https://hdl.handle.net/20.500.12852/16 Disciplines include Computer Science, Digital Media and Interactive Arts, Information Technology, and Library and Information Science 2026-11-14T15:46:51Z https://hdl.handle.net/20.500.12852/3991 Online voting system using Rivest Shamir Adleman encryption algorithm Canatoy, Roda Katrina E. The Online Voting System Using Rivest-Shamir-Adleman Encryption Algorithm is a system designed to manage elections in a college and to allow voters to cast their votes online using their Android mobile devices. The traditional voting process faces challenges such as invalidating the bona fide voters, time-consuming results generation, and low voter turnout. The system allows the system administrators to create elections, define positions, add candidates, add departments and degree programs, and manage voter registrations. Voters can log in using their Android mobile devices by entering their unique credentials and a one-time password sent to their registered mobile numbers or email addresses. After successful verification, users can securely cast their votes and can view election results when the election period is done. Votes are encrypted using the RSA encryption algorithm for secure and tamper-proof voting. The real-time tallying and displaying of the partial result functionalities improve the transparency and speed of the election process. After the election period, the generation of results is faster and more reliable. Since the system can be accessed online, it can help increase the voter turnout. The system helps increase efficiency, improve accessibility, save costs, provide convenient administration, minimize human error, provide faster election results, and quickly generate reports. Abstract only 2025-05-01T00:00:00Z https://hdl.handle.net/20.500.12852/3676 Faculty attendance performance monitoring and decision support system using multiple linear regression algorithm Eregia, Rodolfo C., Jr. The Faculty Attendance Performance Monitoring and Decision Support System Using Multiple Linear Regression Algorithm is a system designed to organize and support decisions on University scheduling of courses through analysis of faculty performance through multiple linear regression algorithm. The system allows profiling of faculty, buildings, courses, and schedules through input or through import though a batch file. Faculty Attendance and Monitoring enhanced though auto filtering and sorting based on the need of the attendance checker, streamlining only important schedules at the current time and place. Using Multiple Linear Regression Algorithm, the system then analyzes whether a faculty is best fit for a certain schedule or not. The analysis is can be used as a guide by the schedule coordinator on assignment of schedules during the beginning of the semester. The methodology used is Evolutionary Prototyping Model, a variant of a prototyping methodology where the prototype is not discarded rather used as an initial resource for developing the next prototype based on the customer’s feedback after testing the prototype. The method has six (6) key phases: Planning, Analysis, Design, Prototype Implementation, Customer Feedback, Final Testing and Implementation and Deployment. The processes from the Analysis Phase to the Customer Feedback Phase comprise the Prototype Building, the core of the system development. The Faculty Attendance Performance Monitoring and Decision Support System Using Multiple Linear Regression Algorithm provides the university the support in making the optimal decisions in the matter of scheduling and monitoring of faculty performance. Abstract only 2024-05-01T00:00:00Z https://hdl.handle.net/20.500.12852/2472 Tuning of speed-governing system for Kalayaan Pumped Storage Power Plant Calibjo, Cirilo C. Changes in system frequency are caused by any imbalance between load and generation. To control these, a speed governor is usually provided. The change in frequency may be either stable or unstable depending on the values of the parameters of the plant and governor. This paper describes a simple analytical procedure of tuning electro-hydraulic P.I.D. governors for the units of Kalayaan Pumped Storage Power Plant. Mathematical analysis is carried out using MATLAB simulation software, while validation through time simulation is performed using Power System Simulator/ Engineering (PSS/ E). Optimum gain settings were found both during isolated and grid operations. Although actual plant testing was not possible, the result of the study is seen as an improvement from the usual trial-and-error approach which is currently being practiced. Journal article 1996-07-01T00:00:00Z https://hdl.handle.net/20.500.12852/2421 Image segmentation and analysis of concrete cracks: A building safety evaluation system Jardeleza, Donie S. One of the factors that determine a city to be highly urbanized is through its strong foundation of structural safety implementations. It demands for a well-established skills of structural specialists in maintaining the quality and reliability of these man-made structures. This study aims to understand and interpret information taken from captured images of concrete cracks. Specifically, it investigates collected images of concrete structure deformities by extracting pixel related details. In this relation, images contain useful features that convey every shades of color value intensities it displays. Known as the “Queen City of the South”, Iloilo City is popular for its Spanish colonial churches and old houses. Regular monitoring of buildings are often undertaken using traditional procedures such as visual inspection and manual recording, making it tedious in the long run. The need to develop a building safety evaluation system to aid in the systematic approach of performing the routine assessment of any structure. The methodology applied through Iterative Modeling was the segmentation of captured concrete crack images and categorized accordingly through clustering using Partitioning Around Medoids (PAM). By using a public dataset containing 20,000 observations to serve as training sets, the pixels extracted were classified by a formulated crack severity category (CSC) which were specified as cutoffs. Percentage values were calculated and those were below a certain light intensity threshold in a pixel were constituted to be part of a crack. Results have shown that k-medoids algorithm was able to minimize the distance between points labeled within a cluster as well as the distances between each of the clusters. These evidence in the results have dramatically decreased the false positives and misclassification of the images. Having known all the related features in the identification of concrete cracks can support building evaluators and regulatory organizations in maintaining and preserving the quality of building construction at the same time, it will encourage the conceptualization of new insights and recommend improvements for better decision making endeavors. Abstract only 2022-07-01T00:00:00Z