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Undergraduate Theses

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14578/5

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    Household utilities monitoring system using geofence technology with data analytics
    Suizo, Clyde Andrie G. (Davao del Sur State College, 2024-06)
    This study was conducted to determine that combining geofence technology into household monitoring systems was an approach to improving utility management and monitoring more efficiently and effectively. While the previously mentioned changes had already been integrated into systems used to monitor household utilities, it was clear that there was a lack of monitoring systems that utilized geofence technology. Such methods were more useful among locals in Don Lorenzo Homes Subdivision, Tres de Mayo, Digos City. As a solution, the monitoring system created in this study achieved the following functions; (1) used a motion sensor as the geofence to cover the entrances and exits of the household with an effective range of 250cm and an effective angle between 60 and 120 degrees, (2) used light and gas sensors integrated into household utilities to gather real-time information with an effective range of 60cm and 20cm respectively, (3) visualized data with the help of bar and line graphs using graphical analytics techniques to display the collected data. Through pilot testing and evaluation, the system gained a 4.2 weighted mean with a remark of GOOD, which demonstrated that employing geofencing techniques increases the efficiency of the homeowner's household utility management. The capstone project stood to make significant contributions to the research field by combining geofence technology and sensors to enhance household utility management. The anticipated benefits in terms of energy efficiency, cost savings, and household utilities management highlighted the project's potential to influence both academic research and practical applications in the smart home domain. The research outcomes could serve as a foundation for further studies, leading to the development of more sophisticated systems and inspiring new approaches to energy efficiency, sustainability, and management.
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    Spring onion disease detection and treatment recommendation
    Talaid, Nikko R. (Davao del Sur State College, 2024-06)
    Spring onion is a delicate crop that demands much attention during its cultivation; diseases such as the purple blotch and leaf blight affect spring onion crops and, in any case, prevention of these diseases is rather complicated to detect. Study aims to diagnose the diseases correctly and make suitable recommendations on the treatment needed. The researcher created an app that help in identifying the spring onion disease and would offer recommendations on how to treat such disease. In developing the app, the researcher used Android studio and Google Colab for datasets training. The technique used in choosing the survey participants is simple purposive random sampling and self-constructed checklist based on the ISO 9124 Likert scale to rate the app's functionality, reliability, and usability. The app is ideal for small and big farmlands, especially in regions without an internet connection, and during an experimental test, it gained an accuracy of 90% in the purple blotch and 93% in leaf blight-captured crop diseases within a 3-inch range. The significant results of this study include the application's ability to detect two types of diseases, namely purple blotch and leaf blight, and its ability to provide personalized treatments, such as recommendations, chemical treatments, and care tips, based on the specific disease detected. The app's contribution to the farming community is its ability to detect crop diseases early, simplify disease detection techniques, increase harvests, decrease chemical use, and prevent minor spring onion problems that could result in major outbreaks and damage large farmlands.
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    Android-based classroom attendance monitoring system using QR technology with SMS notification
    Genobisa, Kenneth N. (Davao del Sur State College, 2024)
    This paper introduces an innovative Android-based classroom attendance monitoring system aimed at overcoming the limitations of traditional manual tracking methods within educational environments. By harnessing SMS notifications and QR codes, the system seeks to enhance both accuracy and efficiency. The system utilizes individual QR codes assigned to students to streamline attendance recording, eliminating the need for manual data entry and reducing errors. eliminating the need for manual data entry and reducing errors. Furthermore, automatic SMS notifications promptly update parents on their student's attendance status, thereby promoting transparency and communication between home and school. The study outlines the developmental objectives of various modules, including storing attendance data, scanning QR codes, managing user accounts, and generating reports. It emphasizes functionality, usability, and reliability in system design. The research's significance lies in its potential to enhance the effectiveness and transparency of attendance monitoring, benefiting school administrators, students, parents, and future researchers alike. Deployed initially at Alfredo Eugenio Sr. Elementary School, the system addresses challenges such as mobile network stability and device reliability, thereby contributing to advancements in educational technology and attendance management
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    Android-based postharvest grading classification of cavendish banana using deep learning and internet of thing
    Caminade, Lilynn S. (Davao del Sur State College, 2024-06)
    This study is combined deep learning and the Internet of things, which used of microcontroller and android devices. An application and weight machine, may be risks of mistakes and consistent of grade classification result of image processing and difficulties in obtaining the weight value of the Cavendish banana. The Rapid Application Development is the help to established an application with weight measurements to capture images and classify the grade classification of the banana. The pretrained model using the teachable machine. The datasets gathered and captured the banana based on the postharvest grading classification: Class A, Class B, Cluster and Totally Reject. The android app is capable of captured image, classify the grading classification of the Cavendish banana with weight detected with the help of weight scale machine do perform and also the captured images and classify of the postharvest grading classification of the banana with weight measurement of the banana and displayed the information on the application. This study contributes to the modern-farming technology in the industries of agriculture and manufacturing to helps workers fasten the performance and productions and along with adapting new technology into agri-technology and also to the economy be more imports and exports globally and this study helps the agri-farming and plantation industries and in importing and exporting quality products globally.