunakan Metode Naive Bayes Classifier.” JIKA (Jurnal Informatika) 4(3):109. doi: 10.31000/jika.v4i3.3086.

Siringoringo, Rimbun. 2018. “Klasifikasi Data Tidak Seimbang Menggunakan Algoritma SMOTE Dan K-Nearest Neighbor.” Jurnal ISD 3(1):44–49.

Tri Romadloni, Nova, Imam Santoso, and Sularso Budilaksono. 2019. “Perbandingan Metode Naive Bayes, Knn Dan Decision Tree Terhadap Analisis Sentimen Transportasi Krl Commuter Line.” Jurnal IKRA-ITH Informatika 3(2):1–9.

Vania, Izella, and Remista Simbolon. 2021. “Pengaruh Promo ShopeeFood Terhadap Minat Beli Pengguna Shopee (Di Daerah Tangerang Selatan).” Fakultas Ekonomi Universitas Advent Indonesia 46–58.

Watrianthos, Ronal, Sudi Suryadi, Deci Irmayani, Marnis Nasution, and Elida F. S. Simanjorang. 2019. “Sentiment Analysis of Traveloka App Using Naïve Bayes Classifier Method.” International Journal of Scientific and Technology Research 8(7):786–88.