000			02897nam a22003137a 4500
003			OSt
005			20240819144238.0
008			240819b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH QA 76 _bS84 2022
100			_aSuerte, Jake Andrey F. _eauthor
245			_aKUBO: _ba mobile-based food planner and recipe app with object detection/ _cJake Andrey F. Suerte, Lovelyn C. Tapales, Jerico C. Villariza, Marcial B. Zipagan jr. --
260			_aManila: _bTechnological University of the Philippines, _c2022.
300			_axii, 164pages: _c29cm. _e+1 CD-ROM (3/4in.)
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aThesis (undergraduate)
502			_aCollege of Science-- _bBachelor of Science in Computer Science: _cTechnological University of the Philippines, _d2022.
504			_aIncludes bibliography.
520			_aThe study, Kubo: A Mobile-Based Food Planner and Recipe App with Object Detection, is a mobile application developed to lessen the time spent by individuals on menu planning and scheduling by assisting both experienced and aspiring cooks to access a variety of cuisines, manage and observe proper mealtime, and help to keep track of foods that they eat. This study specifically aims to design a Food Planner and Recipe App with the following features: (a) AI model to detect ingredients belonging to the classes of Ampalaya, Carrot, Kamatis, Kangkong, Okra, Repolyo, Sayote, Sitaw, Talong, and Upo using user’s mobile camera; (b) Generate Menu schedule according to user’s time availability and the scanned available ingredients; (c) Custom menu schedule according to user’s preference; (d) Menu History for reference; and (e)Alarm Schedule based on Menu Schedule. The mobile application is developed using Flutter Framework with Dart Programming Language for its frontend, Visual Studio Code as an IDE, GCP for server hosting, Colab with TensorFlow 2.0 for AI Training, ExpressJS Framework, and NodeJS for the backend, and MongoDB and HIVE for the database. Before deployment, the developed mobile application undergone a series of Functionality and Portability Testing wherein results indicate that the designed features of the app effectively and efficiently perform its tasks and responsibilities. Also, the performance of the developed mobile application is evaluated in conformance to ISO 25010 where the system garnered an overall mean rating of 3.68 with a descriptive equivalence of “Highly Acceptable” indicating that the system successfully passes all the criteria given by ISO 25010.
650			_aArtificial intelligence
650			_aVegetables
650			_aRecipe generator
700			_aTapales, Lovelyn C. _eauthor
700			_aVillaraza, Jerico C. _eauthor
700			_aZipagan jr., Marcial B. _eauthor
942			_2lcc _cBTH COS _n0
999			_c28887 _d28887