International Journal Of Technical Vocational And Engineering Technology

Analysis of Local Fruit Production Distribution in Pahang Based on Geoinformation Technology

Noor Suhaiza Sauti — 2024-10-31

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.

A Study On The Development Of A Heart Rate Monitoring System To Determine Its Accuracy And Effectiveness

Yusmahaida Yusoff — 2024-10-31

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.

Enriching Operational Efficiency in Industry 4.0 Through Machine Learning: A Case Study

Mohd Shukri Abdul Wahab — 2024-10-31

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

Natesh Murugiah — 2024-10-31

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.

A Brief Discovery on the Evaluation of Rock Wool and Zeolite in a Hydroponic Drip System for The Cultivation of Lettuce (Lactuca Sativa)

Norsuhailizah Sazali — 2024-10-31

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.

Implementation of Multi-Hop Communication in Wireless Sensor Network for Flood Early Warning Monitoring System

Yultrisna — 2024-10-31

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.

The Analysis Correlation of Tool Wear with Signal Frequency Domain During ST 60 Milling Process Using the Pearson Correlation Coefficient Method

Elvis Adril — 2024-10-31

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.

Design of Defect Detection Tools in Through Hole Technology (THT) Solder Joints with Classification Based on Deep Learning

Efrizon — 2024-10-31

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

Era Madona — 2024-10-31

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.

Comparative Unit Price Analysis of Reinforced Concrete Structures Pasar Inpres Painan Project Based on Actual Analysis and Standar Nasional Indonesia (SNI) Analysis

Mafriyal — 2024-10-31

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.

Determinants Of The Intention To Use E-Wallet Among Polytechnic Hulu Terengganu's Students

Nor Ayu Awang Siman — 2024-10-31

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.