000			02830nam a22002417a 4500
003			ft8912
005			20251218155915.0
008			251218b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aQA76.9 A43 C37 2025
082			_a.
100	1		_a Carpio, Bea Mikaela N.; Francisco, Ricky Jr. O.; Gurimbao, Lance Angelo G.
245			_aFurther enhancement of planned random algorithm applied in music shuffling
264		1	_a. _b. _cc2025
300			_bUndergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan hg Lungsod ng Maynila, 2025
336			_2text _atext _btext
337			_2unmediated _aunmediated _bunmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT: The Enhanced Planned Random Algorithm often encounters difficulties with data adaptability, precise similarity detection, and ensuring the even distribution of items. These challenges arise from its dependence on static attributes, exact attribute matching requirements, and an inherent inability to consistently space out similar items, which is particularly critical in applications demanding variety and a nuanced understanding of item characteristics. This research focused on overcoming these fundamental limitations by developing significant enhancements for the Enhanced Planned Random Algorithm by Jumaquin and Licudo (2024). The improved methodology incorporates dynamic attribute processing using a variance threshold method, enabling the automatic identification and prioritization of the most relevant item features, thereby moving beyond predefined attribute sets. To address similarity detection, K-Prototype clustering, complemented by Gower’s Distance, is integrated to facilitate a more sophisticated evaluation of item similarity across mixed numerical and categorical data types, allowing for the recognition of partial attribute matches. Furthermore, to ensure a more consistent and equitable distribution of similar items, the Martin Fiedler Algorithm is applied post-shuffling, promoting better arrangement and mitigating undesirable clumping. Comparative testing against the baseline FEPRA demonstrated marked improvements: the enhanced system dynamically selected a greater number of attributes (11-14 compared to FEPRA’s fixed five), achieved a significant increase in average consecutive Gower’s distance (from 3.78% to 7.00%), indicating superior item differentiation, and resulted in a substantially more uniform distribution of similar items, with reduction in the Standard Deviation of Gaps by as much as 86% to 91%. These synergistic advancements culminate in a more robust, adaptive, and perceptually satisfying item sequencing system, enhancing balance and variety for an improved listener experience.
526			_aF
655			_aacademic writing
942			_2lcc _cMS
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