Implementasi Algoritma Random Forest dalam Klasifikasi Ulasan Pengunjung Mall Semarang untuk Pengambilan Keputusan Layanan
DOI:
https://doi.org/10.29408/edumatic.v9i2.30379Keywords:
google maps, mall, preference, random forests, smoteAbstract
Visitor preferences for malls in Semarang are not optimal because bold reviews have not been utilized optimally in decision making. Our research aims to classify the sentiment of Google Maps reviews from 13 malls in Semarang with a total of 2,600 reviews. Labeling is done manually based on ratings, where ratings 1–3 are considered negative reviews and 4–5 as positive reviews. The classification method used is Random Forest because the ensemble approach (bagging) provides optimal results. The research process includes data collection, labeling, cleaning, data sharing, classification, and model evaluation. The data used is unbalanced and dominated by positive reviews, so the Synthetic Minority Over-sampling Technique (SMOTE) technique was applied. The overall accuracy before and after SMOTE remained the same at 84%. However, the model's performance in detecting negative reviews increased from 27% to 44% in recall and F1-score from 0.40 to 0.52, but these values are still relatively low. Java Supermall Semarang is the mall with the best reviews, with a classification accuracy reaching 90%. This model is better at recognizing positive reviews, but less reliable for negative reviews. Therefore, its use as a decision-making preference needs to be done with caution. This research opens up opportunities for further development, including the use of other models such as BERT which are superior in understanding context and language in reviews.
References
Adib, K., Handayani, M. R., Yuniarti, W. D., & Umam, K. (2024). Opini Publik Pasca-Pemilihan Presiden: Eksplorasi Analisis Sentimen Media Sosial X Menggunakan SVM. SINTECH Journal, 7(2), 80–91. https://doi.org/10.31598/sintechjournal.v7i2.1581
Almansour, N. M. (2022). Triple-negative breast cancer: A Brief Review About Epidemiology, Risk Factors, Signaling Pathways, Treatment and Role of Artificial Intelligence. Frontiers in Molecular Biosciences, 9(1), 1–15. https://doi.org/10.3389/fmolb.2022.836417
Andriansyah, A., Sulastri, E., & Satispi, E. (2021). The Role of Government Policies in Environmental Management. Research Horizon, 1(3), 86–93. https://doi.org/10.54518/rh.1.3.2021.86-93
Apriliyani, M., Musyaffaq, M. I., Nur’Aini, S., Handayani, M. R., & Umam, K. (2024). Implementasi Analisis Sentimen Pada Ulasan Aplikasi Duolingo Di Google Playstore Menggunakan Algoritma Naïve Bayes. AITI: Jurnal Teknologi Informasi, 21(2), 298–311.https://doi.org/10.24246/aiti.v21i2.298-311
Aryanisila, A. (2023). Penyelenggaraan Mall Pelayanan Publik untuk Masyarakat di Kota Palembang. SIMBOL (Jurnal Administrasi Publik dan Pemerintahan), 2(1), 1–11. https://doi.org/10.55850/simbol.v2i1.65
Aufan, M. H., Handayani, M. R., Nurjanna, A. B., Wibowo, N. C. H., & Umam, K. (2024). The Perceptions Of Semarang Five Star Hotel Tourists with Support Vector Machine On Google Reviews. Jurnal Teknik Informatika (JUTIF), 5(5), 1241–1247. https://doi.org/10.52436/1.jutif.2024.5.5.2025
Cahyani, O. N., & Budiman, F. (2025). Performa Logistic Regression dan Naive Bayes dalam Klasifikasi Berita Hoax di Indonesia. Edumatic: Jurnal Pendidikan Informatika, 9(1), 60–68. https://doi.org/10.29408/edumatic.v9i1.28987
Dista, T. M., & Abdulloh, F. F. (2022). Clustering Pengunjung Mall Menggunakan Metode K-Means Dan Particle Swarm Optimization. Jurnal Media Informatika Budidarma, 6(3), 1339–1348. https://doi.org/10.30865/mib.v6i3.4172
Fitri, E., Yuliani, Y., & Gata, W. (2020). Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest, dan Support Vector Machine. TRANSFORMATIKA, 1(1), 71–80.
Furqan, M., Sriani, & Shidqi, M. N. (2023). Chatbot Telegram Menggunakan Natural Language Processing. Walisongo Journal of Information Technology, 5(1), 15-26. https://doi.org/10.21580/wjit.2023.5.1.14793
Kadir, I., Kenanga, D. T., & Kasetty, D. M. (2024). Pengaruh Online Customer Review Dan Online Customer Rating Terhadap Keputusan Pembelian Produk Di Online Marketplace Shopee (Studi Kasus Mahasiswa STIE Petra Bitung). Tangkoko Jurnal Akuntansi dan Manajemen, 10(1), 41–50. https://doi.org/10.33795/jab.v10i1.3601
Khofifah, W., Rahayu, D. N., & Yusuf, A. M. (2022). Analisis Sentimen Menggunakan Naive Bayes Untuk Melihat Review Masyarakat Terhadap Tempat Wisata Pantai Di Kabupaten Karawang Pada Ulasan Google Maps. Jurnal Interkom: Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi, 16(4), 171-180. https://doi.org/10.35969/interkom.v16i4.192
Lu, C., Kuswoyo, C., Abednego, F., & Josephine, S. G. (2021). Pengaruh Faktor Lingkungan Dan Pengalaman Belanja Mall Terhadap Perilaku Belanja Mall. Jurnal Inspirasi Bisnis dan Manajemen, 5(1), 87–100. https://doi.org/10.33603/jibm.v5i1.4937
Mamun, A. A., Islam, M. S., & Hasan, M. K. (2022). A Novel Improved Random Forest For Text Classification Using Feature Selection And TF-IDF. Journal of King Saud University – Computer and Information Sciences, 34(8), 5667–5674. https://doi.org/10.1016/j.jksuci.2022.06.001
Mursyidah, M., Davi, M., & Novitri, S. D. (2024). Klasifikasi Sentimen Review Pengguna terhadap Aplikasi Instagram menggunakan Algoritma Random Forest. Jurnal Infomedia: Teknik Informatika, Multimedia, dan Jaringan, 9(2), 106–115.
Panjaitan, C. H. P., & Supriadi, C. (2023). Analisis Sentimen Pengunjung Wisata Heritage Kota Semarang Menggunakan Naive Bayes Pada Ulasan Google Maps. JETI: Jurnal Elektronika dan Teknologi Informasi, 4(2), 1–28.
Ranika, V. G., Mutrofin, A., & Nathania, E. C. A. (2024). Analisis Preferensi Mall Culture Dalam Gaya Hidup Konsumtif. WISSEN: Jurnal Ilmu Sosial dan Humaniora, 2(3), 89–96. https://doi.org/10.62383/wissen.v2i3.175
Rizki, A. F., Prihartono, W., & Fathurrohman. (2023). Analisis Sentimen Ulasan Google Maps Rumah Sakit Khalishah Di Cirebon Dengan Algoritma Naive Bayes. JITET (Jurnal Informatika dan Teknik Elektro Terapan), 13(2), 728–738. https://doi.org/10.23960/jitet.v13i2.6309
Setiawan, A. D. H., & Maharani, W. (2025). Understanding Public Sentiments on the 2024 Presidential Election through BERT-Powered Analysis. Edumatic: Jurnal Pendidikan Informatika, 9(1), 89-98. https://doi.org/10.29408/edumatic.v9i1.29267
Yahya, F., Ratnawati, D. E., & Rahayudi, B. (2025). Analisis Sentimen Ulasan Pengguna dari Google Maps Menggunakan Metode Long Short-Term Memory (Studi Kasus: Rumah Sakit Gatoel). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 9(4), 1–11.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Annisa Maizaliyanti, Khothibul Umam, Wenty Dwi Yuniarti, Maya Rini Handayani
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Semua tulisan pada jurnal ini adalah tanggung jawab penuh penulis. Edumatic: Jurnal Pendidikan Informatika bisa diakses secara free (gratis) tanpa ada pungutan biaya, sesuai dengan lisensi creative commons yang digunakan.
This work is licensed under a Lisensi a Creative Commons Attribution-ShareAlike 4.0 International License.
