UTAR Institutional Repository (Universiti Tunku Abdul Rahman)
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E-payment development: The adoption of E-payment among undergraduates in Malaysia
Get PDFE-payment methods have been increasingly popular in recent years, particularly among younger generation. Knowing what influences undergraduate students’ use of electronic payments is becoming progressively more important as digital technologies continue to change the financial environment. With a focus on four main variables which are perceived security, perceived benefit, social influence, and worries about counterfeit currency. This study attempts to explore the major factors influencing Malaysian undergraduate students’ adoption of electronic payments. A cross-sectional survey design was employed to obtain primary data from 400 undergraduate students from Malaysia’s public and private institutions. Convenience sampling was used, and data was collected via a standardized questionnaire delivered on both physical and digital channels. The instrument included demographic questions as well as measuring items for the four independent variables, all of which were graded on a five-point Likert scale. A pilot test with 50 respondents was undertaken to confirm that the survey items were clear and reliable. Cronbach’s Alpha was used to verify the construct’s internal consistency, and all variables met the acceptable threshold of 0.70. The findings of this study are likely to provide useful insights into how undergraduates perceive and interact with e-payment platforms. Understanding these perspectives will help to fill in the gaps in past study on e-payment uptake and the promotion of a cashless society, as well as the actual user behavior of young customers. The conclusions will also include recommendations for financial institutions and fintech companies to improve security, enhance user experience, and raise awareness about the benefits of digital payments. Finally, this study contributes to Malaysia’s digital economy goals by proposing solutions that promote higher e-payment use and financial inclusion among Malaysian undergraduates. Keywords: UTAUT model; Malaysia undergraduates; counterfeit currency; cybersecurity; electronic payment adoption; convenience Subject Area: HG1710-1710.5 Electronic funds transfer
A study on debt burden among Gen Z Malaysians: Key factors influencing personal debt accumulation
Get PDFIn recent years, the issue of personal debt accumulation has become increasingly prevalent in Malaysia. This study seeks to examine the influence of peer influence, financial literacy, spending behaviour, social media, and psychological factors on personal debt accumulation among Generation Z aged 21 to 28 in the Federal Territory, Penang, Selangor, and Johor. The proposed framework draws on the theory of planned behaviour, life-cycle hypothesis, impulse buying theory and social learning theory to explain the relationship between these factors and personal debt accumulation. The problem statement addresses the growing concern over high debt levels, driven by rising living costs, stagnant wages, easy access to credit, and weak financial literacy. Surveys show that a large share of Gen Z spend all or more than their income and lack adequate emergency savings, placing them at higher risk of unsustainable debt. A quantitative research approach was adopted, with primary data collected through a structured questionnaire. 400 valid responses were obtained and analysed using SPSS 31.0. The findings revealed that each independent variable significantly impacts personal debt accumulation, with varying levels of influence across the factors examined. The study’s results provide valuable insights for policymakers, financial educators, and institutions to develop targeted strategies aimed at improving financial literacy, promoting responsible spending habits, and reducing debt vulnerability among Gen Z. Moreover, the research serves as a reference for future studies related to personal finance and debt management in similar demographic contexts. Keywords: Personal Debt Accumulation; Peer Influence; Financial Literacy; Spending Behaviour; Social Media; Psychological Factor
Personalized workout planner
Get PDFIn modern times, people often neglect exercise due to busy schedules and reliance on conveniences like advanced transportation, leading to decreased physical activity. Additionally, the effectiveness of workouts is a major concern, as not all routines suit everyone. A personalized workout planner is essential to recommend effective exercise plans based on factors like height, weight, gender, level of activity, and specific goals. However, traditional workout plans may not adapt to injury or difficulties encountered by the user, which can lead to further complications or reduced progress. To address these issues, this project proposes the development of a Personalized Workout Planner mobile application that incorporates injury and failure adjustment features. This app aims to provide users with adaptable workout routines, ensuring safety and efficiency even in cases of injury or when the user struggles with certain exercises. The proposal outlines the project’s background, objectives, existing systems, and the proposed method and system to offer an effective, customized fitness solution. The final product of the project is the combination of FastAPI backend, Google Cloud Platform, Supabase and an React native mobile application with the ability to recommend workouts, fetch workouts, update profile for recommendation needs, feedback for 4 type of uncertainty cases and link with Google Calendar for free slot schedules
Learning sign language through puzzle-solving game
Get PDFThis project focuses on developing an innovative survival-adventure escape game, Hushh,
designed to integrate and teach sign language through an interactive gaming experience. By
immersing players in a virtual world where sign language is the primary mode of
communication, the game cleverly addresses the communication barriers faced by the deaf
community. In Hushh, players find themselves in a forest environment, interacting with nonplayable characters (NPCs) using sign language, solving puzzles, unlocking doors, and
advancing through the game. This immersive experience aims not only to raise awareness about
the communication challenges faced by the deaf but also to provide a fun and engaging way
for players to learn basic sign language
Effect of organic luminescene materials on carbon based perovskite solar cell efficiency
Get PDFIntegrating organic long persistent luminescence (OLPL) materials into carbon-based hole-transport free perovskite solar cells (C-PSCs) offers a favorable route to improving the performance and efficiency of next-generation photovoltaic cells. The majority of luminous materials in the market today are derived from an inorganic compound that needs extremely high processing temperatures and rare elements like dysprosium and europium. These materials are known as inorganic long-persistent luminescence (ILPL) materials. In the same way, OLPL materials, which are also known for their capacity to continue emitting light even after excitation stops, have special benefits for extending solar harvesting and optimizing energy conversion processes. PSCs have advanced significantly in the last few years, but there are still certain problems that prevent PSCs from being commercialized such as sensitivity to heat, light, and moisture, which cause instability and eventually reduce their performance. C-PSCs use carbon materials that can function as hole-transport layers (HTL) as well as extraction layers, helping to lower HTL not only lower production costs and improve the device's stability.
The goal of this work was to develop LPL-based C-PSC by employing carbon paste as a counter electrode. Furthermore, in this work, the use of blended OLPL materials of N,N,N′, N'-tetramethylbenzidine (TMB), and 2,8- bis (diphenylphosphoryl) dibenzo [b,d] thiophene (PPT) in PSCs was also investigated. Using the melt-casting process, several samples with various ratios of TMB and PPT were prepared. PSCs were prepared where the compact and mesoporous solutions of the electron transport layer (ETL) were deposited on FTO substrate. Then light absorber layer of perovskite was applied over the ETL layer, followed by carbon electrode layer. A simple approach was adopted in which an active layer of LPL material was externally coupled to the C-PSCs, achieving the champion efficiency of 7.65% with ILPL at ambient conditions. Besides optimizing the PSCs device, the emission decay rate, overall performance, and the dynamics of charge kinetics were studied. Among the various samples of LPL, cell-3 (TMB: PPT 7:3) has the longest lifetime and the highest photon counts, suggesting the lower rate of electron decay. A successful evaluation was conducted on the all-day C-PSC with organic and ILPL materials. The C-PSC device was tested upto 1680 hours. Unfortunately, coupling the PSC sample with the OLPL layer did not exhibit positive effects. However, the results demonstrate that PSCs can be fabricated under ambient conditions, although further improvement is required in the configuration of PSC with LPL.
Keywords: Perovskite Solar cells, Organic luminescence materials, photoluminescence, power conversion efficiency
Synthesis and characterization of benzothiazole Schiff base liquid crystals
Get PDFA series of benzothiazole-based Schiff base esters 6-methyl-2-(4 alkanoyloxybenzylideneamino)benzothiazole (BTHCn), where n denotes the number of carbon atoms in the alkanoyloxy chain (n = 12, 14, 16 and 18), were successfully synthesized. The characteristics and mesomorphic properties of the liquid crystals were also determined. The synthesis was first carried out through Schiff base condensation to produce Schiff base linkage as the main feature in the structure. Schiff base intermediate synthesized, BABT reacted with fatty acid to produce an ester liquid crystal (BTHCn). The structures of the compounds were investigated through Fourier-Transform Infrared (FTIR) spectroscopy, 1D- and 2D-Nuclear Magnetic Resonance (NMR) spectroscopy, and Ultraviolet-Visible (UV–Vis) spectroscopy. Purity and physical characteristics were examined by determining the melting point and Thin Layer Chromatography (TLC). Thermal and mesomorphic characteristics were analyzed through Differential Scanning Calorimetry (DSC). BTHCn homologues displayed distinctive mesophases, with differences transition temperatures influenced by the alkanoyloxy chain length
Suspicious activities detection for anti-money laundering using machine learning techniques
Get PDFIn recent years, money laundering activities have evolved rapidly and become a primary concern for governments and financial institutions worldwide. This type of financial crimes causes negative impacts on the integrity and stability of the global banking sector. Traditional rule-based anti-money laundering (AML) systems are static and unable to effectively detect the novel tactics involved in modern money laundering schemes. To solve money laundering, more effective techniques for detecting suspicious transactions must be developed. Machine learning is able to learn complex relationships within large datasets then identify anomalies that deviate from well-defined patterns. This enables machine learning model to detect those suspicious activities more accurately than traditional approaches.
The ultimate goal of this project is to improve the efficiency, accuracy and transparency of anti-money laundering efforts in today’s banking sector. The final product is a web-based system, AMLGuard, which incorporates a machine learning model to detect suspicious transactions related to money laundering. XGBoost is selected as the core detection engine due to its superior performance among five supervised machine learning algorithms tested: Random Forest, Naïve Bayes, Support Vector Machine and Artificial Neural Network. Explainable AI techniques are incorporated to provide clear explanations of the model’s decisions for each transaction. Additionally, AI-powered insights are integrated to offer human investigators natural language explanation and recommendation for enhancing their understanding of model output and decision making. Overall, AMLGuard demonstrates the potential of integrating advanc
Design and development of a smart used car recommendation system
Get PDFThis project focuses on developing a recommendation system dashboard project for academic purpose, specifically within the field of machine learning. The primary goal is to implement recommendation system algorithms that provide personalized used car recommendations for Malaysian users. This project is required as Malaysia faces a few challenges in the used car market. These problems include choice overload overwhelming users and lack of used car recommendation system tailored to Malaysians looking for used cars. To understand and address these challenges, reviews of past studies related to Malaysian used car market and recommendation system has been done. As a result, this dashboard will use combination of few approaches such as content filtering and clustering to ensure accurate recommendations to users. Technologies that are commonly used in recommendation systems such as Python will also be used for the development process. This project aims to contribute to local used car market by ensuring transparency and healthy interaction between buyers and authentic used car dealers. The output of this project should be able to collect preferences from user, then provide recommendations based on these preferences
Cooking assistant with nutritional tracking app
Get PDFThis project will develop a mobile application to address the need for accessible nutritional tracking and convenient cooking guidance. This project integrates computer vision and machine learning techniques into the mobile app. The problem lies in the complexity of maintaining a balanced diet while juggling busy lifestyles, with the challenge of accurately tracking nutritional intake. The technique and methodology adopted include the computer vision and convolutional neural network (CNNs) for ingredient recognition from user captured images. Moreover, creating the interface using Figma. In conclusion, this project demonstrates how artificial intelligence driven solutions may simplify meal preparation and encourage healthier eating habits. The app's ability to integrate nutritional tracking, ingredient recognition, ingredient management and voice-assisted cooking advice onto a single platform is a creative way to tackle contemporary dietary issues
Student resource exchange: a web-based system for sharing educational resources among students
Get PDFThis project focuses on the development of the Student Resource Exchange, a web-based platform designed to enhance the sharing and accessibility of educational resources among students. The platform addresses key limitations found in existing resource-sharing platforms, such as the lack of automated content analysis, insufficient content classification, and inadequate support for personalized learning. To overcome these issues, the system integrates Optical Character Recognition (OCR) for extracting text from images, Whisper for video transcription, and Google Gemini for generating concise video summaries. Natural Language Processing (NLP) techniques, such as TF-IDF with Logistic Regression for hate speech detection and BERT-based sentiment analysis, ensure safe and meaningful interaction within the platform. For content classification, the Google Gemini API with structured prompt engineering is applied to automatically organize materials into relevant academic courses. In addition, the platform introduces AI-powered study tools, including automated question generation and flashcards, as well as a built-in smart calendar planner that applies conflict detection and priority-based scheduling algorithms to help students manage study sessions and deadlines effectively. This system aims to provide a more efficient, user-friendly, and engaging platform for students to exchange educational resources, ultimately fostering a more collaborative and accessible learning environment