000			02894nam a22002417a 4500
003			ft8777
005			20251111105200.0
008			251111b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aT57.7 A27 2025
082			_a.
100	1		_a Abrera Jr., Joselito Joshua S.; Caballes, Gab P.; Santiago, Denmark O.
245			_aLubriscan: A deep learning approach for revealing counterfeit motorcycle oil/lubricants via primary packaging analysis
264		1	_a. _b. _cc2025
300			_bCapstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025
336			_2 text _a text _b text
337			_2unmediated _aunmediated _bunmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT: The practice of selling counterfeit motorcycle lubricants have long invaded the Philippine market most notably on e-commerce platforms such as Shopee and Lazada where products cannot be seen and manually verified. Consumers are also buying these products without ever knowing they are counterfeit in the first place due to limited knowledge. To combat this, LubriScan is a mobile application developed to accurately detect and classify the authenticity of motorcycle lubricants through their primary packaging, aiming to enhance user accessibility and awareness towards counterfeit products. The mobile application utilizes the latest YOLOv8 multi-label classification algorithm to identify key features on the bottle of the lubricant and enhance user’s awareness on the lubricants they purchase. The model was trained by purchasing genuine lubricants from authorized resellers while counterfeit lubricants were obtained from Shopee by inspiring the number of 1-star reviews provided by consumers after acquiring these products. 310 images of counterfeit lubricants and 249 images of genuine ones, with a total of 559 images were used as dataset for the model. The model was validated using 112 images from the dataset and achieved a Mean Average Precision (mAP) of 91%, demonstrating high precision and recall. Additionally, the model achieved an accuracy of 88.4% and an F1 score of 90.44% with few instances of false positives and false negatives due to misclassification. Motorcycle riders and motor shop staff and owners evaluated LubriScan using the ISO/IEC 25010 software quality model under the Functional Suitability, Performance Efficiency, Usability, and Reliability. The system received an overall mean score of 4.48 as “Satisfied”, indicating the respondent’s positive reception of the app’s capabilities. LubriScan effectively combines accessibility and accurate detection, making it a valuable tool for both consumers and retailers in spotting counterfeit motorcycle lubricants as well as enhancing awareness towards motorcycle lubricants.
526			_aF
655			_aacademic writing
942			_2lcc _cMS
999			_c37064 _d37064