| 000 -LEADER |
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| fixed length control field |
02380nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
ft8851 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251205150843.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
251205b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
T58.5 B39 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Bayot, Joaquin Alastair F.; Pahoyo, Gene Daniela L.; Segunto, Christy C. |
| 245 ## - TITLE STATEMENT |
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| Title |
Recommender system using decision tree and neural network for disaster risk reduction management services |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Other physical details |
Capstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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| Source |
text |
| Content type term |
text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
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| Source |
unmediated |
| Media type term |
unmediated |
| Media type code |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
volume |
| Carrier type term |
volume |
| Carrier type code |
volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: At the occurrence of natural disasters alongside other environmental crises, the Disaster Risk Reduction Management Services (DRRMS) of the Department of Education Central Office upholds the initiative of responding to these events within schools of their responsibility. In collaboration with the DRRMS, the affected institutions most generate a Rapid Assessment of Damage Report (RADaR) to relay a detailed account to the central office regarding the overall condition of each school and its population. However, the DRRMS with remarkable years of efficiency may still experience difficulty in analyzing RADaR files. A particular problem raised in this study is which academic institutions bear the most damage and must be prioritized for response/recovery plans. On top of that, what specific measures must be implemented to ensure the school’s recovery from the disaster? Hence, this study aims to utilize data mining through a Recommender System empowered by Decision Tree, Neural Networks, and Content-Based Filtering. The developed product accepts a standardized RADaR file and from these will determine the severity via Decision Tree, provide a detailed assessment through Neural Network, and generate recommendations using the Content-Based Filtering. It also optimizes as SMS notification feature in which the DRRMS can easily relay recommendations to the affected schools. To sum it all up, this Recommender System cases data processing amidst extensive loads of RADaR files and has proven to further improve accuracy, reliability, and productivity. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
|
| Item type |
Thesis/Dissertation |