E-Government systems have brought significant changes on the way public services are easily being delivered nowadays. This motivates the researchers to contribute to this innovation by developing a system named “BALANGAY” which makes barangay services more accessible to the constituents. This is anchored with the objectives of the study which is mainly to build a centralized database for barangays and design modules with the purpose of streamlining barangay processes. Part of it is to generate an incident heatmap that will assist barangay officials in creating better programs and project developments. The study is a combination of qualitative and quantitative type of research which is reflected on the use of interview and survey questionnaires. An iterative waterfall methodology was also adopted as a guide in the development process. Meanwhile, the ISO 9126 Software Quality Model was used in the system evaluation wherein each response is measured using the 5-point Likert scale and respondents were selected using purposive sampling. The result shows that out of sixteen (16) barangay constituents and sixteen (16) IT experts who participated, most of them were very satisfied with the system with a weighted mean of 4.45 and a verbal interpretation of “Very Satisfied”.

While several content management systems (CMS) and audience analytics tools are available for digital signage in the market, they are often sold separately and can be expensive. Therefore, this project aims to design a cost-effective and unified web-based solution for digital signage that combines content management and audience analytics functions, reducing the need for multiple purchases. This can be achieved by utilizing Raspberry Pi technology, known for its cost-effectiveness and versatility in integrations, along with a face recognition camera and machine learning methods. This proof of concept demonstrates the integration of these components to create a dynamic digital signage system. Overall, this project has the potential to offer an affordable solution for companies aiming to efficiently manage and optimize their digital marketing strategies, especially in the Digital Out-of-Home (DOOH) Advertising space.

HWYL (meaning “a stirring feeling of emotional motivation and energy”) is a 3D isometric puzzle adventure game for Android devices. The whole game revolves around the adventures of Thomas as he unknowingly helps the mayor and the townspeople in solving environmental problems through playing meaningful mini games. The mini games comprise of different casual games that focus on teaching the players about the major environmental issues such as ozone depletion, and disposal of wastes. The interactive learning experience educates players how to mitigate environmental problems. The game garnered very satisfactory results from the play testers, proving that the game has been successful in promoting environmental awareness through edutainment, and the game as a system works as intended with compliance to software quality factors.

The college entrance examination is vital for program admission. Typically, entrance examinations are conducted onsite using paper and pens. When the COVID-19 pandemic hit, the entrance examination was lifted and physical gatherings were prohibited. Since many schools cannot offer an online admissions exam, they rely on grades and interviews to admit and qualify students for degree programs. However, academic standards differ between schools, and grades may not be enough to assess students' capacity. Thus, this study aims to develop an Online Proctored Entrance Examination System (OPEES) with Degree Program Recommender for colleges and universities to help institutions administer onsite or online entrance tests and generate course suggestions using a rulebased algorithm. The study employed the scrum methodology in software development. OPEES allows applicants to submit applications online, and institutions can manage user accounts, tailor exams and degree programs’ criteria, manage exam dates, and assign proctors. Online proctoring using Jitsi, an opensource multiplatform voice, video, and instant messaging tool with end-to-end encryption, ensures exam integrity. The system’s features were evaluated by 102 respondents, comprised of end-users (students and school personnel) and IT professionals, using the FURPS (Functionality, Usability, Reliability, Performance, and Supportability) software quality model. In the software evaluation, the overall system proved to be functional as perceived by the respondents, as manifested by the mean rating of 4.61. In conclusion, the system's architecture was deemed feasible and offers a better way to streamline admission examinations and determine a student’s applicable degree program by enabling institutions to customize their exams and degree program requirements. It will be beneficial to look into recommendation system algorithms and historical enrollment data to improve the system’s use case.

According to National Center for Education Statistics, almost half of the first-time freshmen full time students who began seeking a bachelor’s degree do not graduate. The imbalance between the student enrolment and student graduation can be solved by early predicting and identifying students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions. The study focused on the application of the ensemble models in predicting student graduation. Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications. The study recorded an increase of classification accuracy in predicting student graduation using ensemble models and combining multiple algorithms.

The study aims to integrate neural network algorithm that predicts students' vulnerability of not having graduation on time to an adaptive learning management system. Neural network is one of the popular machine learning techniques because of its learning algorithm. The learning algorithm is focused on updating weights of the edges in order to produce minimal mean squared error between actual and predicted values. The integration of this platform could lead to much efficient learning management system as LMS is mainly driven to provide individualized and personalized learning tailored to specific requirements and learning preferences. The neural network algorithm is designed to classify students with learning difficulty so that administrators can formulate remediation and academic support policies.

Medicine intake, as prescribed by physicians and health care providers, is important not only for minimizing the risk of relapse but also to treating conditions and improving one’s overall well-being. However, adherence to a medication routine seems to be a problem for some people which is usually affected by a variety of factors such as hectic day-to-day activity schedules, poor prescription instruction, concurrent intake of multiple medications, and forgetfulness. Medication adherence has been then considered as one of the major medical problems globally. In such cases, a medical device that could alert and remind patients in taking their medicines on time will come in handy. Consequently, this study aimed to design and develop a pocket-sized electronic pillbox device using TFT LCD display, Arduino microcontroller, Piezo Buzzer (for sound notification), Eccentric Rotating Mass (for vibration notification), Lithium Ion battery as power source, and plastic organizer as the main body. The said pillbox device will act as a countermeasure for medication non-adherence particularly by patients under the case of polypharmacy. Thus, this study focused on the design and development of the prototype, hardware testing and system qualification only. Furthermore, this paper is part of a future study where the assessment and measure of device behavior and adherence will be conducted to compare whether the utilization of pillbox device has an impact to the people who are using it.

In the Philippines, Light Rail Transit (LRT) 1 is one of the most used public mass rapid transports by Filipino commuters in going to their respective destinations in Metro Manila. However, conditions of the trains have been deteriorating over the past years resulting to insufficient numbers of trains to meet the commuter demands during peak hours causing irate passengers, delays in train arrival and uncomfortably crowded stations and trains. Currently, LRT1 implements Passenger Limit Per Platform (PLPP) to regulate load capacity at the station platforms, prevent overloading of trains and congestion at the paid area. But the said scheme is being done manually which is tedious to staff and is prone to error. Thus, this study presents the integration of embedded system and different software applications to manage the crowd of all LRT1 stations platforms and trains intelligently. A Simulation software was developed to populate data to different stations that are relevant during operations in the absence of the station prototype. Integration and acceptance tests showed that all components of the system are functioning accurately according to the predetermined design specifications. The developed system proves to be functionally acceptable in terms of suitability and accuracy, and highly functional in terms of security. Thus, the overall system is functionally acceptable as perceived by the respondents as manifested by the mean rating of 3.28.

This study is concerned with the development of an information system for disaster risk reduction and management in the Philippines. It covered relational and multi-dimensional database designs as well as software applications for disaster preparedness and response combined with Decision Support System. The application highlights the following administrative modules namely community registration with fingerprint biometrics and camera integration, emergency evacuation, search and rescue operation, cash and in-kind donation, evacuation center and disaster event profiling, weather forecast, and private messaging. Moreover, the decision support system highlights the live data consolidation of disaster affected areas and individuals through data visualization and geographic information system. It presents historical information of previous disasters in a multi-dimensional viewpoint from national level to barangay or district level. Finally, the system can dynamically generate predicted list of potential evacuees via Logistic Regression Analysis. The system's response time test revealed a highly acceptable result with latency ranging from 31ms to 419ms. The software quality evaluation in terms of functionality, usability, efficiency, and maintainability proved to be excellent and is highly commendable by the ICT department and operations group of the National Disaster Risk Reduction and Management Council, Office of Civil Defense.