Analisis Sentimen Berbasis Aspek Ulasan GoFood di X dengan Ekstraksi Aspek Rule-Based dan Klasifikasi IndoBERT

Nazlah Ayunda Syahputri Jacub; Agustinus Jacobus; Muhamad Dwisnanto Putro

doi:10.30606/rjti.v5i3.4822

Authors

Nazlah Ayunda Syahputri Jacub Universitas Sam Ratulangi
Agustinus Jacobus Universitas Sam Ratulangi
Muhamad Dwisnanto Putro Universitas Sam Ratulangi

DOI:

https://doi.org/10.30606/rjti.v5i3.4822

Keywords:

Analisis Sentimen Berdasarkan Aspek, GoFood, IndoBERT, Rule-Based, Layanan Pesan-Antar Makanan, Platform X.

Abstract

Social media reviews provide insights into user perceptions of food delivery services, but general sentiment analysis does not identify specific service aspects. This study applies Aspect-Based Sentiment Analysis (ABSA) using a hybrid Rule-Based and IndoBERT approach to GoFood reviews on X. Rule-Based methods extract aspects using keywords and structured rules, while IndoBERT classifies sentiment based on textual context. From 28,763 Indonesian-language tweets obtained from Kaggle, 5,657 aspect–sentiment pairs were obtained after preprocessing, aspect extraction, auditing, and manual verification. The dataset covers four aspects: price, service, food quality, and application. Using a StratifiedGroupKFold split of 70:15:15, IndoBERT achieved 90.31% accuracy and a Macro F1-score of 0.8883 on 846 test samples. Food quality achieved the highest aspect-specific accuracy (92.62%), followed by price (90.40%), service (90.00%), and application (87.88%). Price was the most frequently identified aspect, while positive sentiment dominated food quality (60.4%) and neutral sentiment dominated the other three aspects. These findings provide detailed aspect-level insights into user perceptions and can help identify service aspects to maintain or examine further. However, the impact on service quality improvement has not been directly evaluated.

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References

N. S. Shaeeali, A. Mohamed, and S. Mutalib, "Customer reviews analytics on food delivery services in social media: A review," IAES Int. J. Artif. Intell., vol. 9, no. 4, pp. 691–699, 2020, doi: 10.11591/ijai.v9.i4.pp691-699.

A. Fathrizqy, P. Mahardika, and A. Muid, "The Sentiment Analysis of Online Customer Review on Food and Beverages Delivery Services in the GOJEK Application Using K-Nearest Neighbors," pp. 854–862, 2023, doi: 10.46254/ap03.20220167.

P. J. J. S. Gautama, A. Wibowo, and J. J. Siang, "Analisis Sentimen Pengguna Terhadap Akun X/Twitter Resmi ‘DANA’ Dengan Algoritma Indobert," INTECOMS J. Inf. Technol. Comput. Sci., vol. 8, no. 2, pp. 549–559, 2025, doi: 10.31539/intecoms.v8i2.14477.

F. Aftab et al., "A Comprehensive Survey on Sentiment Analysis Techniques," Int. J. Technol., vol. 14, no. 6, pp. 1288–1298, 2023, doi: 10.14716/ijtech.v14i6.6632.

G. Brauwers and F. Frasincar, "A Survey on Aspect-Based Sentiment Classification," ACM Comput. Surv., vol. 55, no. 4, pp. 1–35, 2023, doi: 10.1145/3503044.

R. Chauhan, A. Gusain, P. Kumar, C. Bhatt, and I. Uniyal, "Fine Grained Sentiment Analysis using Machine Learning and Deep Learning," Int. Conf. Sustain. Emerg. Innov. Eng. Technol. ICSEIET 2023, vol. 11, no. 5s, pp. 423–427, 2023, doi: 10.1109/ICSEIET58677.2023.10303481.

Anushree Goud and Bindu Garg, "Advancements in Aspect-Based Sentiment Analysis: Leveraging Deep Learning and Machine Learning Techniques," J. Inf. Syst. Eng. Manag., vol. 10, no. 21s, pp. 451–473, 2025, doi: 10.52783/jisem.v10i21s.3371.

J. Siswanto, S. Suakanto, M. Andriani, M. Hardiyanti, and T. F. Kusumasari, "Interview Bot Development with Natural Language Processing and Machine Learning," Int. J. Technol., vol. 13, no. 2, pp. 274–285, 2022, doi: 10.14716/ijtech.v13i2.5018.

C. Yusni Adhelina, M. Halmi Dar, and M. Nirmala Sari Hasibuan, "Implementation of Deep Learning Models in Conducting Aspect-Based Sentiment Analysis," Int. J. Sci. Technol. Manag., vol. 6, no. 4, pp. 949–957, 2025, doi: 10.46729/ijstm.v6i4.1251.

A. Wafda, D. H. Fudholi, and J. Nugraha, "Aspect-Based Sentiment Analysis on Twitter Tweets About the Merdeka Curriculum Using Indobert," JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 10, no. 3, pp. 586–599, 2025, doi: 10.33480/jitk.v10i3.5692.

M. Rayie, R. Putera, and H. Irawan, "Analisis Sentimen Ulasan Produk Toko Online Rava.Id Untuk Peningkatan Produk Menggunakan Algoritma IndoBERT-RoBERTa," Universitas Telkom, 2025. [Online]. Available: https://repository.telkomuniversity.ac.id/pustaka/239729/

A. Jazuli, Widowati, and R. Kusumaningrum, "Optimizing Aspect-Based Sentiment Analysis Using BERT for Comprehensive Analysis of Indonesian Student Feedback," Appl. Sci., vol. 15, no. 1, pp. 1–28, 2025, doi: 10.3390/app15010172.

V. D. Setiawan, D. U. Iswavigra, and E. Anggiratih, "Implementation of IndoBERT for Sentiment Analysis of the Constitutional Court’s Decision Regarding the Minimum Age of Vice Presidential Candidates," Sci. J. Informatics, vol. 12, no. 3, pp. 397–406, 2025, doi: 10.15294/sji.v12i3.26320.

F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, "IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP," COLING 2020 - 28th Int. Conf. Comput. Linguist. Proc. Conf., pp. 757–770, 2020, doi: 10.18653/v1/2020.coling-main.66.

Anugerah Simanjuntak et al., "Research and Analysis of IndoBERT Hyperparameter Tuning in Fake News Detection," J. Nas. Tek. Elektro dan Teknol. Inf., vol. 13, no. 1, pp. 60–67, 2024, doi: 10.22146/jnteti.v13i1.8532.

R. Talita Trista and E. Tri Asmoro, "Analisis Sentimen dalam Data Big Data," J. Multidiscip. Inq. Sci. Technol. Educ. Res., vol. 2, no. 1, pp. 700–706, 2025.

W. Ahmad, H. U. Khan, F. K. Alarfaj, and M. Alreshoodi, "Aspect-Based Sentiment Analysis: A Comprehensive Review and Open Research Challenges," IEEE Access, vol. 13, pp. 65138–65182, 2025, doi: 10.1109/ACCESS.2025.3555744.

N. F. Adhim and N. Cahyono, "Optimization of IndoBERT for Sentiment Analysis of FOMO on Social Media Through Fine-tuning and Hybrid Labeling," J. Appl. Informatics Comput., vol. 9, no. 6, pp. 3786–3797, 2025.

A. Fatihaturrahmah and K. Ditha Tania, "A Hybrid Approach of Aspect-Based Sentiment Analysis and Knowledge Extraction for Evaluating Security Perceptions in Digital Payment Applications," Sci. J. Informatics, vol. 12, no. 4, pp. 635–650, 2025, doi: 10.15294/sji.v12i4.31557.

C. H. Lin and U. Nuha, "Sentiment analysis of Indonesian datasets based on a hybrid deep-learning strategy," J. Big Data, vol. 10, no. 1, 2023, doi: 10.1186/s40537-023-00782-9.