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Veri Shandy, Sony
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Environmental Science

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Journal : The Scientific Journal of Information Systems

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OPTIMIZATION OF GYM CHECK IN AND MEMBERSHIP INFORMATION SYSTEM QR CODE SCANNER BASED Badriah, Nurul; Veri Shandy, Sony
Scientific Journal of Information System Vol. 2 No. 2 (2024): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v2i2.131

Abstract

The development of information technology provides opportunities to improve operational efficiency in various fields, including membership management at gyms. A QR code scanner-based information system is an effective solution for managing gym member check in. Users can check in simply by scanning a QR code that is linked to their membership data. This system not only facilitates the attendance process but also helps managers monitor the number of member check in, remaining attendance quota, and attendance time. This researcaims to design and implement a QR code scanner-based check in system at the gym, as well as analyze the impact of the system on operational efficiency.
Implementation of Regression CART Decision Tree for Best Cycling Time Recommendation Based on Weather Data Badriah, Nurul; Muttaqi, Fajar; Veri Shandy, Sony; Alfaujianto, Moh
Scientific Journal of Information System Vol. 3 No. 2 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i2.233

Abstract

Cycling requires careful time planning to ensure safety and comfort, especially when consideringweather conditions such as temperature, wind speed, and overall weather status. However, cyclistsoften struggle to determine the optimal time to ride due to the lack of accurate and easily accessiblerecommendations. This study aims to design and implement a mobile application that recommendsthe best cycling time based on real-time weather data. The system applies the Regression CARTDecision Tree method, trained using hourly temperature, wind speed, and weather conditionparameters. Unlike classification approaches, Regression CART Decision Tree produces acontinuous percentage score indicating the suitability level of each hour for cycling. Real-time datais obtained via the OpenWeatherMap API to maintain accuracy. The developed prototype displayshourly weather data along with the recommendation percentage, helping users plan their rides moreeffectively. Model evaluation shows that the Regression CART Decision Tree achieved high accuracywith a low Mean Absolute Error (MAE) and strong correlation between predicted and actualsuitability scores. The results confirm that the model performs consistently in various weatherscenarios. Overall, the system successfully delivers reliable, data-driven recommendations, assistingcyclists in selecting the safest and most comfortable cycling times.
Co-Authors Alfaujianto, Moh Badriah, Nurul Muttaqi, Fajar