000			02970nam a22003257a 4500
003			OSt
005			20250710135603.0
008			250709b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH QA 76.9 _bI54 2025
100			_aInfante, Charlin M. _eauthor
245			_aSmart restroom: _bpreventive maintenance system using machine learning algorithm/ _cCharlin M. Infante, Ryan Rey D, Magdalita, Joey Boy E. Mission, John Michael P. Pontanos, and Rafaela E. Santos.--
260			_aManila: _c2025. _bTechnological University of the Philippines,
300			_axii, 168pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege of Industrial Technology.-- _bBachelor of engineering technology major in computer engineering technology: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aTraditional restroom maintenance practices are predominantly reactive, leading to inefficiencies, increased operational costs, and user dissatisfaction due to unexpected equipment failures. This thesis aimed to develop the "Smart Restroom," a preventive maintenance system leveraging IoT sensors and machine learning (ML) algorithms to transition from reactive to proactive facility management. The system captures critical parameters predictive of maintenance needs by integrating sensors to monitor real-time data such as usage frequency, environmental conditions, and consumable supply levels. Machine learning models, employing anomaly detection and time series forecasting, analyze this data to anticipate equipment malfunctions and optimize maintenance schedules. The proposed framework not only predicts imminent failures but also prioritizes tasks based on urgency, enhancing resource allocation. Experimental results demonstrate the system’s efficacy in reducing downtime, lowering maintenance costs, and improving hygiene standards through timely interventions. Furthermore, this study highlighted the system’s scalability and adaptability, positioning it as a viable component of smart city infrastructures aimed at sustainable urban management. The innovation lies in its data- driven approach, which transcends conventional threshold-based alerts, offering dynamic insights tailored to specific restroom usage patterns. This thesis underscored the transformative potential of IoT and ML in public infrastructure, advocating for their broader adoption to enhance operational efficiency and user experience in communal spaces.
650			_aSmart restroom
650			_aPreventive maintenance
650			_aSupervised machine
700			_aMagdalita, Ryan Rey D. _eauthor
700			_aMission, Joey Boy E. _eauthor
700			_aPontanos, John Michael P. _eauthor
700			_aSantos, Rafaela E. _eauthor
942			_2lcc _cBTH CIT _n0
999			_c30231 _d30231