Keywords
customer satisfaction
machine learning
sentiment prediction
user sentiment
How to Cite
Abstract
This study examines user sentiment towards Shopee Xpress delivery times using machine learning techniques. We collected 497 reviews from platforms like X and the Google Play Store, leveraging the valuable feedback despite its unstructured and informal nature. After labelling 398 reviews for model training and reserving 99 for sentiment prediction, we implemented two classification algorithms: Support Vector Machine (SVM) and Logistic Regression. These models categorised sentiments into negative, neutral, and positive classes. Despite class imbalance in the training data, SVM outperformed Logistic Regression with an accuracy of 93%, demonstrating a more balanced performance across sentiment categories compared to Logistic Regression's 90% accuracy. Both models showed consistent sentiment prediction on new data. Our findings highlight the potential of sentiment analysis as a valuable tool for Shopee Xpress to understand customer perceptions and improve delivery experiences. By providing actionable insights, this study can inform logistics improvements and enhance customer satisfaction. Future research could benefit from collaborating with Shopee to access internal data and integrating additional data sources for more comprehensive insights, ultimately driving business growth and customer loyalty. This study contributes to the growing body of research on sentiment analysis in logistics and e-commerce.
References
Y. Septiawan, A. Aglasia, and D. A. Muktiawan, “Peningkatan Performa Analisis Sentimen Ulasan Pelanggan terhadap Layanan Pengiriman Menggunakan Model Naïve Bayes yang Dioptimalkan dengan PSO,” ROUTERS: Jurnal Sistem dan Teknologi Informasi, vol. 3, no. 1, pp. 10–19, 2025, doi: 10.25181/rt.v3i1.4001.
I. R. Afandi, F. Noor Hasan, A. A. Rizki, N. Pratiwi, and Z. Halim, “Analisis Sentimen Opini Masyarakat Terkait Pelayanan Jasa Ekspedisi Anteraja dengan Metode Naive Bayes,” Jurnal Linguistik Komputasional, vol. 5, no. 2, pp. 63–70, 2022, doi: https://doi.org/10.26418/jlk.v5i2.107.
N. A. Qadri, S. Alam, and M. Taufik, “Analisis Sentimen Brand Barenbliss Menggunakan Machine Learning (Studi Kasus: E-Commerce Shopee),” INNOVATIVE: Journal of Social Science Research, vol. 5, no. 1, pp. 1–12, 2025, doi: https://doi.org/10.31004/innovative.v5i1.16163.
T. Widarmanti and A. Munisera, “Analisis Sentimen Mengenai Kualitas Layanan Jasa Ekspedisi Shopee Xpress pada Media Sosial Twitter,” Jurnal Ilmiah Universitas Batanghari Jambi, vol. 23, no. 3, p. 3370, 2023, doi: 10.33087/jiubj.v23i3.4077.
M. Taufiq, Recky, and Y. P. Sari, “Pengaruh Kompensasi, Motivasi, dan Kepuasan Kerja Terhadap Kinerja Karyawan pada Shopee Express Kabupaten Bandung,” JURNAL ECONOMICA, vol. 12, no. 2, pp. 226–235, 2024, doi: https://doi.org/10.46750/economica.v12i2.297.
S. Nur Rismanah, R. Astuti, and F. M. Basysyar, “Penerapan Algoritma Support Vector Machine dalam Menganalisis Sentimen Ulasan Pelanggan Shopeefood Berdasarkan Twitter,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 1, pp. 406–412, 2024, doi: 10.36040/jati.v8i1.8401.
A. Simanungkalit, J. P. P. Naibaho, and A. De Kweldju, “Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi Shopee Menggunakan Algoritma Naïve Bayes,” Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi, vol. 13, no. 1, p. 659, 2024, doi: 10.35889/jutisi.v13i1.1826.
A. M. Putri, W. K. Nofa, and D. A. P. Hapsari, “Penerapan Metode Bert untuk Analisis Sentimen Ulasan Pengguna Aplikasi Segari di Google Play Store,” JUIT: Jurnal Ilmiah Teknik, vol. 4, no. 1, pp. 89–104, 2025, doi: https://doi.org/10.56127/juit.v4i1.1902.
Y. Handika, I. F. Hanif, and F. N. Hasan, “Analysis of Public Sentiment Towards POLRI’s Performance using Naive Bayes and K-Nearest Neighbors,” IJID (International Journal on Informatics for Development), vol. 13, no. 1, pp. 386–399, 2024, doi: 10.14421/ijid.2024.4500.
S. Heristian, M. Napiah, and W. Erawati, “Analisis Sentimen Ulasan Pelanggan Menggunakan Algoritma Naive Bayes pada Aplikasi Gojek,” Computer Science (CO-SCIENCE), vol. 5, no. 1, pp. 35–41, 2025, doi: https://doi.org/10.31294/coscience.v5i1.7775.
Tukino, “Penerapan Algoritma Convolutional Neural Network untuk Klasifikasi Sentimen Pada Layanan e-Commerce,” JURNAL DESAIN DAN ANALISIS TEKNOLOGI (JDDAT), vol. 4, no. 1, pp. 44–53, 2025, doi: https://doi.org/10.58520/jddat.v4i1.72.
M. I. Burhan, “Analisis Sentimen Publik Terhadap Aplikasi Mypertamina pada Google Playstore,” Jurnal Manajemen Perbankan Keuangan Nitro, vol. 8, no. 1, pp. 1–12, 2025, doi: 10.56858/jmpkn.v8i1.390.
B. Kholifah, I. Thoib, N. Sururi, and N. D. Kurnia, “Analisis Sentimen Warganet Terhadap Isu Layanan Transportasi Online Berbasis InSet Lexicon Menggunakan Logistic Regression,” Kumpulan jurnaL Ilmu Komputer (KLIK), vol. 11, no. 1, pp. 14–25, 2024, doi: http://dx.doi.org/10.20527/klik.v11i1.655.
N. Sari, M. Jazman, T. K. Ahsyar, Syaifullah, and A. Marsal, “Penerapan Algoritma Klasifikasi Naive Bayes dan Support Vector Machine untuk Analisis Sentimen Cyberbullying Bilingual di Aplikasi X,” Sistemasi: Jurnal Sistem Informasi, vol. 14, no. 1, pp. 211–224, 2025, doi: https://doi.org/10.32520/stmsi.v14i1.4799.
T. G. W. M. Sidabutar and D. Juardi, “Analisis Sentimen Masyarakat Terhadap Penggunaan Halodoc Sebagai Layanan Telemedicine di Indonesia,” JITET (Jurnal Informatika dan Teknik Elektro Terapan), vol. 13, no. 1, pp. 571–575, 2025, doi: http://dx.doi.org/10.23960/jitet.v13i1.5682.
B. Restya and N. Cahyono, “Optimasi Metode Klasifikasi Menggunakan FastText dan Grid Search pada Analisis Sentimen Ulasan Aplikasi SeaBank,” JIKO (Jurnal Informatika dan Komputer), vol. 9, no. 1, pp. 226–238, 2025, doi: 10.26798/jiko.v9i1.1523.
E. G. Radjah and A. C. Talakua, “Analisis Sentimen Komentar Terhadap Konten Tenun NTT di Youtube Menggunakan Metode SMOTE dan Logistic Regression,” Jurnal Transformatif, vol. 13, no. 2, pp. 84–94, 2024, doi: https://doi.org/10.58300/transformatif.v13i2.1005.
N. A. Amalia, I. T. Utami, and Y. Wilandari, “Analisis Sentimen Kebijakan Penyelenggara Sistem Elektronik Lingkup Privat Menggunakan Penalized Logistic Regression dan Support Vector Machine,” Jurnal Gaussian, vol. 12, no. 4, pp. 560–569, 2024, doi: 10.14710/j.gauss.12.4.560-569.
D. Prasetia, N. Rahaningsih, R. D. Dana, and C. L. Rohmat, “Analisis Sentimen Pengguna Aplikasi Mybluebird dengan Algoritma Naïve Bayes di Playstore,” JITET (Jurnal Informatika dan Teknik Elektro Terapan), vol. 13, no. 1, pp. 602–607, 2025, doi: http://dx.doi.org/10.23960/jitet.v13i1.5687.
B. A. Putra et al., “Optimisasi Algoritma K-Means dengan Metode Reduksi Dimensi untuk Pengelompokan Big Data dalam Arsitektur Cloud Computing,” Jurnal Software Engineering and Information System (SEIS), vol. 5, no. 1, pp. 1–8, 2025, doi: https://doi.org/10.37859/seis.v5i1.7616.
R. D. R. Apriliansyah, R. AstutI, W. Prihartono, and R. Hamonangan, “Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Pengunjung di Pantai Kejawanan,” JITET (Jurnal Informatika dan Teknik Elektro Terapan), vol. 13, no. 1, pp. 917–926, 2025, doi: http://dx.doi.org/10.23960/jitet.v13i1.5774.
S. N. Salombe, S. Alam, and V. Aris, “Analisis Sentimen Ulasan Netizen pada Aplikasi Disney + Hotstar di Aplikasi Google Play Store Menggunakan Machine Learning,” INNOVATIVE: Journal of Social Science Research, vol. 5, no. 1–9, pp. 5968–5977, 2025, doi: https://doi.org/10.31004/innovative.v5i1.15846.
S. Setyabudi and E. Aryanny, “Sentiment Analysis of Lazada Marketplace User Ratings with Naïve Bayes and Support Vector Machine Methods,” JURNAL INOVTEK POLBENG - SERI INFORMATIKA, vol. 10, no. 1, pp. 422–433, 2025, doi: https://doi.org/10.35314/sww8cg21.
R. R. Rismansyah, A. Sudiarjo, and T. Mufizar, “Analisis Sentimen Ulasan Shopee Pada Google Play Store Menggunakan Algoritma Naive Bayes,” Jurnal Elektro & Informatika Swadharma (JEIS), vol. 5, no. 1, pp. 109–119, 2025, doi: https://doi.org/10.56486/jeis.vol5no1.661.
S. Fathurrohman, I. R. Afandi, and F. N. Hasan, “Sentiment Analysis of TIMNAS Indonesia’s Participation in the Asian Cup U23 2024 on X Using Naive Bayes and SVM,” IJID (International Journal on Informatics for Development), vol. 13, no. 1, pp. 434–447, 2024, doi: 10.14421/ijid.2024.4504.
I. D. Yulisa, Gusmelia Testiana, and Imamulhakim Syahid Putra, “Classifying High School Scholarship Recipients Using the K-Nearest Neighbor Algorithm,” IJID (International Journal on Informatics for Development), vol. 11, no. 2, pp. 232–241, 2023, doi: 10.14421/ijid.2022.3804.
Y. N. Fernando, A. Y. Rahman, and R. P. Putra, “Analisis Sentimen pada Ulasan Konsumen Ayam Goreng Nelongso di Google Maps Menggunakan Metode Bidirectional Long Short-Term Memory (Bi-LSTM),” Jurnal Ilmiah Mahasiswa Sains UNISMA Malang (JIMSUM), vol. 3, no. 1, pp. 65–85, 2025, doi: http://dx.doi.org/10.33474/jimsum.v3i1.26592.
M. Arsadhana, B. Efendi, and M. Trihudiyatmanto, “Analisis Kepuasan Pelanggan Melalui Sentimen Ulasan Menggunakan Algoritma Naive Bayes,” Jurnal Magisma, vol. 13, no. 1, pp. 1–8, 2025, doi: https://doi.org/10.35829/magisma.v13i1.471.
S. Wulandari, M. Trihudiyatmanto, and B. Efendi, “Analysis of MSME Customer Perception Through Sentiment Reviews,” Jurnal Magisma, vol. 13, no. 1, pp. 48–55, 2025, doi: https://doi.org/10.35829/magisma.v13i1.476.
F. Andriansyah and P. Astuti, “Analisis Sentimen Komentar Konten Edukatif di Instagram Dengan Metode Naïve Bayes dan Support Vector Machine,” Jurnal Imtechno, vol. 6, no. 2, pp. 27–31, 2025, doi: https://doi.org/10.31294/imtechno.v6i1.7262.
M. F. A. Shidiq and D. Alita, “Analisis Sentimen Masyarakat Terhadap Kasus Judi Online Menggunakan Data dari Media Sosial X Pendekatan Naive Bayes dan SVM,” Jurnal Sistem Informasi dan Informatika (Simika), vol. 8, no. 1, pp. 24–35, 2025, doi: https://doi.org/10.47080/simika.v8i1.3624.
H. T. Saputra, A. Damanik, M. M. Shaquille, M. A. Rusydi, and M. Habib, “Analisis Modelling pada Reviewes Lazada Indonesia Menggunakan Latent Dirichlet Allocation (LDA) untuk Optimalisasi Strategi Bisnis,” JEKIN: Jurnal Teknik Informatika, vol. 5, no. 1, pp. 361–370, 2025, doi: https://doi.org/10.58794/jekin.v5i1.1325.
W. Hadi, T. D. Putra, and D. Oktafiani, “Analisis Sentimen Terhadap Review Produk Lazada Indonesia Menggunakan Random Forest Classifier,” INNOVATIVE: Journal Of Social Science Research, vol. 5, no. 1, pp. 2348–2357, 2025, doi: https://doi.org/10.31004/innovative.v5i1.17934.
K. Khotimah, Martanto, A. R. Dikananda, and A. Rifa’i, “Analisis Sentimen Ulasan Aplikasi Pintu di Google Play Store Menggunakan Algoritma Naïve Bayes,” JITET (Jurnal Informatika dan Teknik Elektro Terapan), vol. 13, no. 1, pp. 963–969, 2025, doi: http://dx.doi.org/10.23960/jitet.v13i1.5789.
E. D. K. Wardani, F. F. Yo, and W. N. Meylugita, “Implementasi Algoritma Naïve Bayes untuk Analisis Ulasan Pengguna untuk Aplikasi Seabank di Google Play Store,” Jurnal Kecerdasan Buatan dan Teknologi Informasi, vol. 4, no. 1, pp. 13–24, 2025, doi: https://doi.org/10.69916/jkbti.v4i1.193.
D. Fadila and M. Ikhsan, “Analisis Sentimen pada Aplikasi Tokopedia Menggunakan Metode Support Vector Machine,” Progresif: Jurnal Ilmiah Komputer, vol. 21, no. 1, pp. 238–245, 2025, doi: 10.35889/progresif.v21i1.2593.
R. Yusuf, K. Bahumatra, N. Komaria, E. A. Aqma, and L. Cahyani, “Analisis Sentimen Terhadap Aplikasi Google Meet Berdasarkan Komentar Pengguna Menggunakan Metode Logistic Regresion,” Jurnal Ilmiah Edutic: Pendidikan dan Informatika, vol. 11, no. 1, pp. 53–64, 2024, doi: https://doi.org/10.21107/edutic.v11i1.28113.
R. Maheri, F. N. Salisah, and F. Muttakin, “Analisis Sentimen Ulasan Aplikasi M-Paspor Menggunakan Naive Bayes dan Support Vector Machine,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 10, no. 1, pp. 448–458, 2025, doi: https://doi.org/10.29100/jipi.v10i1.5826.
I. R. Afandi, I. F. Hanif, F. N. Hasan, E. S. Sinduningrum, Z. Halim, and N. Nunik, “Analisis Sentimen Opini Masyarakat Terkait Penyelenggaraan Sistem Elektronik Menggunakan Metode Logistic Regression,” Jurnal Linguistik Komputasional, vol. 5, no. 2, p. 77, 2022.
A. R. Hidayati, A. S. Fitrani, and M. A. Rosid, “Analisa Sentimen Pemilu 2019 pada Judul Berita Online Menggunakan Metode Logistic Regression,” Kesatria: Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen), vol. 4, no. 2, pp. 298–305, 2023, doi: https://doi.org/10.30645/kesatria.v4i2.164.
A. N. Sativa, A. Rizky, I. Putri, J. A. Putri, A. Irsyad, and Islamiyah, “Analisis Sentimen Twitter Ibu Kota Negara Nusantara Menggunakan Algoritma Naive Bayes, Logistic Regression dan K-Nearest Neighbors,” Adopsi Teknologi dan Sistem Informasi (ATASI), vol. 3, no. 2, pp. 34–40, 2024, doi: https://doi.org/10.30872/atasi.v3i2.1371.
T. Prasetyo, A. A. Waskita, and T. Taryo, “Analisis Sentimen Pengguna Seputar Kendaraan Listrik di Twitter Dengan Penerapan Algoritma Naïve Bayes, KNN, dan Decision Tree untuk Klasifikasi,” Jurnal Sistem Komputer dan Kecerdasan Buatan, vol. 8, no. 2, pp. 108–116, 2025, doi: https://doi.org/10.47970/siskom-kb.v8i1.783.
A. G. Budianto, Rusilawati, A. T. E. Suryo, G. R. Cahyono, A. F. Zulkarnain, and Martunus, “Perbandingan Performa Algoritma Support Vector Machine (SVM) dan Logistic Regression untuk Analisis Sentimen Pengguna Aplikasi Retail di Android,” Jurnal Sains dan Informatika, vol. 10, no. 2, pp. 1–10, 2024, doi: 10.34128/jsi.v10i2.911.
I. R. Afandi, S. Fathurrohman, and M. W. A. Wibowo, “Sentiment Analysis of Mandatory Halal Certification Policy on Twitter Using the Naive Bayes and K-Nearest Neighbors Algorithm,” Halal Research, vol. 5, no. 1, pp. 37–58, 2025, doi: https://doi.org/10.12962/j22759970.v5i1.1806.
F. M. Hidayat and H. Sanjaya, “Analisis Sentimen Publik Terhadap Penjualan Iphone 16 dan Kebijakan TKDN di Indonesia,” Infotech Journal, vol. 11, no. 1, pp. 73–80, 2025, doi: https://doi.org/10.31949/infotech.v11i1.13159.
Z. B. Toyibah, Y. N. Putri, Puandini, Z. M. Widodo, and A. Tsalitsatun, “Perbandingan Kinerja Algoritma Multinomial Naïve Bayes dan Logistic Regression pada Analisis Sentimen Movie Ratings IMDB,” Jurnal Ilmiah Edutic : Pendidikan dan Informatika, vol. 10, no. 2, pp. 181–189, 2024, doi: https://doi.org/10.21107/edutic.v10i2.28150.
P. W. Hardjita, Nurochman, and R. Hidayat, “Sentiment Analysis of Tweets on Prakerja Card using Convolutional Neural Network and Naive Bayes,” IJID (International Journal on Informatics for Development), vol. 10, no. 2, pp. 82–91, 2022, doi: 10.14421/ijid.2021.3007.
A. Septini, Susanto, and Elmayati, “Analisis Sentimen Masyarakat di Twitter Mengenai Open AI CHATGPT Menggunakan Metode Support Vector Machine (SVM),” Bulletin of Computer Science Research, vol. 5, no. 2, pp. 138–149, 2025, doi: 10.47065/bulletincsr.v5i2.475.
M. Z. Ramadhan and R. Mubarak, “Analisis Sentimen Masyarakat Terhadap Kenaikan Bahan Bakar Minyak pada Media Sosial Youtube dengan Metode K-Nearest Neighbor dan Support Vector Machine,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 1, pp. 984–991, 2025, doi: https://doi.org/10.36040/jati.v9i1.12526.
A. Saninah, W. Prihartono, C. L. Rohmat, and K. Cirebon, “Analisis Sentimen Pengguna Terhadap Aplikasi Duolingo dengan Naïve Bayes Classifier,” JITET (Jurnal Informatika dan Teknik Elektro Terapan), vol. 13, no. 1, pp. 619–627, 2025, doi: http://dx.doi.org/10.23960/jitet.v13i1.5691.
Alvinalia, A. Rahim, and T. A. Y. Siswa, “Analisis Sentimen Aplikasi Mysiloam Menggunakan Metode Naive Bayes,” JITET (Jurnal Informatika dan Teknik Elektro Terapan), vol. 13, no. 1, pp. 1546–1555, 2025, doi: http://dx.doi.org/10.23960/jitet.v13i1.5997.
Y. R. Z. Mustopo and Afiyati, “Analisis Sentimen Proyek Strategis Nasional Food Estate Menggunakan Algoritma Naïve Bayes, Logistic Regression dan Support Vector Machine,” Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), vol. 9, no. 2, pp. 485–494, 2025, doi: https://doi.org/10.35870/jtik.v9i2.3312.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.