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Abstract

This study focuses on designing and developing a web-based data collection application at PT Hidup Selalu Sejahtera utilizing fuzzy algorithms. The research method involves analyzing sales data to identify consumer behavior patterns and enhance marketing strategies. The application is developed using the Software Development Life Cycle to ensure efficiency and effectiveness. Findings show that the application improves decision-making accuracy based on sales data and offers deeper insights into consumer preferences. Furthermore, it enhances data management, leading to increased productivity within the organization. However, the study acknowledges limitations related to the volume of data analyzed, which may impact the generalizability of the findings. Ultimately, the conclusion highlights the significance of adopting information technology to boost operational efficiency and refine marketing strategies within the company

Bahasa Abstract

Penelitian ini bertujuan untuk merancang dan mengembangkan aplikasi pengumpulan data berbasis web di PT Hidup Selalu Sejahtera dengan memanfaatkan algoritma fuzzy. Metode yang digunakan meliputi analisis data penjualan untuk memahami pola perilaku konsumen serta mengoptimalkan strategi pemasaran. Dalam proses pengembangannya, aplikasi ini mengikuti siklus hidup pengembangan perangkat lunak untuk memastikan sistem yang efisien dan efektif. Hasil penelitian menunjukkan bahwa aplikasi yang dibuat dapat meningkatkan akurasi pengambilan keputusan berdasarkan data penjualan dan memberikan wawasan lebih dalam mengenai preferensi konsumen. Selain itu, aplikasi ini juga mendukung pengelolaan data yang lebih baik, sehingga meningkatkan produktivitas perusahaan. Namun, penelitian ini memiliki batasan terkait jumlah data yang dianalisis, yang dapat mempengaruhi generalisasi hasil. Kesimpulan penelitian menekankan pentingnya penerapan teknologi informasi untuk meningkatkan efisiensi operasional dan strategi pemasaran di perusahaan

References

Adegoke, M., et al. (2022). Application of multilayer extreme learning machine for efficient building energy prediction. Energies, 15(24), 9512. https://doi.org/10.3390/en15249512

Alaoui, M., et al. (2023). Prediction of energy consumption of an administrative building using machine learning and statistical methods. Civil Engineering Journal, 9, 1007–1022. https://doi.org/10.28991/cej-2023-09-05-1007

Barbaresi, A., et al. (2022). Application of machine learning models for fast and accurate predictions of building energy need. Energies, 15(4), 1266. https://doi.org/10.3390/en15041266

Bourhnane, S., et al. (2020). Machine learning for energy consumption prediction and scheduling in smart buildings. SN Applied Sciences, 2(2), 297. https://doi.org/10.1007/s42452-020-2314-6

Cao, Y., et al. (2023). PSO-stacking improved ensemble model for campus building energy consumption forecasting based on priority feature selection. Journal of Building Engineering, 72, 106589. https://doi.org/10.1016/j.jobe.2023.106589

Chen, Y., et al. (2023). Machine learning approach to predict building thermal load considering feature variable dimensions: An office building case study. Buildings, 13(2), 312. https://doi.org/10.3390/buildings130200312

Fallah, A. M., et al. (2023). Novel neural network optimized by electrostatic discharge algorithm for modification of buildings energy performance. Sustainability, 15(4), 2884. https://doi.org/10.3390/su15042884

Fellah, M., et al. (2025). Harnessing machine learning for enhanced thermal insulation and energy efficiency in buildings worldwide. Results in Engineering, 25. https://doi.org/10.1016/j.rineng.2025.25.0001

Figueiredo Filho, D. B., Silva Júnior, J. A., & Rocha, E. C. (2011). What is R² all about? Leviathan, 3, 60–68.

Ganaie, M. A., et al. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151. https://doi.org/10.1016/j.engappai.2022.105151

Golafshani, E., et al. (2024). An artificial intelligence framework for predicting operational energy consumption in office buildings. Energy and Buildings, 317, 114409. https://doi.org/10.1016/j.enbuild.2024.114409

Guo, H., et al. (2022). Machine learning-based method for detached energy-saving residential form generation. Buildings, 12(10), 1504. https://doi.org/10.3390/buildings12101504

Hashempour, N., Taherkhani, R., & Mahdikhani, M. (2020). Energy performance optimization of existing buildings: A literature review. Sustainable Cities and Society, 54, 101967. https://doi.org/10.1016/j.scs.2020.101967

Joseph, V. R. (2022). Optimal ratio for data splitting. Statistical Analysis and Data Mining: The ASA Data Science Journal, 15(4), 531–538. https://doi.org/10.1002/sam.1001

Khan, A. M., et al. (2024). BIM integration with XAI using LIME and MOO for automated green building energy performance analysis. Energies, 17(13), 3295. https://doi.org/10.3390/en17133295

Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669–679. https://doi.org/10.1016/j.ijforecast.2015.08.001

Markarian, E., et al. (2025). Informing building retrofits at low computational costs: A multi-objective optimisation using machine learning surrogates of building performance simulation models. Journal of Building Performance Simulation, 1–17. https://doi.org/10.1080/19401493.2025.1234567

Mahamedi, E., et al. (2024). A reinforcing transfer learning approach to predict buildings energy performance. Construction Innovation, 24(1), 242–255. https://doi.org/10.1108/CI-09-2023-0152

Mehraban, M. H., Alnaser, A. A., & Sepasgozar, S. M. E. (2024). Building information modeling and AI algorithms for optimizing energy performance in hot climates: A comparative study of Riyadh and Dubai. Buildings, 14(9), 2748. https://doi.org/10.3390/buildings14092748

Mohan, R., & Pachauri, N. (2025). An ensemble model for the energy consumption prediction of residential buildings. Energy, 314, 134255. https://doi.org/10.1016/j.energy.2025.134255

MOOCS Expert. (n.d.). Web-based data collection applications: An overview. Retrieved July 27, 2025, from https://www.moocsexpert.com

Ngnamsie Njimbouom, S., et al. (2023). Predicting site energy usage intensity using machine learning models. Sensors, 23(1), 82. https://doi.org/10.3390/s23010082

Penna, P., et al. (2015). Multi-objectives optimization of energy efficiency measures in existing buildings. Energy and Buildings, 95, 57–69. https://doi.org/10.1016/j.enbuild.2015.01.015

Prafitasiwi, A. G., Rohman, M. A., & Ongkowijoyo, C. S. (2022). The occupant’s awareness to achieve energy efficiency in campus building. Results in Engineering, 14. https://doi.org/10.1016/j.rineng.2022.14.0001

Rastbod, S., et al. (2023). An optimized machine learning approach for forecasting thermal energy demand of buildings. Sustainability, 15(1), 231. https://doi.org/10.3390/su150100231

Salami, B. A., et al. (2023). Building energy loads prediction using Bayesian-based metaheuristic optimized explainable tree-based model. Case Studies in Construction Materials, 19, e02676. https://doi.org/10.1016/j.cscm.2023.e02676

Yao, J., et al. (2022). Applications of stacking/blending ensemble learning approaches for evaluating flash flood susceptibility. International Journal of Applied Earth Observation and Geoinformation, 112, 102932. https://doi.org/10.1016/j.jag.2022.102932

Zhang, L., et al. (2021). A review of machine learning in building load prediction. Applied Energy, 285, 116452. https://doi.org/10.1016/j.apenergy.2021.116452

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