International Journal Of Technical Vocational And Engineering Technology
The International Journal of Technical Vocational and Engineering Technology (iJTvET) is a peer-reviewed, open-access journal published by the Malaysian Technical Doctorate Association (MTDA). It aims to advance knowledge and practice in technical vocational education and engineering technology. iJTvET covers various topics, including innovations in Technical and Vocational Education and Training (TVET), applied research in various engineering technology disciplines, and integrating industry needs with educational programs. The journal also explores workforce development strategies, innovation and entrepreneurship within the technical and engineering sectors, and policy and management issues related to TVET and engineering. With its interdisciplinary approach, iJTvET is a valuable resource for researchers, educators, and industry practitioners. The journal is indexed in ROAD, Google Scholar, and MyJurnal, ensuring wide accessibility and visibility to a global audience.
Persatuan Kedoktoran Teknikal Malaysia (PKTM)
The content of The International Journal of Technical Vocational and Engineering Technology (IJTVET) is licensed under a Creative Commons Attribution 4.0 International license (CC BY NC ND 4.0). Authors transfer the ownership of their articles' copyright and publication right to the International Journal of Technical Vocational and Engineering Technology (IJTVET). Permission is granted to the Malaysian Technical Doctorate Association (MTDA) to publish the submitted articles. The authors also permit any third party to freely share the article as long as the original authors and citation information are properly cited.
Proof Of Concept: Integrating Deep Learning And IOT In Oyster Mushroom Farming
This proof-of-concept study explores the integration of deep learning and Internet of Things (IoT) technologies in oyster mushroom (Pleurotus ostreatus) farming to overcome challenges of manual monitoring, inconsistent yields, and resource inefficiency. The objective was to design and evaluate an intelligent system for automated environmental control and harvest prediction. Guided by the design thinking framework, the study progressed from problem identification to prototype development and testing. A solar-powered mushroom house (4 × 6 ft) was equipped with IoT sensors for temperature, humidity, air quality, and light, integrated with actuators for misting, ventilation, and lighting. Convolutional neural network models, trained using collected and historical datasets, enabled accurate harvest readiness prediction. Experimental results showed prediction accuracy of 85–92% and effective environmental regulation with reduced human intervention. Despite challenges related to sensor calibration, dataset size, and scalability, the findings validate the feasibility of IoT–deep learning integration. The novelty of this work lies in combining real-time sensing with predictive analytics, offering a sustainable and scalable pathway toward intelligent mushroom farming for smallholder agriculture.
Copyright (c) 2025 International Journal Of Technical Vocational And Engineering Technology https://creativecommons.org/licenses/by-nc-sa/4.0/
Comparative Analysis Of Visitor Face Recognition Attendance Systems: Dashboard Themes Vs Bootstrap-Based Interfaces
The integration of face recognition technology into visitor attendance systems has significantly enhanced security, monitoring, and operational efficiency across institutional settings. This paper presents a comparative study between two systems: the Visitor Face Recognition Attendance Database System Using Dashboard Themes (VFRA-DT) and the Visitor Face Recognition Attendance System with Bootstrap-Based Interface (VFRA-BI). The primary objective is to evaluate and compare the systems based on critical dimensions, interface architectures and design, recognition performance, database efficiency, and overall system reliability, to select the most suitable interface for creating a visitor face recognition system. Both systems were designed to automate the visitor check-in process, eliminate manual logging, and provide administrators with real-time data visualization. VFRA-DT features a dashboard-centric design tailored for administrative control, while VFRA-BI focuses on modularity and responsive design using the Bootstrap framework. The systems were deployed and tested under diverse environmental conditions—such as varying lighting, backgrounds, and distances—to evaluate their accuracy, response time, error rates, and usability. The results indicate that VFRA-BI achieves higher recognition accuracy (92% vs. 89%), faster attendance logging (1.8s vs. 2.1s), more responsive database querying, and reduced system downtime compared to the baseline. VFRA-BI’s use of a machine learning-driven approach and flexible interface enhances its adaptability in dynamic settings. Meanwhile, VFRA-DT remains effective in more controlled environments, offering a user-friendly interface with reliable recognition capabilities. Nonetheless, both systems face limitations in low-light and complex background scenarios, particularly with darker face images. This study provides comparative insights to support the development and implementation of efficient, secure, and scalable face recognition systems for visitor management.
Empowering Students' Problem-Solving Skills through CDIO-Based Learning Algorithm Using Sphero as Learning Tool in Programming
This action research investigates the effectiveness of a CDIO-based learning module using the Sphero robot to enhance algorithmic understanding and problem-solving skills among Semester 1 students enrolled in Programming Fundamental (SFC 10403) at Kolej Komuniti Gopeng. Eighteen students completed identical pre-test and post-test Likert-scale questionnaires assessing algorithmic comprehension and problem-solving ability, while structured teacher observation checklists tracked engagement and participation. Post-intervention mean scores increased substantially from approximately 2.17–2.44 in pre-test to 3.78–4.33 in post-test, indicating significant gains. This study addresses a research gap in the integration of CDIO pedagogy with educational robotics at the community college level, an area underexplored in existing literature. The findings suggest that combining CDIO with robotics offers a powerful, hands-on approach for strengthening programming pedagogy and aligning TVET curriculum design with Industry 4.0 skills demands, making it a valuable model for broader adoption in STEM and TVET education.
CDIO; problem-solving skills; programming; algorithmic; educational robotics;
Implementation of Educational Robotics in Inclusive Classroom from the Lenses of Pre-service Teachers
The integration of Educational Robotics (ER) into inclusive educational environments holds considerable potential for creating equitable, adaptive, and future-oriented learning experiences. This study explores pre-service teachers' attitudes and perceptions towards implementing ER in inclusive classrooms at the Institute of Teacher Education, Technical Education Campus (IPGKPT), Malaysia. Anchored within global educational frameworks, including the OECD Learning Compass 2030, UN Sustainable Development Goal 4, Malaysia’s Digital Education Policy 2023–2030, and Malaysia TVET Policy 2030, the research positions ER as central to transformative education agendas. Utilizing a descriptive quantitative research design, data were collected from 61 pre-service teachers enrolled in the Bachelor of Teaching Program (PISMP) through a structured questionnaire validated for reliability (Cronbach’s α > 0.90). Descriptive statistical analyses indicated that pre-service teachers demonstrate a very high positive attitude towards the role of technology in education (M = 4.32, SD = 0.64). Participants also highly valued ER’s potential for enriching inclusive teaching strategies (M = 4.12, SD = 0.68). Moreover, respondents recognized ER's significant contribution to developing essential competencies among students with Special Educational Needs (SEN), notably digital literacy, creative expression, and problem-solving skills (M = 4.30, SD = 0.67). Despite these positive perceptions, pre-service teachers identified critical barriers to ER adoption, notably insufficient technical skills and infrastructural limitations. The findings highlight the urgent necessity for targeted professional development and pedagogical training in ER to effectively integrate these technologies into diverse classroom settings. This research contributes to the ongoing discourse in inclusive digital education by emphasizing ER's capacity to support differentiated instruction and promote active engagement among learners with diverse educational needs. Recommendations for enhancing teacher education curricula, strategic policy implementation, and avenues for future research are provided, aligning closely with the objectives outlined in Malaysia’s Digital Education Policy 2023–2030.
Enhancing Business Pitching Skills through Teaching Innovation: Leveraging Microsoft Team Speaker Coach
In today’s fast-evolving agribusiness landscape, the ability to deliver a persuasive business pitch is no longer optional; it is essential. Agropreneurs must be able to communicate innovative solutions clearly and confidently, particularly when seeking funding or entering competitive markets. Yet, agricultural education often lacks sufficient tools for real-time feedback and iterative improvement. To address this pedagogical gap, this study employed a within-subjects pre-post experimental design to evaluate the effect of Microsoft Teams Speaker Coach, an AI-powered communication feedback tool, on students’ pitching performance. A total of 33 Diploma in Agropreneurship students from the Faculty of Plantation and Agrotechnology, UiTM Sabah Branch, participated in this study. Descriptive analysis showed a significant improvement in performance; the mean pitching score improved from 27.97 (pre-test) to 30.76 (post-test) with a statistically significant difference confirmed by a paired samples t-test (t = 6.11, p = < .001.), indicating that the integration of AI-powered feedback produced a meaningful enhancement in students’ ability to pitch agricultural business ideas effectively. Beyond offering empirical evidence for AI’s role in education, this study aligns with Sustainable Development Goal 4 (Quality Education) by embedding innovation, digital literacy, and learner-centered strategies into skill-based curriculum design. Nonetheless, the study recognises a key limitation: research on AI coaching tools for pitching remains scarce, with limited scientific validation and evidence to confirm their sustained impact. This highlights the urgent need for future studies to expand the empirical base and rigorously test AI-assisted pedagogies to ensure that their adoption in education is both evidence-based and transformative.
Technology Acceptance and Its Impact on Student Dependency in Assignment Completion at Politeknik Kota Bharu
This study aims to assess the impact of perceived ease of use, perceived usefulness, and use of artificial intelligence (AI) tools on students’ reliance in completing academic assignments among diploma students at Malaysian Polytechnics. Based on the Technology Acceptance Model (TAM) by Davis (1989), this study examines the extent to which students accept and use AI tools such as ChatGPT, Grammarly, and QuillBot in the context of daily learning. This study uses a quantitative approach with an online questionnaire distributed to polytechnic students, and the data is analyzed using descriptive statistics and regression analysis to identify the relationship between variables. The findings show that perceptions of ease of use and usefulness of AI have a significant relationship with the level of students’ use and reliance. Over-reliance is detected when students use AI not only as a learning aid, but also as a shortcut in completing assignments, thus affecting critical thinking and academic autonomy. This study concludes that although AI benefits students in terms of understanding and productivity, the level of reliance needs to be monitored ethically and systematically. Therefore, the study recommends that higher education institutions develop clear AI usage policies, increase ethical awareness among students, and include AI literacy in the curriculum as an effort to balance effective technology use and self-directed learning.
Gamified Learning and Entrepreneurial Skill Development: The Case of Business Adventure – The Entrepreneur’s Quest in Malaysian Polytechnics
Today's world shows an increasing demand for educational resources that go beyond theory, aiming to develop practical skills and encourage an entrepreneurial mindset. This is evident in the rapid changes in economic landscapes, innovation, and entrepreneurship. The Entrepreneur’s Quest: Business Adventure is an innovative educational board game designed to transform how business and entrepreneurship concepts are taught especially to diploma and undergraduate students. Moving beyond passive learning, the game engages players in an active simulation where they must navigate the complexities of starting and growing a business from launching a startup to managing a corporate empire. Unlike conventional board games, this game combines a structured knowledge system through tier up question cards covering management, marketing, strategy, and digital entrepreneurship. Players must answer questions given correctly to make strategic moves such as purchasing properties, upgrading businesses, and making investment decisions. The inclusion of real-world inspired event cards such as market crashes, tech booms, and product recalls has added layers of unpredictability and critical decision-making. By doing so, it’s allowing players to experience the volatility and excitement of entrepreneurial ventures in a lower risk setting. This study adopts a design-based research (DBR) methodology to evaluate the educational impact of the game among a group of diploma students in polytechnic. Data collected through observations, focus group discussions, and pre/post-game surveys reveal that the game significantly enhances commitment, motivation, and comprehension of core business concepts. Students reported increased confidence in applying theory to practice, along with improvements in financial literacy, strategic thinking, and teamwork. The results verify the game's effectiveness as both a revision tool and a platform for experiential learning. It encourages students to think critically, collaborate, and adapt to challenges as skills essential in today’s entrepreneurial ecosystem. Furthermore, the game's content aligns with current trends in digital business, such as innovation, online marketing, and globalization, making it a relevant and future-oriented teaching tool. This product innovation recommends integrating The Entrepreneur’s Quest into entrepreneurship and management curricula to complement conventional teaching methods. It also proposes digital adaptation and content customization to be broad and versatile. Ultimately, this gamified approach redefines how management and entrepreneurship is taught, making learning more engaging, outstanding, and impactful.
entreprenuership; management; educational board game.
Physicochemical Analysis of Cookies Enriched With Sugar Cane Bagasse Fibre
Sugar cane bagasse, a discarded plant fibre remaining subsequent to the extraction of sugar cane juice, holds significant value as an abundant source of insoluble dietary fibres. These fibres offer a wide range of applications in various industries, including both the food and non-food sectors. Despite its potential, the comprehensive analysis of the physicochemical properties of sugar cane bagasse fibre for its role in cookie production is currently lacking or disregarded in evaluation processes. This study focused on utilizing sugar cane bagasse, a by-product of sugar extraction, for enhancing cookies. The bagasse was processed into powder after steam treatment, and cookies were made with different levels of bagasse powder (2%, 4%, and 6%). The study assessed the cookies' moisture content, crude fibre, and textural properties. The findings revealed that the cookies with bagasse (F1 6%) had higher moisture content (3.67±0.1274%), and the crude fibre content (2.02±0.871%) increased with the bagasse level. While there were no significant differences in hardness, cohesiveness, and springiness between the samples, cookies with bagasse had lower hardness (14047.33±21048.66) and springiness (0.366±0.152) but higher cohesiveness (0.0733±0.01155) than control cookies. The study highlights the potential of using sugar cane bagasse fibre in cookies, contributing to waste reduction in the food industry.
Physicochemical Properties And Formulation Screening Of Rabbit Soy Patty Using Plackett-Burman Design Method
Rabbit meat is a highly nutritious and lean protein source, containing essential amino acids crucial for tissue development. Soy dreg, a high-protein agricultural by-product, can potentially enhance the nutritional profile of rabbit meat products. Despite these benefits, rabbit meat remains an unpopular choice among Malaysian consumers. This study aimed to screen the formulation of rabbit soy patty using the Plackett-Burman design (PBD), analyse protein content and texture, and determine the best formulation through response optimization techniques. Minitab software version 20.2 was used to create 12 formulations based on PBD, and the resulting patties were evaluated for physicochemical properties. Texture analysis revealed that shortening significantly affected hardness with p-value 0.046 leading to a softer texture. Protein content analysis showed that ice, isolated soy protein (ISP), soy dreg, and rabbit meat had the most significant effect with p-values of 0.004, 0.005, 0.016, and 0.023, respectively. PBD successfully identified four key parameters (ice, ISP, soy dreg, and rabbit meat) that significantly influence protein content and texture. This study demonstrates the feasibility and potential advantages of incorporating soy dreg into rabbit meat patties to increase protein content and improve texture. Future research should focus on optimizing soy dreg inclusion levels, exploring ingredient interactions, and investigating new processing methods to further enhance the nutritional profile and sensory attributes of rabbit soy patties. Effective dissemination and collaboration with industry partners are crucial to promote the adoption of this sustainable approach to food production and address global food security challenges.
Evaluating the Usability of the TrackMyStaff Mobile Application for Efficient Staff Movement Tracking
This study evaluated the usability of TrackMyStaff, a mobile application developed on the Google Appsheet platform for digitizing staff movement tracking within the Department of Mathematics, Sciences and Computer at Politeknik Ungku Omar. Aiming to replace a manual, paper-based system, the application was deployed on users' mobile devices. Usability assessment was conducted through a comprehensive questionnaire administered to all department members, focusing on identifying the application’s strengths and weaknesses across key usability dimensions: Layout, Functionality, Ease of Use, Learnability, and Satisfaction. Analysis of the questionnaire data revealed positive findings across all dimensions. Users generally reported satisfaction with the application's layout, finding it intuitive and well-organized. The application's functionality was deemed appropriate and effective for tracking staff movements, and users found it easy to use and learn. High levels of user satisfaction indicate that the application meets user needs and preferences. However, areas requiring improvement were also identified, such as limited feedback mechanisms. These findings provide valuable insights for informing design refinements and future iterations of the application. The study concludes that TrackMyStaff demonstrates strong potential for improving staff movement tracking processes within the department. It offers a more efficient and convenient alternative to traditional methods. However, further testing and development are recommended to address identified usability issues and ensure scalability and robustness for wider deployment across the institution.
Performance And Durability Enhanced Composite Nanomaterial Membrane For Fuel Cell Applications
The proton exchange membrane (PEM) based on polybenzimidazole (PBI) is developing into a promising alternative to Nafion for high-temperature proton exchange membrane fuel cells (HT-PEMFC). While Nafion membranes are effective at atmospheric pressure and low temperatures, their performance deteriorates significantly at temperatures above 80 °C, making them unsuitable for the operation of HT-PEMFCs. PBI membranes, on the other hand, have excellent thermal stability and durability, making them well-suited for high-temperature conditions. However, their relatively low proton conductivity (between 10-15 mS/cm) poses a challenge for efficient HT-PEMFC performance. This study aims to synthesise and investigate the effects of incorporating the ionic liquid 1-butyl-3-methylimidazolium chloride ([BMIM][Cl]) on the physicochemical properties, ionic characteristics, and overall performance of polybenzimidazole/graphene oxide (PBI-GO) composite membranes in HT PEMFCs. The PBI composite membranes were prepared with a fixed weight percentage of 2 wt% graphene oxide (GO) and different concentrations of [BMIM][Cl] (from 1 to 3 wt%) by the solution casting method. The results showed that the composite membrane with 2 wt% [BMIM][Cl] (PBI-GO-[BMIM][Cl] (2 wt%)) exhibited optimal ion morphology distribution and lower acid leaching (about 54% loss after 5 hours). In addition, the PBI-GO-[BMIM][Cl] (2 wt%) membrane showed a significantly improved maximum power density of 128.45 mW cm-² at 160 °C compared to the PBI-GO-[BMIM][Cl] (2 wt%) composite membrane, which showed a maximum power density of only 106.70 mW cm-² at 25 °C. The improved performance of the PBI-GO-[BMIM][Cl] (2 wt%) membrane indicates its potential as a cost-effective and highly efficient material for HT-PEMFC applications. The use of this membrane in fuel cell technology could lead to significant cost savings in maintenance and energy consumption, while contributing to a reduction in carbon monoxide and carbon dioxide emissions, in line with industry targets for sustainable energy solutions.
Do You Prefer Virtual Tourism (VT)? The Use of Minecraft in Developing Titania Virtual Theme Park
Virtual theme parks (VTPs) are revolutionizing entertainment by merging traditional amusement with digital technology, offering immersive home-based experiences. This study developed an accessible and enjoyable VTP on platforms like Minecraft, specifically targeting inclusive tourism experiences for individuals with disabilities. We investigated factors influencing the intention to participate in VTP tourism, grounded in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Key factors examined included performance expectancy, social influence, facilitating conditions, hedonic motivation, price value, and innovativeness. A preliminary quantitative study involving 50 participants, utilizing an adapted questionnaire, revealed that hedonic motivation had the strongest correlation with intention to use, emphasizing the need for engaging VTP experiences. Performance expectancy also showed a strong positive correlation, highlighting the importance of high-quality, reliable platforms. Facilitating conditions and price value were significant, indicating that practical considerations like resource availability and cost-effectiveness are crucial for adoption. Social influence and innovativeness further underscored the role of social dynamics and openness to new technologies. The study concludes that VTPs offer a forward-thinking, sustainable, and inclusive solution for the entertainment industry. Future research should explore long-term sustainability, environmental impacts, and VTPs' potential to bridge the gap between sustainability, gaming, and tourism, optimizing experiences for diverse groups, especially individuals with disabilities.
The Cultural Visual Elements Significance of Malaysian Spice Powder Packaging Design
Culture is the set of shared, fluid, and abstract beliefs, values, routines, traditions, and artistic expressions that exist within a particular social group. The research aims to understand the cultural significance of visual elements in spice powder packaging design. 104 participants will be surveyed to understand their preferences for cultural representation. The results will provide insights into how to integrate cultural factors into packaging design, ensuring customer resonance without cultural appropriation. The study uses songkets, floral patterns, colour, font, photography, and traditional motifs to represent Malaysia's cultural diversity. The goal is to advance culturally sensitive and inclusive design techniques, increase branding, and improve the visual appearance of the final packaging product.
The Experimental Speed Control of DC Shunt Motor Using Field Control Method via MATLAB Simulink
DC Shunt motor speed control using a field control method is a method that researchers have studied. The study of the characteristics of the motor, primarily in controlling the motor speed, is performed by this research with a constant input voltage. The research was done to indicate that both techniques, which are laboratory experiments and the MATLAB/Simulink model, can be executed. The outcome is to produce the desired speed of the DC Shunt Motor model via MATLAB/Simulink, as well as the result from the laboratory experiment. The external resistor is used in this experiment as a rheostat, where the resistance is varied. Variation of external resistance appears to reflect on the control of the speed of the DC motor. Simulation and laboratory experiment results indicate that the speed using the simulation model via MATLAB/Simulink is quite similar to the speed using the laboratory experiment result, and based on that, it shows the DC Shunt motor speed can be controlled using a field control method.
Developing Carbon Monoxide Gas Detectors In Vehicle Cabins
A vehicle will produce carbon monoxide (CO) gas when an incomplete combustion occurs. CO is odourless and colourless. The MQ-9 sensor is installed on the vehicle to detect the presence of CO in the vehicle cabin. The detection of CO in the vehicle cabin involves the MQ-9 Sensor, Arduino Mega 2560, and Raspberry Pi. When the MQ-9 Sensor detects CO in the vehicle's cabin, it will send a voltage to the Arduino Mega 2560, and the Liquid Crystal Display (LCD) will display the existence of CO. It will also display the activity of the MQ-9 Sensor. This system will generate an alarm signal and activate the Power Window Motor to pull the vehicle window open 2 inches to increase the Oxygen content in the vehicle. The alarm will sound again when the MQ-9 sensor no longer detects the presence of CO in the vehicle cabin, and the Power Window Motor will raise the window again. At the same time, Raspberry Pi will send information about the presence of CO to the vehicle owner's mobile phone. Indirectly, this approach will help prevent deaths from CO poisoning in vehicles.
Batch Adsorption Of Manganese (II) Ions Using Fruit Peels: Equilibrium And Kinetic Studies
Manganese (Mn) is recognized as a potent neurotoxin, known to elicit severe health implications such as hallucinations, learning impairments, and Mn-induced parkinsonism in both adults and children. In 2021, the World Health Organization (WHO) established a provisional guideline of 0.08 mg/L Mn compared to 0.4 mg/L in 2004 due to mounting evidence underscoring Mn neurotoxicity. Over the years, numerous conventional methods have been explored to address this pressing concern. This study, however, explores an innovative approach focusing on the adsorption process employing waste fruit peels (LOLAC), which are from orange, lemon, and langsat, to remove Mn (II) ions. The primary objective is to ascertain the optimal conditions for manganese adsorption utilizing LOLAC. Parameters influencing Mn (II) ions biosorption, including pH, biosorbent dosage, initial adsorbate concentration, and contact time, were investigated. Batch mode was adopted for its inherent controllability, while UV-Vis spectrophotometry facilitated initial and final Mn (II) ion concentration measurements. Subsequently, the percentage of Mn (II) ions removed was computed to elucidate adsorption equilibrium and kinetics. Experimental outcomes unveiled orange peels as the most efficacious, achieving a remarkable 96.3% removal of manganese under conditions of pH 6, 0.8 g/100ml biosorbent dosage, 100 ppm initial adsorbate concentration, and a contact time of 90 minutes. The Langmuir isotherm model and pseudo-second order kinetics exhibited commendable fitting with the data of Mn adsorption onto LOLAC, indicating a monolayer adsorption mechanism with a second-order reaction rate on the adsorbent. Consequently, activated carbon from orange peel emerges as a promising, cost-effective biosorbent for the remediation of manganese ions from wastewater, outperforming its langsat and lemon peel counterparts.
Fabrication of Biodegradable Mango-Banana Waste Planting Bag: A Sustainable Alternative to Polyethylene
Polymer-based planting bags are presently employed in nurseries and commodity plantations for cultivation. A notable drawback associated with the use of these bags in agriculture is the difficulty in their proper disposal once they have completed their intended function. The continued presence of polybags in the soil contributes to heightened levels of toxicity. In response to environmental concerns, biodegradable planting bags have been introduced on a global scale. Hence, this research had been conducted to characterize the properties of the mango-banana waste planting bag prepared. Three difference sample was fabricated with and without the presence of glycerol and with difference ratio of glycerol. The purpose of this project is to design biodegradable planting bags made from mango peel and banana stem that are more environmentally friendly. The study on mechanical properties focused on water absorption test and bursting test whereas the biodegradation ability of mango-banana waste planting bag had been analysed using soil burial degradation test. The results shown that the highest water absorption test is Sample 1, followed by sample 2 and 3. The visual inspection from bursting test shown that all sample exhibited an excellent root growth from biodegradable planting bag after 30 days. The highest weight loss percentage in composting is Sample 1 with 67% as compared to sample 2 and 3 with 75% and 86%. This study analysis emphasis that mango-banana planting bag with and without glycerol can function as an alternative in replacing the polyethylene planting bag and help in addressing environmental issue.
A Study On The Development Of A Heart Rate Monitoring System To Determine Its Accuracy And Effectiveness
Heart rate monitoring systems are valuable tools in both medical and fitness settings since they provide crucial data for health assessment and activity optimization. The study's goal was to build and execute a heart rate monitoring system that used modern sensors and signal processing techniques, as well as to test the system's accuracy against a known heart rate measurement instrument. The development process involves System Design, which incorporates optical and electrical sensors to gather heart rate data, as well as a microprocessor for signal processing and transmission. The next step is prototype development, which focuses on portability and user comfort. Testing and validation entail using human participants to compare system results to those from clinically approved heart rate monitors. Various activities, such as relaxing, walking, and severe activity, are used to evaluate performance in various settings. The designed heart rate monitoring system has good accuracy, with an average deviation of less than 5% compared to reference devices. Accuracy was constant throughout activities, with little differences noted during high-intensity exercise. The device tracked heart rate variations in real time, providing accurate data for both short- and long-term monitoring. The study emphasizes the relevance of sensor quality and signal processing in creating accurate heart rate monitoring devices. While the system performed admirably in most situations, further refinements could increase accuracy during high physical activity. The use of machine learning techniques for adaptive filtering and noise reduction holds promise for future iterations.
Copyright (c) 2024 International Journal Of Technical Vocational And Engineering Technology
Determinants Of The Intention To Use E-Wallet Among Polytechnic Hulu Terengganu's Students
Malaysia's e-wallet market is already expected to expand significantly towards a cashless society. It performs similar functions to a physical wallet, but without the physical hassle of having to store the clutter of several cards, cash, and other items. There are numerous reasons that influence consumers in using e-wallets. Thus, it is very important to know the factors affecting the intention to use e-wallet among higher education students in Malaysia. The research aims to investigate the determinants of the intention to use e-wallet among the Polytechnic Hulu Terengganu’s students based on Technology Acceptance Model. The research attempts to measure the relationship between perceived usefulness, perceived ease of use and perceived security with the intention to use e-wallet. This quantitative research method was employed via online survey of 97 accounting students at Polytechnic Hulu Terengganu, Terengganu and data were analysed using SPSS version 28. The result showed that 86 students (88.7%) were familiar with the e-wallet. Meanwhile, the finding concluded that perceived usefulness, perceived easy to use and perceived security have significant positive relationship with the intention to use e-wallet. This finding indicates that people would like to look for convenience, usefulness and secure platform in their daily transaction. Overall, the results of this study may provide government, financial institutions and facility providers on the guidelines that could provide more efficient services as to improve e-wallet services according to what the consumers desire.
Utilizing Chitosan Extracted From Shrimp Byproducts As A Bio-Coagulant For Treating Oily Waste Emulsions
Across sectors, the coagulation process is widely used to remove colloidal contaminants, particularly suspended particles like bacteria and organic debris. But because of their natural source and biodegradable nature, using bio-coagulants offers a more feasible option. With a monthly production range of 20 to 60 tones, this project focuses on the extraction of chitin and chitosan from shrimp waste with the goal of using chitosan as a bio-coagulant for the treatment of oily waste emulsions. The use of chemical coagulants, such as ferric chloride and aluminum sulfate, raises questions about their potential negative effects on the environment and human health. These coagulants are particularly difficult for smaller-scale enterprises to obtain. Therefore, the main goals of this project are to extract chitosan from shrimp waste and assess how well it works to remediate oily waste emulsions. Deproteinization, demineralization, and deacetylation processes are all included in the extraction process. Testing that comes after this evaluates oily wastewater by employing powdered and coarse chitosan, and coagulation processes are tested in jars. The findings demonstrate that powdered chitosan performs better than coarse forms, with an effectiveness of coagulation activity is 91.79%, fulfilling the project's goals. To improve chitosan's ability to cure oily waste emulsions, it is advised to adjust the dosage to 0.1g/ml – 0.15g/ml. This study highlights the possibility of using chitosan made from shrimp waste as an efficient and sustainable coagulation process substitute, posing a threat to traditional chemical coagulants. This strategy complies with green chemistry and sustainable industrial practices by optimizing environmental effects and utilizing natural resources.
Comparative Unit Price Analysis of Reinforced Concrete Structures Pasar Inpres Painan Project Based on Actual Analysis and Standar Nasional Indonesia (SNI) Analysis
Work concrete is a very important factor in development projects because this work has a larger volume than other work projects. The contractor must have more details inside analyze the price of unit work to get effective and efficient cost. Contractor often makes price cost analyses different from Standard National Indonesia (SNI), over cost or under cost. The contractor should analyze unit prices based on actual conditions to ensure a cost-efficient project budget. This research aims to count the total cost structure concrete reinforced (columns, beams, slabs) Pasar Inpres Painan Projects, Pesisir Selatan, West Sumatra, Indonesia with actual analysis and Standard National Indonesia (SNI) analysis. The Standard National Indonesia (SNI) method provides guidelines for calculating unit price analysis that develop standard national construction, with adapt conditions using AHSP 2023 published by the regulation minister of PUPR no. 8 in 2023. In the actual method, unit price analysis is based on index cost from daily project progress, including quantity and productivity. The results of the research, with actual methods analysis, obtained a total project cost of IDR. 10,979,791,988.17, with the column cost at IDR 2,064,056,033.12, the beam cost of IDR 5,864,218,409.93, and the slab cost at IDR 3,051,517,545.13. However, the SNI method showed the total project cost of IDR 15,371,771,346.57, with the column cost at IDR 2,877,891,431.64, and the beam cost at Rp. 7,877,841,167.72, and the slab cost at IDR 4,616,038,747.19. Hence the Actual method analysis showed that the total cost project is much cheaper than the SNI analysis, IDR 4,391,979,358.40 or 28.57. Analysis based on actual conditions project can be efficient, causing index cost from daily project progress including quantity and productivity, if workers increase, actual total costs will be efficient.
Design of Defect Detection Tools in Through Hole Technology (THT) Solder Joints with Classification Based on Deep Learning
A tool has been created for soldering defect detection to reduce the processing time for testing the quality of solder joints for THT PCBs in soldering practical activities at the Padang State Polytechnic. This tool is equipped with the help of a Logitech c920 Webcam and a Jetson Nano microprocessor which is used to store and run programs that have been created in Python programming software so that this tool can be used portably. The research method starts with creating a dataset, labeling, training data, and making tools. In this research, the YOLO Convolutional Neural Network method was used to help determine soldering defects with four classifications, namely hole soldering, bridge soldering, void on solder pad, and good soldering. The test results showed that the tool was able to detect these four classifications with mAP@0.5 values, Bridge 97.20, Good 54.50%, Hole 83.90%, and padless 57.20%. Overall, the tool can function well.
Fall Detection Tool for Vertigo Patients Using The Artificial Neural Network Backpropagation Method With Telegram Notifications
In this research, a fall detection tool was developed using a backpropagation artificial neural network (ANN) algorithm, especially for patients who are at risk of experiencing vertigo and syncope with telegram notifications. This research aims to design and implement a fall detection system for six different movements, namely sitting, walking, bending, falling sideways, falling backward, and bending using ANN Backpropagation as a decision-making system from previous research which used thresholds. The research stages were carried out by processing data and solving problems to determine the position of the accelerometer sensor, activities carried out during data collection, and characteristics of fall conditions. Data collection consisted of 120 training data and 60 test data. The results of the research show whether the recognition of falling movements has been tested using the backpropagation method at two iteration stages, namely at 10,000 epochs, and 17,000. The best results were obtained at 17,000 epoch iterations with an accuracy rate of 94%, while in the 10,000-epoch iteration the accuracy level obtained was 92%, whereas without using the method, the accuracy was 85%. The HTTP protocol was used for data communication between the device and the database server in 17 seconds so that in 1 minute 4 data is collected.
The Analysis Correlation of Tool Wear with Signal Frequency Domain During ST 60 Milling Process Using the Pearson Correlation Coefficient Method
High Speed Steel Cutter (HSS) is widely used in milling processes to cut workpieces, one of them is ST 60. However, cutter weariness is a major problem that can affect the quality of milling and production costs. This is because a prolonged cutting process can cause gradual damage to the cutter's eye due to repeated friction between the cutters and the treated material. Therefore, the study aims to analyze the weariness of the HSS cutter during the ST 60 milling process by using a domain frequency vibration signal approach and periodic weight measurement of the cutter to monitor its weariness. The method to be performed in this study is to perform a milling operation on the ST 60 material using a HSS cutter as well as recording the vibration signal and measuring the cutter's weight at each experimental interval. The result of the vibration signal recording will be processed using Fourier transformation (FFT) to monitor vibration frequency changes that reflect cutter damage. It is hoped that this research will provide a deeper understanding of the mechanisms of HSS cutter depletion, thereby improving the quality of the result and optimizing the cutting conditions to slow the rate of the device.
Implementation of Multi-Hop Communication in Wireless Sensor Network for Flood Early Warning Monitoring System
A system has been created for a flood early warning monitoring system using LoRa-based multi-hop communication based on several limitations that LoRa has in its implementation of single-hop communication and end devices are located in dark spots or areas that are difficult to reach by the network. The research method starts with creating sensor nodes, relays, and gateways and measuring system performance. The results of system performance measurements are shown by the highest average RSSI value for multi-hop communications being -56.6 dBm at a distance of 20m and the lowest being -85.2 dBm at a distance of 300 m. Meanwhile, the highest average RSSI value for single-hop communication was -79 dBm at a distance of 20 m and 50 m and the lowest was -85.2 dBm at a distance of 150 m. The PDR value in multi-hop communication is 100% up to a distance of 300 m and the PDR value in single-hop communication is 100% up to a distance of 150 m, while in multi-hop communication it is 100% up to a distance of 600 m. The system can monitor data readings from rain sensors and ultrasonic sensors using the NodeMCU ESP8266 on the Thinkspeak web server via a smartphone screen. Overall the tool can function well.
A Brief Discovery on the Evaluation of Rock Wool and Zeolite in a Hydroponic Drip System for The Cultivation of Lettuce (Lactuca Sativa)
There is a growing interest in the utilization of soilless media, such as zeolite and rockwool, for cultivating vegetables in drip hydroponic systems. The motivation behind the selection of zeolite was due to its high cation exchange capacity (CEC) and ability to retain moisture. A study was conducted to determine the impact of zeolite and rockwool on the growth and leaching analysis of lettuce in a drip hydroponic system. The lettuce obtained fertilization through a nutrient solution that contains both macro and micronutrients. The solution's electrical conductivity (EC) ranged from 1.2 to 1.9 dS/m, with a pH of 5.75 and a temperature of 30-32 ºC. Based on the study on lettuce growth, an additional amount of zeolite in the rockwool system resulted in an increase of 11.48% in stem diameter, 5.13% in root weight, 25.5% in dry root weight, 20% in shoot weight, and 20.23% in dry shoot weight, as compared to rockwool only. However, there were no changes in lettuce height and leaf diameter identified. Based on the analysis of the hydroponic wastewater solution, the concentration of nitrate, nitrite, and ammonia ions decreased by 41.17%, 50%, and 75%, respectively, in the presence of zeolite in rockwool system compared to rockwool only. The presence of zeolite has a significant impact on reducing the concentration of nitrate, nitrite, and ammonia ions, however, it has a minimal effect on the growth of lettuce’s height and leaf diameter.
Enriching Operational Efficiency in Industry 4.0 Through Machine Learning: A Case Study
The rapid advancement of Industry 4.0 has brought significant transformations across multiple sectors, necessitating the adoption of innovative technologies to boost operational effectiveness. Machine learning has emerged as a powerful tool for optimising processes and driving performance improvements. This paper presents a case study conducted at a fuel station to evaluate the impact of machine learning on operational efficiency within the industry 4.0 framework. The primary aim is to assess how machine learning algorithms can address operational challenges and enhance overall performance in a fuel station setting. The study thoroughly analysed operational data, particularly inventory management, to identify areas ripe for optimisation. Using the Waikato Environment for Knowledge Analysis (WEKA) software, tailored machine-learning models were developed to meet the specific needs of the fuel station, incorporating techniques like predictive analytics. The case study demonstrated notable improvements in operational efficiency through machine learning integration. The fuel station optimised inventory levels and minimised downtime by leveraging historical data and real-time insights. The implementation of machine learning facilitated proactive maintenance scheduling and reduced equipment failures. This research highlights the potential of machine learning to transform operational processes within the fuel retail industry and beyond, particularly in the era of Industry 4.0. The findings stress the importance of embracing technological innovations to thrive in this evolving landscape, offering valuable guidance to industry practitioners seeking to enhance efficiency and competitiveness. Recommendations include further exploration of advanced machine learning techniques and investment in digital transformation initiatives to unlock more outstanding operational excellence in industrial contexts.
Review on AutoSeed: Smart Seeding Tray Robot with IoT Integration
The rapid advancement of technology presents both opportunities and challenges for contemporary agriculture, necessitating innovative solutions to address labor shortages and inefficiencies. This report delineates the design, development, and application of Autoseed: Smart Seeding Tray Robot with IOT Integration aimed at revolutionizing traditional seeding practices through the integration of robotics and renewable energy technologies. The primary objective is to develop a low-cost, solar-powered agricultural robot equipped with sensors, transceivers, and computational devices to perform tasks such as planting seeds, watering, and monitoring soil moisture and pH levels. The project employs a sophisticated Cartesian arm system for precise seed planting within seeding trays, significantly improving efficiency and accuracy over manual methods. State-of-the-art moisture and pH sensors are incorporated to enable real-time monitoring of soil health parameters. The robot's functionality is enhanced by sun tracking technology, which optimizes solar panel efficiency and ensures continuous operation even in off-grid settings. Users can access comprehensive soil condition data via a seamlessly integrated mobile application, facilitating informed decision-making and tailored adjustments to optimize seed growth environments. Key findings demonstrate Autoseed's potential to alleviate labor scarcity and reduce human error while promoting sustainable agricultural practices. By harnessing solar power and incorporating a rechargeable battery system, Autoseed minimizes its environmental footprint, aligning with ecological consciousness. The system's performance was tested and validated in various agricultural tasks, showcasing its capability to enhance farming efficiency and sustainability. Autoseed represents a significant shift towards automated, sustainable farming, promising increased efficiency and food security while mitigating ecological impact.
Analysis of Local Fruit Production Distribution in Pahang Based on Geoinformation Technology
Fruits play a pivotal role as a staple food in society and contribute significantly to the country's economy. Consequently, information regarding fruit production holds paramount importance for human activities across various sectors, including agriculture, industry, and food resources. The statistical data on fruit production, provided by the Department of Statistics Malaysia, is currently presented in tabular form and has not been transformed into specific maps accessible to the public. Therefore, employing geoinformation or Geographic Information System (GIS) technology, georeferencing is executed to precisely pinpoint the coordinates of cities and regions in Pahang. The choropleth map method is applied, utilizing corresponding colours to depict density or variation units and present aggregate summaries effectively. As a result of this process, 11 maps were generated, each depicting the production of 11 different types of local fruits across various regions in Pahang. The study's significance lies in its capacity to assist users in accessing information about the distribution of fruit production by regions in Pahang for the year 2020. The transformation of raw statistical data into visually informative maps enhances accessibility and facilitates a comprehensive understanding of the local fruit production landscape.
The Effectiveness of Game-Based Learning: HRM Monopoly among students at Politeknik Muadzam Shah
The 21st-century educational landscape increasingly integrates creativity and entertainment to enhance learning, particularly for subjects that blend theory and practice. This study addresses the need for innovative pedagogical tools by introducing Human Resource Management (HRM) Monopoly, a game-based learning intervention designed to make HRM theory and practice more engaging and comprehensible. Games are known to improve learner engagement, stimulate critical thinking, enhance soft skills, and ultimately optimize knowledge acquisition by fostering a relaxed learning environment that extends attention spans. HRM Monopoly caters to groups of fewer than eight players, who earn points and in-game currency by correctly answering questions related to HRM concepts. This research involved 58 polytechnic students enrolled in a business studies program. Post-test results, derived from questionnaire data, demonstrated significant improvement in students' comprehension of Human Resource Management. Specifically, 91.4% of respondents agreed or strongly agreed with improved knowledge, and 92.7% indicated increased interest in the subject after engaging with HRM Monopoly. While effective, a notable limitation identified was the potential for budget constraints to impede the adoption of educational games. Despite this, the findings suggest that HRM Monopoly is an effective tool for cultivating student interest and enhancing understanding during the teaching and learning process. This research advocates for game-based learning as a valuable solution to address challenges associated with memorizing and comprehending theoretical concepts in higher education.