000			02960nam a2200301 i 4500
003			CSPC
005			20260817162335.0
008			260817s2025 ph a b 000 0 eng d
040			_aCSPC _beng _cCSPC _erda
090			_aUTH BPA P896r 2025
100	1		_aPrades, Angela May L., _eauthor.
245	1	0	_aRecalibrating the proxy means test model of the pantawid pamilyang pilipino program (4Ps) using ai-powered lean six sigma methodology / _cAngela May L. Prades, Jessica D. Relos, and Jaselle Ann R. Lanuzo.
300			_a133 leaves.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
502			_aUndergraduate Theses (BPA) - Public Administration. _cCamarines Sur Polytechnic Colleges, 2025
504			_aIncludes bibliographic references.
520	3		_aThe Pantawid Pamilyang Pilipino Program (4Ps) has traditionally used a Proxy Means Test, integrating with machine learning-based classification for its selection process that focuses mostly on proxy indicators for income to be eligible for membership. However, relying only on income-based proxy indicators has often led to misclassification. The main issue is that the current model does not consider non-income proxy indicators of poverty since some truly poor families are left out while others who do not qualify are included. These include not just economic conditions, but also education, health, geographic challenges, social disadvantages, and psychological well-being. This study aimed to improve the eligibility model by using the DMAIC framework to build a more accurate and data-driven classification system. The data consisted of interview transcripts in Bikol-Rinconada and Tagalog, which provided detailed scenarios about the daily lives of the vulnerable households. Thematic analysis was used to identify common patterns, and Latent Dirichlet Allocation was applied to extract probabilistic themes from the text. Rule-based scenarios were then developed to reflect the realities of the vulnerable households. These scenarios were utilized to train and test a Naive Bayes classifier. The model, called EquiSelect Classifier, was evaluated using a confusion matrix and achieved an F1 score of 82.35 percent. To further strengthen the approach, future plans include integrating real-time data, expanding the dataset to cover more regions, xvii and applying cross-validation techniques. The study demonstrates how transforming interview transcripts into probabilistic classification may improve targeting accuracy in conditional cash transfer programs.
700	1		_aRelos, Jessica D., _eauthor.
700	1		_aLanuzo, Jaselle Ann R., _eauthor.
720			_aMagistrado, Jeffrey B., _eadviser.
720			_aBarcinilla, Ejay F., _echair, panelist.
720			_aEclar, Eugene N., _epanelist.
720			_aArevalo, Al Lexus P., _epanelist.
942			_2ddc _n0 _cUM _hP896r _iBPA _kUTH _m2025
999			_c32962 _d32962