UTAR Institutional Repository (Universiti Tunku Abdul Rahman)
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Personal finance management and budget application
Get PDFWith the growing complexity of modern financial systems, it has become essential for individuals to manage their personal finances wisely to stay informed and in control of their financial activities. While many finance-related mobile apps exist, most lack the comprehensive features needed for complete and flexible money management. This project, titled "Personal Finance Management and Budget Application," addresses this gap by offering a complete and user-friendly mobile platform for personal finance management. The application enables users to manage a variety of financial tasks, including adding income, expenses, and transfers, handling multiple accounts, setting budgets, and categorizing transactions into customizable categories and subcategories for both income and expense types. The app supports full CRUD (Create, Read, Update, Delete) operations for transactions, accounts, categories, subcategories, and budgets. To help users stay within their budgets, the app incorporates a built-in notification system that alerts users when their spending in any category reaches the predefined limit. Financial reports, presented via pie charts, bar charts, and line graphs, provide users with a visual representation of their spending habits and financial progress. Additionally, users can easily back up or transfer their data through JSON file imports and exports. A standout feature of the app is its ability to suggest categories and subcategories based on the transaction note, speeding up data entry. The receipt scanner, which reads transaction details from images of receipts, further enhances efficiency by automatically filling in important information. The app is developed using Flutter for mobile interfaces and SQLite for local data storage, offering a clean and intuitive user experience. With this app, users are empowered with the tools they need to better manage their finances, stay organized, and cultivate positive financial habits
Vehicle speed detection using machine vision on a single-board computer
Get PDFThis proposal project is about to develop a real time speed tracking system in UTAR. In this era, more and more students drive to school, and many of them drive very fast. Although their speed cap and road bumps in school area, but many of them still unrealise that they exceed the speed limit. One of the methods to remind student how fast they drive, is to make a sign board where it can detect how fast they drive and show it on the sign board with a large 7segment LCD. This not only reminds the driver how fast they drive, but also other students around that area could also see the sign board.
This project leverages a single-board computer such as Raspberry Pi 4B and Star Five Vision Five v2 to monitor vehicle speed using a camera-based detection system. The captured data is processed to determine the speed, which is subsequently displayed on a 2-digit 7-segment display. The integration of real-time image processing with the simplicity of the display offers an efficient solution for speed detection applications. The system's design, implementation, and performance evaluation are discussed, highlighting its potential use in traffic monitoring and management systems. The platform architecture will be based on ARM and RISC-V
Large language model application integration for extracting unstructured data
Get PDFLarge Language Models (LLMs) have unlocked new opportunities in processing unstructured information, which is increasingly prevalent in industries such as logistics, healthcare, and finance. To address this growing need despite the inherent inconsistency of LLM , this project presents a data extraction framework that could reliably integrate LLMs into data processing pipelines to extract structured information from unstructured data sources, particularly images containing handwriting. The methodology involved the development of a data processing pipeline that incorporate LLMs to automate data extraction while addressing the unpredictability of LLM outputs. A novel “Sieve Methodology” was introduced to enhance output reliability through multiple iterations of validation, comparison, and refinement of the data extracted by the LLMs. This approach was prominent in making sure that the outputs were suitable for downstream processing while optimizing batch processing efficiency. The system also features support for concurrency, confidence scoring and automated template matching with cropping to improve responsiveness and scalability in real-world deployment scenarios. The framework was rigorously tested on handwritten parcel data with printed text, checkbox and signature, which is a common challenge in the logistics industry. The results were significant: the project achieved up to 96.93% of accuracy, a reduction in processing time of up to 89.46%, and a 37.70% reduction in token usage compared to baseline method where images are directly fed into LLM for processing. These outcomes clearly demonstrate the effectiveness of the proposed framework in enhancing the efficiency and reliability of data extraction processes. In the end, this project successfully developed a dual-functionality system that allows the integration of LLMs as both a standalone tool with GUI and an intermediate module (web API) within automated workflows, highlighting its practical applicability and contribution to the advancement of intelligent data extraction systems
Enjoyable - a multi-contexts and real-time audio description method on YouTube videos for the visual impaired
Get PDFVideo streaming platforms often lack sufficient accessibility features for visually impaired users, as generating audio descriptions (AD) manually is time-intensive and resource-heavy. This project introduces "Enjoyable," an online platform with an automated AD system. Instead of relying on external databases, the system enables content creators to label clustered faces directly within videos, improving character recognition across diverse genres. A structured script-based approach integrates image captions and dialogue, forming a comprehensive movie script. This script is processed by LLM-based Single-Prompt Multiturn Multi-Agent Reasoning System (SMARS), comprising agents—Investigator agent, Visual Validator agent, Context Historian agent, Integrator agent, Audio Describers agent, Syntax Fixers agent, Language Flow Expert agent, Word Count Checker agent, Fact Checker agent, Messenger agent, Target Audience agent—who collaboratively generate personalized ADs. This method enhances accessibility by streamlining visual-auditory data interaction and addressing limitations of existing AD systems
Prevalence of musculoskeletal disorder (MSD) symptoms and their associated risk factors among e-hailing drivers in Selangor - A cross-sectional study
Get PDFAs e-hailing drivers are frequently exposed to prolonged working hours, repetitive tasks, and poor ergonomics, the risk of developing MSDs is heightened. However, the prevalence and contributing factors of these symptoms remain understudied within Malaysia’s e-hailing industry. This study aimed to identify the prevalence of MSD symptoms and assess the individual, occupational, physical and psychosocial factors contributing to their occurrence. A cross-sectional study design was employed, using homogeneous purposive sampling to recruit e-hailing drivers who met all inclusion criteria. This non-probabilistic method enabled the deliberate selection of participants relevant to the study objectives. A total of 188 completed survey questionnaires were collected from drivers at various waiting areas near Kuala Lumpur International Airport. After excluding 11 invalid responses, 177 valid responses were analysed. Data collected included socio-demographic background, work characteristics, psychosocial factors and self-reported MSD symptoms via Nordic Musculoskeletal Questionnaires. Descriptive statistics, Chi-square tests, binary logistic regression, multiple logistic, and linear regression analysis were conducted to assess associations between risk factors and MSD symptoms. The study found that 82.5 % of respondents experienced MSD symptoms, most commonly in the neck (62.7 %), shoulders (54.2 %) and lower back (54.2 %). Chi-square test results revealed that muscle pain before joining the e-hailing industry (OR = 4.196, 95 % CI = 1.868 – 9.423, p < 0.001), traumatic work or road accident history (OR = 3.838, 95 % CI = 1.395 – 10.557, p = 0.006), assisting with lifting luggage (OR = 15.536, 95 % CI = 1.559 – 154.809, p = 0.017) and job dissatisfaction (OR = 3.846, 95 % CI = 1.277 – 11.628, p = 0.011) were significantly associated with the prevalence of MSD symptoms. Subsequently, binary logistic regression was performed for each significant variable, adjusting for age and BMI to control for confounding. The same four variables remained significant. A multiple logistic regression model showed that muscle pain before joining e-hailing industry (OR = 5.488, 95 % CI = 1.994 – 15.108, p < 0.001), traumatic work or road accident history (OR = 4.48, 95 % CI = 1.277 – 15.750, p = 0.019), job dissatisfaction (OR = 4.913, 95 % CI = 1.356 – 17.794, p = 0.015) and lack of stretching/ massages during breaks (OR = 3.011, 95 % CI = 1.032 – 8.787, p = 0.044) were all significantly associated with MSD prevalence. Multiple linear regression revealed that mental stress from the job (B = 2.077, β = 0.383, 95 % CI = 1.306 – 2.848, p < 0.001), muscle pain before joining the e-hailing industry (B = 1.713, β = 0.298, 95 % CI = 0.970 – 2.457, p < 0.001), napping during breaks (B = 0.987, β = 0.167, 95 % CI = 0.250 – 1.724, p = 0.009), and being a non-smoker (B = 0.860, β = 0.131, 95 % CI = 0.025 – 1.695, p = 0.044) were significantly associated with higher MSD scores. Early identification and management of MSD symptoms are vital to prevent chronic health problems. Future longitudinal studies are recommended to explore additional risk factors like whole-body vibration and vehicle ergonomics. Keywords: musculoskeletal disorders (MSDs), e-hailing drivers, occupational health, ergonomics, Nordic Musculoskeletal Questionnaire (NMQ)
Subject area: RC925-935 Diseases of the musculoskeletal syste
Hardware business management system
Get PDFThis project focuses on developing a comprehensive web-based business management system specifically tailored for hardware store owners, aiming to streamline the operations and address the inefficiencies of traditional manual methods and fragmented software solutions. The system integrates essential functions such as inventory management, sales tracking, customer and supplier management and data-driven reporting into a unified platform, significantly enhancing operational efficiency and reducing the management burden. The inventory management feature allows for real-time stock tracking, automatic updates and notifications for low or out-of-stock items, preventing supply disruptions. Work order management optimizes customer service by efficiently managing repair requests and order fulfilment, ensuring smooth operations. The sales tracking and reporting features enable business owners to monitor all transactions and analyze sales data, helping identify trends, optimize revenue and improve product offerings. Additionally, the customer management functionality helps maintain detailed profiles of customers, tracking purchase history and preferences to personalize services and strengthen customer relationships. Supplier management enables better communication and organization of supplier data, streamlining the order and allowing for more effective negotiations. The system also includes reporting and analytics tools that generate daily, monthly and annual reports on sales performance and other key metrics, with visual representations such as charts and graphs for easier data interpretation. The dashboard serves as the central hub for quick access to all key functionalities and provides real-time sales analysis. Furthermore, the live chat feature integrated into supplier management allows users to report damaged products and negotiate directly with suppliers, ensuring seamless communication and enhancing overall operational flow. By offering an integrated, user-friendly interface, this system not only improves business efficiency but also empowers hardware store owners with actionable insights, leading to better decision-making, enhanced customer satisfaction and increased growth. In the long term, the system is envisioned to expand beyond the hardware industry, evolving into a versatile, intelligent and automated business management platform applicable to various industries, contributing to the modernization of business operations
Relationship between transformational leadership components and talent retention among academic staff of public research universities in Malaysia
Get PDFGenerally known, there is a certain level of importance of transformational leadership and talent retention within the organizational growth and development of the future economy. The objective of this research is set to understand the relationship between transformational leadership components and talent retention among academic staffs in public research universities in Malaysia. In this research, the independent variables are individualized consideration, intellectual stimulation, inspirational motivation, and idealized influence, while determining their direct relationship with dependent variable, talent retention. The scope of our targeted sample size and subject to study is among the academic staffs from the 5 public research universities in Malaysia, which includes UM, UKM, UPM, UTM, and USM, and 229 questionnaire respondents are successfully collected. With the aid from Statistical Package for Social Science (SPSS) Software to run the reliability tests, explaining the correlation coefficient, and testing of each hypothesized relationships between the existing variables. After experimenting and analysis, the results shown from Pearson Correlation Coefficient and Multiple Linear Regression Analysis determine the significant positive relationship between all the existing independent variables (individualized consideration, intellectual stimulation, inspirational motivation, and idealized influence) and our dependent variable (talent retention). This study is done for the belief in enhancing the literature gap since there are lack of focus within the talent retention on academic staffs of Malaysian public research universities.
Keywords: transformational leadership; talent retention; individualized consideration; intellectual stimulation; inspirational motivation; idealized influence; public research universities; academic staffs
Subject Area: HD56-57.5 Industrial productivit
The role of credible suppliers and perceived risk in motivating Malaysian Gen-Z to purchase blind boxes
Get PDFThe blind box market has gained significant popularity globally, particularly among younger consumers, by leveraging the elements of surprise and novelty. However, in Malaysia, this trend has not achieved the same level of engagement as in other regions. This study investigates the factors influencing Malaysian Gen-Z's intention to purchase blind boxes, focusing on supplier credibility (attractiveness, trustworthiness, and expertise) and perceived risk. Grounded in the Source Credibility Theory (SCT), the research integrates perceived risk as an additional variable to explore its impact on purchase intentions. A quantitative approach was employed, with data collected from 367 Malaysian GenZ respondents aged 12 to 25 through a cross-sectional survey. The findings reveal that perceived risk and supplier expertise significantly influence purchase intentions, while attractiveness and trustworthiness do not exhibit a statistically significant impact. Specifically, reducing perceived risk traits, such as financial concerns and quality uncertainty, enhances the likelihood of purchase. Additionally, suppliers' expertise in providing accurate information and proactive problem-solving positively affects consumer confidence. The study contributes to the literature by extending the SCT framework to the blind box context and highlighting the critical role of perceived risk in consumer decision-making. Practical implications suggest that marketers and policymakers should prioritize transparency, risk mitigation strategies, and supplier expertise to foster trust and encourage participation in the blind box market. Limitations include the cross-sectional design and gender imbalance in responses, which future research could address through longitudinal studies and broader demographic sampling.
Keyword: Purchase Intention; Blind Box; Attractiveness; Trustworthiness; Expertise
Subject Area: HF5410-5417.5 Marketing, Distribution of produc
Factors shaping undergraduate students’ acceptance of E-government services in UTAR Kampar - A UTAUT perspective
Get PDFThis study explores the key determinants affecting undergraduate students' acceptance of e-government services at UTAR, Kampar Campus, Malaysia. Understanding how university students, who are both current customers and future leaders, embrace e-government services is crucial as governments speed their digital transformation projects. The Unified Theory of Acceptance and Use of Technology (UTAUT) is used to examine how performance expectancy, effort expectancy, social impact, and facilitating factors affect students' e-government platform adoption. The quantitative study surveyed 368 undergraduates from six UTAR Kampar faculties using a standardized questionnaire. SPSS was used for descriptive statistics, reliability testing, and multiple linear regression to analyze variable correlations. The results show that all four UTAUT constructs explain 87.5% of the variance in student acceptance of e-government services. The study adds several significant entries to the literature. These findings imply that students prioritize trustworthy technical support and resources when embracing e-government services, followed by peer and societal influences, simplicity of use, and perceived benefits. Enhancing technical infrastructure and support systems, building more user-friendly interfaces and simpler processes, promoting e-government services through peer networks and social media, and clearly articulating their benefits and features. The study gives useful information, but it has some flaws, like only looking at data from who worked there themselves. Future studies could look at a larger group of students from more than one school and use qualitative methods to learn more.
Keywords : e-government; private university students; students’ acceptance; technology acceptance; UTAUT model
Subject Area: JQ21-6651 Political institutions and public administratio
Securing data center with digital twins
Get PDFThis development-based project proposes an innovative approach to data center network planning by implementing Digital Twin technology to automate and accelerate network topology generation. Traditional network modelling tools, such as GNS3, rely on manual drag-and-drop configurations, which are inefficient and time-consuming for large-scale deployments. To address this limitation, this project introduces an AI-driven network diagram generator that leverages the OpenAI API to interpret natural language commands and dynamically construct network diagrams. Users can intuitively create a complete network, add, modify, or remove network components through text-based inputs, significantly reducing design time and minimizing human error. The system supports an export function that allows users to save generated network diagrams in both .png format for documentation and .json format for direct integration with GNS3. This enables seamless transfer of AI-generated topologies into a real emulation environment, allowing engineers to validate and refine their designs without rebuilding them manually. Compared to conventional methods, the proposed system provides a much faster and more efficient way to generate complex network topologies.
The project follows a structured development methodology, including requirements analysis, prototype design, API integration, and iterative testing. Expected outcomes include a user-friendly, scalable tool that enhances network planning speed, efficiency, and compatibility with industry-standard platforms. By bridging the gap between AI automation and network emulation tools, this solution provides IT professionals with a smarter and more productive approach to data center infrastructure modelling