Alfan Afiyudin (1), Rr. Rochmoeljati (2)
General Background: The rapid advancement of digital technology has catalyzed the proliferation of online service platforms, intensifying competition among providers to deliver optimal user experiences. Specific Background: In this landscape, food delivery services such as GoFood Merchant play a crucial role, yet user dissatisfaction remains a persistent challenge. Knowledge Gap: Despite the abundance of user-generated reviews, comprehensive sentiment and pattern analysis for GoFood Merchant remains limited, particularly in the integration of sentiment classification with pattern discovery and actionable recommendations. Aims: This study aims to analyze user sentiment toward the GoFood Merchant application using the Naïve Bayes algorithm and identify common complaint patterns via the Apriori algorithm, followed by solution formulation through the 5W+1H approach. Results: Utilizing 1,243 Play Store reviews, the sentiment classification model achieved an accuracy of 87%, indicating robust performance. Further analysis of negative reviews revealed five dominant keywords: “driver,” “order,” “aplikasi,” “resto,” and “iklan.” Novelty: The integration of sentiment analysis, association rule mining, and structured problem-solving provides a novel framework for understanding and addressing user dissatisfaction. Implications: The findings offer strategic insights for enhancing user experience and strengthening GoFood Merchant’s competitive advantage in the saturated online service marketplace.
Highlights:
Identifies dominant user complaints using data mining techniques.
Combines sentiment classification with complaint pattern discovery.
Provides actionable recommendations using the 5W+1H framework.
Keywords: Sentiment Analysis, Naïve Bayes, Apriori Algorithm, User Complaints, GoFood Merchant
F. Tamimi and S. Munawaroh, “Teknologi Sebagai Kegiatan Manusia dalam Era Modern Kehidupan Masyarakat,” Saturnus: Jurnal Teknologi dan Sistem Informasi, vol. 2, no. 3, pp. 66–74, 2024.
Badan Pusat Statistik, Statistik Penyediaan Makanan dan Minuman 2023, Jakarta, Indonesia: BPS, 2024. [Online]. Available: https://www.bps.go.id/id/publication/2024/12/23/f2c7743c4712aaeaa4abf694/statistik-penyediaan-makanan-dan-minuman-2023.html [Accessed: Mar. 18, 2025].
R. A. Arrofi, A. Ajie, and T. Sutabri, “Penggunaan Transformasi Digital Bisnis untuk Para Pelaku UMKM Kuliner,” Jurnal Riset Manajemen dan Ekonomi, vol. 2, no. 1, pp. 180–189, 2024.
D. Febrianti, I. N. Rahayu, and M. Anjani, “Peningkatan Produktivitas Pemasaran UMKM Kuliner dari Konvensional Menuju Era Digital,” Indonesian Journal of Digital Business, vol. 2, no. 2, pp. 23–30, 2022.
F. R. Gustian and A. Gunawan, “Perubahan Dinamika Bisnis Kuliner di Era Marketplace: Studi Kasus Adaptasi terhadap Digitalisasi,” Jurnal Ilmu Sosial Ekonomi dan Politik, vol. 3, no. 1, pp. 246–253, 2025.
C. Candy and J. Joycelin, “Integrasi Sistem Manajemen Stok Berbasis Digital pada Restoran Acia Ikan Bakar,” Jurnal Aksi Sosial Pemberdayaan, vol. 1, no. 4, pp. 41–54, 2024.
S. M. Salsabila, A. A. Murtopo, and N. Fadhilah, “Analisis Sentimen Pelanggan Tokopedia Menggunakan Metode Naïve Bayes Classifier,” Jurnal Minfo Polgan, vol. 11, no. 2, pp. 30–35, 2022.
GoBiz, Solusi Terbaik untuk Kelola Bisnis Anda. [Online]. Available: https://gobiz.co.id/ [Accessed: Mar. 18, 2025].
F. Khoirunnisa and H. Februariyanti, “Analisis Sentimen Kualitas Layanan Google Meet Menggunakan Naïve Bayes Classifiers dan Association,” SemanTIK, vol. 8, no. 1, pp. 35–44, 2022.
H. Aulawi, W. A. Kurniawan, and F. A. Rachman, “Analisis Sentimen Kepuasan Driver terhadap Kebijakan Baru Sistem Order Gojek,” Jurnal Sains dan Teknologi, vol. 14, no. 1, pp. 86–94, 2020.
G. Testiana and D. Erlina, “Analisis Sentimen pada Twitter terhadap UIN Raden Fatah Menggunakan Support Vector Machine,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 9, no. 1, pp. 698–708, 2022.
D. Septiani and I. Isabela, “Analisis Term Frequency Inverse Document Frequency (TF-IDF) dalam Temu Kembali Informasi pada Dokumen Teks,” Jurnal Sistem dan Teknologi Informasi Indonesia, vol. 1, no. 2, pp. 81–88, 2022.
Derisma and F. Febrian, “Perbandingan Teknik Klasifikasi Neural Network, Support Vector Machine, dan Naive Bayes dalam Mendeteksi Kanker Payudara,” Bina Insani ICT Journal, vol. 7, no. 1, pp. 53–62, 2020.
R. Nurhidayat and K. E. Dewi, “Penerapan Algoritma K-Nearest Neighbor dan Fitur Ekstraksi N-Gram dalam Analisis Sentimen Berbasis Aspek,” Jurnal Ilmiah Komputer dan Informatika, vol. 12, no. 1, pp. 45–52, 2023.
J. A. Wibowo, V. C. Mawardi, and T. Sutrisno, “Visualisasi Word Cloud Hasil Analisis Sentimen Berbasis Fitur Layanan Aplikasi Gojek dengan Support Vector Machine,” Jurnal Serina Sains, Teknik dan Kedokteran, vol. 2, no. 1, pp. 61–70, 2024.
A. Saputra, H. L. Sari, and D. Sartika, “Implementasi Metode Association Rule Mining pada Penjualan Barang di Toko Bangunan Ada Mas Menggunakan Algoritma Apriori,” Jurnal Multidisiplin Dehasen, vol. 2, no. 4, pp. 709–718, 2023.
R. Sari, R. D. Cahyaningrum, and A. S. Putri, “Penerapan Metode 5W+1H dalam Analisis Kebutuhan Sistem Informasi,” Jurnal Teknologi dan Informasi, vol. 9, no. 2, pp. 112–125, 2021.