Rancang Bangun Sistem Reservasi Wisata Alam Berbasis Website dengan Integrasi Algoritma Neural Networks dan Validasi QR-Code
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1318Keywords:
Reservation System; Neural Networks; Scan QR-Code; Sentiment Analysis; Visitor PredictionAbstract
The management of tourist destinations in the current digital era faces complex challenges related to operational efficiency and visitor management optimization. Often, tourist destinations experience serious obstacles in predicting accurate visitor volumes, which impacts the unreadiness of infrastructure as well as inefficiencies in the time-consuming manual ticket validation process. This study aims to design and implement a comprehensive website-based reservation system for Syaakirah The View by integrating the Neural Networks algorithm as a smart solution for forecasting future visitor numbers. In developing the system, the methodology used is the Waterfall model. This structured approach is chosen to ensure that each development stage ranging from requirements analysis, system design, implementation, to testing runs systematically and is well-documented. To ensure the reliability and validity of the prediction results, the Neural Networks model is trained using the 10-fold cross-validation technique on historical tourist visit datasets for the 2025–2026 period. This dataset has previously gone through a series of data preprocessing stages, namely data cleaning and normalization, to address noise or inconsistent data, allowing the model to learn visitor behavior patterns more accurately. The results of the study show a significant contribution to the operational activities of the tourist destination. First, the system successfully implements an online reservation feature integrated with QR-Code validation; this mechanism is proven to accelerate ticket verification time by up to 70% compared to previous conventional or manual methods. Second, the constructed Neural Networks model achieves a very satisfactory prediction accuracy level, with a Mean Absolute Percentage Error (MAPE) value of 3.41% or equivalent to an accuracy level of 96.59% on the test data. This achievement empirically proves that the model is capable of forecasting visitor numbers with a very low error rate without experiencing overfitting, thereby providing a more data-driven and precise decision-making foundation for the management of Syaakirah The View.
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