Analysis of Study Space Comfort Levels in Coffee Shops Using Decision Tree and Random Forest Algorithms
DOI:
https://doi.org/10.33005/jasid.v2i2.83Keywords:
Decission Tree, Random Forest, Coffee Shop, Comfort LevelsAbstract
Coffee shops are no longer just places to enjoy drinks, they have also become workspaces and study spaces for university students. This study examines this shift through Exploratory Data Analysis (EDA) of questionnaire data from 150 valid respondents, covering 22 variables such as visit frequency and duration, spending, number of devices, seating area, preferred time, noise disturbance, Wi-Fi and power outlet problems, and the percentage of tasks completed. The data were cleaned and checked for missing values and outliers using the IQR method. The results show that most respondents feel comfortable, with scores of 4 and 5 being the most common, particularly among students from Computer Science and Engineering and Science, in the late afternoon, and in workspace areas. Comfort is positively related to productivity, with the percentage of tasks completed rising from 52.0% for Uncomfortable respondents to 64.9% for Comfortable respondents and 74.1% for Very Comfortable respondents. However, Wi-Fi and power outlet problems remain common, experienced occasionally by 41.3% and often by 33.3% of respondents. Decision Tree and Random Forest models reached 50.0% and 56.7% accuracy respectively in predicting comfort, so they are not yet reliable without further tuning. Overall, the findings confirm that coffee shops now serve as student workspaces, and facility quality is a factor owners should address.
References
Liu, M., “In the Post-pandemic and Internet 4.0 era, Cafes' New Operation Mode of Compound Space”, Frontiers in Business, Economics and Management, 7(1), 24, 2022.
W. Guo, L. Wang, and L. Caneparo, “Research on the factors that influence and improve the quality of informal learning spaces (ILS) in university campus,” Buildings, vol. 14, no. 11, Art. no. 3458, Nov. 2024, doi: 10.3390/buildings14113458.
E. R. Layton and M. Love, “A happy medium: Academic library noise from the perspective of students and librarians,” Codex: J. Louisiana Chapter ACRL, vol. 6, no. 1, pp. 32–53, 2021.
[2] D. J. Wibisana, W. Sunarya, and D. K. Santoso, “Servicescape design analysis: The influence of ambient conditions and spatial layout on seating preferences at Bento Café, Surabaya,” Jurnal Arsitektur ZONASI, vol. 9, no. 3, pp. 549–564, Oct. 2026, doi: 10.17509/jaz.v9i3.97251.
Y. N. Lukito and A. P. Xenia, “Café as third place and the creation of a unique space of interaction in UI Campus,” IOP Conf. Ser.: Earth Environ. Sci., vol. 126, no. 1, Art. no. 012208, 2018, doi: 10.1088/1755-1315/126/1/012208.
Kurnianto, G. W., Aggacitto, & Ardillah, R. T., “Study Time at the Coffee Shop: Exploring Student Motives through a Phenomenological Study”, Indonesian Psychological Research, 7(1), 43–54, 2025.
Rompas, A. M., Kusuma, H. E., & Wuisang, C. E. V. (2025). Learning Flow in Café: The Role of Spatial Characteristics. DIMENSI: Journal of Architecture and Built Environment, 52(2), 110–120.
Wicaksono, S. A., Kadek, I., & Nuryana, D., “Perbandingan Klasifikasi Hasil Indeks Kepuasan Masyarakat Menggunakan Algoritma Decision Tree, Random Forest, dan K-Nearest Neighbor”, Emerging Statistics and Data Science Journal, 3(1), 447, 2024.
Putra, R. S., Simbolon, H. F. S., Linhar P, A., & Izhari, F., “Perbandingan Algoritma Decision Tree dan Random Forest dalam Klasifikasi Kepuasan Pengguna Sistem Informasi Akademik”, TECHSI - Jurnal Teknik Informatika, 16(2), 2025.
Tiftazani, A., Tenriawaru, A., & Rahman, G. A., “Analisis Klasifikasi Kepuasan Mahasiswa terhadap Penyelenggaraan Pelayanan Akademik Menggunakan Algoritma Random Forest”, AnoaTIK: Jurnal Teknologi Informasi dan Komputer, 2(2), 77–83, 2024.
