LAKASABrowsing by Author "Malicdem, Alvin R."

Browsing by Author "Malicdem, Alvin R."

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Crashlytics:
(Don Mariano Marcos Memorial State University – Mid La Union Campus, 2024-12) Madayag, Maveric B.; Asejo, Reendhel John P.; Bucsit, James V.; Gameng, Klarence Jhay G.; Mique, Jr., Eusebio L.; Sapuay-Guillen, Sheena I.; Ledda, Mark Kristian C.; Malicdem, Alvin R.
This study aimed to analyze accident-prone areas in San Fernando City, La Union, Philippines, using a web-based system developed with Association Rule Mining. It provides valuable insights to inform interventions and policies to reduce vehicular accidents in the region. The research follows the CRISP-DM methodology, which includes six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. System development was based on the Rapid Application Development (RAD) model, which enabled iterative prototyping with user feedback. The system integrated the FP-Growth algorithm for Association Rule Mining to identify accident patterns and was coupled with an interactive map for enhanced visualization and decision-making. Usability testing revealed an average System Usability Scale (SUS) score of 87.14, indicating "Best Imaginable" usability and high user satisfaction.
Deep residual U-Net based lung image segmentation for lung disease detection
(IOP Conference Series: Materials Science and Engineering, 2020) Mique, Eusebio L., Jr.; Malicdem, Alvin R.
The World Health Organization (WHO) estimated that by the year 2030, lung disorders such as Chronic Obstructive Pulmonary Disease (COPD) would be one of the leading cause of death all over the world. Consequently, accurate and timely detection of lung diseases may prevent further death. It is therefore vital that the early detection may lead to treatment and prevention of mortality among patients. However, there are only a minimum number of experts or well-trained radiologists reading Chest X-Ray (CXR) that delays the timely diagnosis of lung diseases. In order to aid the radiologist in reading CXR images, a computer-aided tool is proposed. Before the processing of images, it needs to be segmented to make it easier for the machine to understand. This study is focused on developing a model that will segment the lung from CXR images. Using Residual U-Net (ResUnet) architecture based semantic segmentation, the researchers were able to develop and train a model using a set of 562 CXR images and lung mask images, 70% of the images were used as training data and 30% as test data. The model was trained with 40 epochs and a batch size of 16. Dice coefficient was used to assess the similarity of the segmented result and the ground truth mask. The developed model has achieved a Dice coefficient of 0.9860. The developed model can then be used in classifying lung diseases by focusing on the segmented image rather than focusing on the entire CXR image.
DEH-DoSv6:
(Bulletin of Electrical Engineering and Informatics, 2021-02) Naagas, Marlon A.; Malicdem, Alvin R.; Palaoag, Thelma D.
With the rapid depletion of IPv4 protocol in these recent years, the IETF introduced IPv6 as a solution to address the exhaustion, however, as a new protocol exists, new characteristics have been introduced and new threats have been discovered. Extension Headers are the new characteristics of IPv6 that have an emerging and re-emerging security threats that is needed to be taken into consideration during the full migration to the IPv6 network. This study revealed that up to this moment, the popular vendors are still vulnerable and doesn’t have any default protection to deal with extension headers’ Denial of Service Attack (DoS). Also, this study leads to the development of new security model which creates a new solution to address the emerging threats of IPv6 extension headers’ Denial of Service Attack. Moreover, the results of this study show that our proposed security model is more effective in terms of neutralizing the unwanted traffic causing evasion attack by filtering, rate-limiting and discarding the malformed packets of prohibited extension headers’ payload versus the traditional router protection.
Design architecture of a student co-curricular activity management platform
(International Journal of Recent Technology and Engineering (IJRTE), 2019-09) Malicdem, Alvin R.; Perilla, Fernandino S.
This paper introduces a design architecture of a co-curricular activity management platform for students. Becauseof the absence of features in existing learning management platforms to manage co-curricular activities that educational institutions have to offer, these learning management platforms are not fitted to be utilized. One of the essential circumstances for raising qualified and prepared students in this day and age is to guarantee their interest and participation in social activities. In the education aspect, social activities are co-curricular activities earlier known as extracurricular activities, which are components of non academic curriculum that helps to create different facets of character improvement of students. Quantitative research particularly descriptive research using survey questionnaires and guided interviews, Intensive literature review on published studies and articles, and latest information technology reviews were conducted to come up with an appropriate design architecture to improve student’s engagement in co-curricular activities. Integrating the ideas and insights gathered on the conducted study, a new design architecture for a co-curricular activity management platform was proposed. In order to evolve architecture continually, the new design provided greater flexibility for development teams that decreases development cycle times by allowing them to update modules of the platform independently without affecting the other parts, and also the design responded to the needs of the users, and integrated emerging ICT trends. The tailor fitted design architecture of the platform addressed the specific needs of its end-users, thus providing students more convenient experience and opportunities to engage in co-curricular activities provided by higher educational institutions.
Health data visualization for the province of La Union
(Don Mariano Marcos Memorial State University - Mid La Union Campus, 2024-12) Estalilla, Jemyrdave P.; Meneses, Alexandra Arianne Y.; Malicdem, Alvin R.; Estira, Marydel C.; Pimentel, Emmalou B.
This study focused on developing a health data visualization system for the province of La Union to transform raw health records into interactive visual formats, including heatmaps and graphs. By concentrating on the top five diseases, the system enabled stakeholders to identify trends, demographic impacts, and geographic hotspots. The research utilized descriptive and applied methodologies to determine key performance indicators, design user-friendly interfaces, and evaluate the system's usability through the System Usability Scale (SUS), which yielded an "Excellent" rating. Through Extreme Programming, the system addressed critical gaps in health data interpretation by providing accessible, actionable insights for public health officials, healthcare providers, and the community. The visualization system fostered better decision-making, enhanced health awareness, and supported targeted interventions.
iBuild:
(Don Mariano Marcos Memorial State University - Mid La Union Campus, 2025-06) Orofino, Gleanne Yvan Khyle Y.; Lorenzana, Rose Ann B.; Sobredo, John Deo D.; Malicdem, Alvin R.; Licayan, Arnold C.; Sardeng, Shekainah Kim A.; Ambueguia, Kristoffer John H.
Ibuild is an educational augmented reality (AR) mobile gaming application designed to assist students in learning PC desktop assembly through interactive simulation. The study aimed to determine the hardware and software requirements, develop a mobile app and determine the usability of the mobile app. Using Unity, the app was developed with 3D models and AR overlays to guide students through step-by-step assembly. Through the use of Augmented Reality, the application overlays virtual components onto the real-world environment. In the context of enhancing learning through play, this research examined the role of educational computer games and their effectiveness. To better understand how design impacts game visualization, this study emphasized the importance of augmented reality-based design, 3D objects, and innovative approaches to computer assembly in educational settings.
Maritescanner:
(Don Mariano Marcos Memorial State University – Mid La Union Campus, 2024-12) Rimando, Chrystal Mhaye G.; Cruz, Michael Ryan S.; Flores, Elmer E., Jr.; Mendoza, Hazel F.; Malicdem, Alvin R.; Patacsil, Joseph A.; Nisperos, Zhella Anne V.
The rise of disinformation made it harder to trust what isseen online, especially in the news. To address this, MariteScanner was developed as a web-based tool designed to help users determine whether an online news article is credible. The system used technologies like the Facebook Graph API and advanced algorithms to cross-check informationwith trusted sources, analyzedmetadata, and identified patternsthatsuggested whether the content was reliable or misleading. The CRISP-DM framework guided the processfrom data collection and cleaning to system testing. The final product was an easyto-use platform that provided clear results, such as credibility scores, classifications, and supporting evidence. During testing, the System Usability Scale (SUS) was used, and the tool earned a score of 82.29, indicating it was user-friendly and met expectations.
MekaniGo:
(Don Mariano Marcos Memorial State University - Mid La Union Campus, 2024-01) Paras, Mary Joy D.; Galang, Janfranz L.; Lopez, Rhea Mae D.; Molleda, Rhey Art R.; Malicdem, Alvin R.; Hortizuela, Manny R.; Catungal, John Ernest R.
MekaniGo, a Mobile Based Application Mechanics and Auto Repair Shop Finder aimed at helping drivers locate reliable repair shops while supporting mechanics in managing high customer volumes. The study applied a comprehensive approach, using the Value Proposition Canvas for strategic alignment, Business Model Canvas for roadmap development, and Validation Board to confirm assumptions. Extreme Programming was employed throughout the application's development stages, from Planning to Determination. The MekaniGo mobile app demonstrated very high acceptability across dimensions like functionality, performance, and security. The evaluation, based on ISO/IEC 25010:2011, emphasized customer alignment, strategic planning, and adherence to quality standards for successful development. The application not only proved effective and efficient but also consistently met end-user expectations, reflecting its user-friendly experience and overall quality.
Network security policies and procedure based on the threat-driven approach for Ilocos Sur Polytechnic State College
(Don Mariano Marcos Memorial State University – Mid La Union Campus, 2021-04) Tizon, Joshua M.; Patacsil, Joseph A.; Malicdem, Alvin R.; Cabading, Jose Mari N.; Ledda, Mark Kristian C.
Avoiding the numerous consequences that may result from a cyber-attack, the colleges and universities should take affirmative steps to strengthen their networks and defend their data. This paper primarily aimed to help strengthen the privacy, security, and safety of information of Ilocos Sur Polytechnic State College by designing a Network Security Policy and Procedure for ISPSC using a threat-driven approach. The descriptive research design to analyze the data gathered from the testing of the security and accuracy of the design policy of a network was used. The findings show that the crafted policy and standard operational procedure for network security of the said institution shall be approved by the Board of Trustees of the institution with the integration of the additional policies provided by the researcher. The designed policy and standard operational procedures for the network security of the institution has been successfully evaluated by the technical experts and can be utilized to reinforce the compliance of ISO 27001 and ISO 9001:2015 Certification. The network security has proven to be very much good in the three aspects of validation as to functionality, efficiency, and benefits as felt need of institution.
Online college admission management system of DMMMSU
(Don Mariano Marcos Memorial State University – Mid La Union Campus, 2013-03) Abad, Enrique G.; Ledda, Mark Kristian C.; Malicdem, Alvin R.; Badon, Ralph Vincent H.; Hufana, Gilbert R.; Songcuan, Jerome P.
The focus of the study was to develop an Online College Admission Management System (OCAMS) of DMMMSU. It specifically determined the capabilities and constraints of the existing CAMS of DMMMSU along PIECES framework; developed OCAMS; and determined the usability of OCAMS. The study was processed through descriptive and applied methods. The population included 3 personnel in-charge, 34 staffs, 256 freshmen students, 27 high school students and 5 IT experts. Data were collected through questionnaire and analysed using 5-point Likert scale. The following findings were yielded: the existing CAMS of DMMMSU is a constraint; OCAMS was developed using AWE model; and OCAMS was rated usable. The following conclusions are forwarded: the constraints are indicative of deficiencies which ought to be addressed in order to provide an effective and efficient CAMS; OCAMS is achievable with its development through AWE model; and OCAMS is a usable tool to support the needs of the users.
Online corpus of spoken Ilokano language
(IOP Conference Series: Materials Science and Engineering, 2019) Apostol, Franklin R.; Malicdem, Alvin R.
There has been a great effort in the collection of different languages in the past years all over the world, and the development of online corpus outside the country brought new possibilities in the Philippines. However, there is a limited resource for the Ilokano Language. This paper introduces the Corpus of Spoken Ilokano Language, an online repository of spoken Ilokano in the Philippines specifically in region 1. The main component of this study is spoken Ilokano. It has been specifically built for natural language processing. It shows the difference of Ilokano language as spoken by Ilokanos in the region. The database consists of 160 speakers, 40 speakers in each province of the region, each speaking about 74 statements. Spoken Ilokano language was audio recorded and transcribed. A web application has been developed making the dataset available online. The corpus was validated to provide a useful resource of data that can be used for automatic speech recognition models.
Predictive analytics for rice production and yields through machine learning
(Don Mariano Marcos Memorial State University – Mid La Union Campus, 2024-12) Ramos, Mark Angelo V.; Estacio, Jowill Dave B.; Indong, Raven Icy C.; Partible, Anacel C.; Estira, Marydel C.; Sardeng, Shekainah Kim, A.; Malicdem, Alvin R.
This study aimed to predict rice production and yields in the Province of La Union using a dataset collected from Department of Agriculture Regional Office I. The research employed the CRISP-DM methodology, utilizing machine learning algorithms, focusing on the Random Forest model due to itshigh accuracy and robustness. The dataset included key variablessuch as year, land area, types of seeds, water source, seasons, and municipalities, which were analyzed and processed to train the model. The structured approach of CRISP-DM ensured a comprehensive analysis through its phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Preliminary results demonstrated that the Random Forest model achieved excellent accuracy in predicting rice yields, providing valuable insights for agricultural stakeholders. These insights assisted in strategic agricultural planning, resource allocation, and sustainable farming practices, emphasizing the potential of machine learning in enhancing food security.
SearchScam:
(Don Mariano Marcos Memorial State University - Mid La Union Campus, 2024-12) Daz, Blanca Camille N.; Carig, Hannah Grace T.; Culaton, Jomar S.; Urbi, Rey F.; Malicdem, Alvin R.; Bangug, Cristy M.; Estira, Marydel C.; Sapuay-Guillen, Sheena I.
The growing prevalence of online scams makesthisstudy explored the development of a sentiment-analysis-based system designed to classify online gambling platforms as either legitimate or fraudulent. By employing advanced tools such as the Google Places API. 1VHOIS API. and OpenAI’s natural language processing models, the system effectively analyzed user feedback. metadata, and social media trends to detect scam indicators. Following the CRISP-DM framework, rigorous methodologies for data collection, preprocessing, and analysis were applied, resulting in a system that provided clear and interpretable credibility scores. Validated through the System Usability Scale (SUS) with a score of 71.07, the research highlighted the pivotal role of sentiment analysisin processing unstructured data and enhancing cybersecurity efforts. This work contributed to combating online fraud by offering a practical tool that bridges technological innovation with user safety, fostering trust and security in the digital age.
UNAScan:
(Don Mariano Marcos Memorial State University - Mid La Union Campus, 2025-11) Bernal, Yhoebe Rae C.; Acocos, Kyla Q.; Garcia, Jasmin M.; Kimayong, Princess Joy D.; Estira, Marydel C.; Mique Jr., Eusebio L.; Malicdem, Alvin R.; Pulmano, Dominador G.
This study was conducted to support sugarcane farmers in detecting and managing leaf diseases that affect crop quality and yield, especially for Basi production. The capstone project aimed to build a dataset of sugarcane leaf images and train an optimized deep learning model using the MobileNetV3 architecture. The modeling and development process followed the CRISP-DM framework, ensuring structured stages from data understanding to model evaluation. The trained model was then integrated into both web and mobile platforms through the Agile Methodology. A descriptive and developmental research design was applied with twenty (20) respondents: five (5) agricultural staff, five (5) Basi producers, and ten (10) sugarcane farmersfrom Naguilian, La Union. The system achieved a 98% accuracy rate and a System Usability Scale (SUS)score of 85.6, indicating excellent usability. Respondents described the system as efficient, user-friendly, and highly valuable for early detection and effective management of common sugarcane leaf diseases.
Using big data analysis to retain customers for tele comindustry
(ACM Digital Library, 2019-04-19) Gu, Yuanhu; Malicdem, Alvin R.; Dela Cruz, Josephine S.; Palaoag, Thelma Domingo
Nowadays, telecommunication markets are becoming more and more competitive, and customer churn is becoming more and more serious. In the tough competitive mobile market, Customer Churn Management is becoming more and more critical. In developing countries, most customers switch service providers because of good promotional incentives and lower monthly costs offered by competitive service providers. How to predict customer churn quickly and accurately becomes very important. In this paper, the researchers successfully analyzed the customer churn using big data feature analysis and multi-feature analysis. User data were modeled by XGBoost algorithm. The model is optimized repeatedly with GridSearchCV as a parameter tool. The accuracy of the model on the test set is 85.1%. The researchers predicted about 11000 customer lists per month that may be about to churn. Using K-means clustering method, 11000 churn target customers per month were classified into three categories and telecom companies are suggested to take some solutions which are found by feature analysis to retain customers. This big data analysis can be used to retain customers for the telecom industry.

Browsing by Author "Malicdem, Alvin R."

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