Journal of Advanced Research Design

Temperature Effect in the Calibration of NIS Reference Humidity Sensors Using a Two-pressure Humidity Generator

2024-11-01T00:54:09+07:00

Humidity sensors play a vital role in massive production technology and are widely used in industrial processes, medical facilities, museums, agricultural settings, food preservation, meteorology, etc. This paper describes the study of the effect of temperature on different humidity sensors used in practice. The analysis and findings of a study on the role of temperature in the calibration of humidity sensors are presented in this paper using a two-pressure humidity generator (2-P). Relative humidity measurements were performed at different humidity levels: 10, 30, 50 and 80 %. The temperature was adjusted at different setpoints: 25, 35, 45 and 55 °C. The data and associations were assessed using pythonic statistical software. The results show that humidity correction is contingent on the temperature and device used when considering certain conditions. The calibration correction is independent of the temperature for temperatures ranging from 25 to 35 °C, but the calibration correction is strongly dependent on the temperature for conditions above 35 °C in the range from 30 to 80 % relative humidity. The effect of temperature on the calibration of seven hygrometer models was evaluated using the humidity standard in the thermal metrology laboratory at the National Institute for Standards. The results showed that there is a strong correlation between temperature and humidity. The difference in the humidity correction factor shown from the results in this paper was applied during the calibration processes to ensure accuracy and improve measurements in the thermal metrology laboratory at the National Institute for Standards.

Microbial Contamination on Hospital Lift Buttons: A Metagenomic Perspective

2024-12-05T04:48:43+07:00

Lift buttons in the hospitals, recognised as high-touch fomites, contributed to the transmission of hospital-acquired infections (HAIs) due to frequent contact by individuals from diverse backgrounds and professions. Despite scheduled cleaning with dedicated chemicals, concerns remained regarding the persistence of microbial contamination on these surfaces, especially in Southeast Asia healthcare settings. This study aimed to explore the prevalence of microbial contamination and its diversity at a university teaching hospital (TH) in Pahang, Malaysia, during the COVID-19 pandemic. A purposive swab sampling approach was employed, with the sample collected three times at two-week intervals. Microbial prevalence and diversity were assessed using the standard plate count method and metagenomic analysis. Statistical analysis, including ANOVA and Bonferroni tests, were performed at a significance level of alpha value of less or equal to 0.05. The study revealed a significant prevalence of microbial contamination on interior and exterior lift buttons, reaching 44.4 %. An important difference was observed in the mean bacterial load between horizontal and vertical panel lift buttons, with horizontal panels contributing more to the overall microbial load (p < 0.05). Additionally, a significant relationship was found between the contamination levels of exterior lift button sets and the selected floors (p < 0.05). Metagenomic analysis identified Firmicutes as the dominant phylum, with Bacillus and Meyerozyma as the most prevalent genera. The KEGG pathway analysis emphasised the importance of ABC transporters and two-component pathways, with enriched vital genes involved in iron acquisition, energy utilisation, cell motility and drug resistance. These findings underscored the prevalence of microbial contamination on hospital lift buttons and their ability to adapt to challenging environmental conditions. Given the potential of lift buttons to harbour pathogenic microbes, it was imperative to implement effective infection control measures to minimise the risk of HAIs transmission. Future studies should broaden the scope of the research and explore diverse regional hospitals to understand the microbial contamination pattern on the lift buttons.

Thermal Energy Storage Potential of Banana Stem and Peel: Comparing Heat Conductivity at Room Temperature Post-Oven Drying

2024-12-09T13:36:14+07:00

Bananas, a globally consumed staple, generate substantial agricultural waste, particularly peels and pseudostems, which account for over 60 % of the biomass. This waste presents an opportunity for value-added applications, especially in fire safety and thermal-resistant materials. In this study, dried banana pseudostems and peels were analysed for their thermal properties using Newton’s Law of Cooling. Key findings revealed that banana pseudostem outperformed the peel in thermal resistance, attributed to its higher lignin and cellulose content, which enhance insulation. These properties suggest that banana by-products hold significant potential for sustainable, thermally resistant materials, addressing fire safety challenges in regions like Malaysia. This research underscores the dual environmental and functional benefits of repurposing banana waste, aligning with sustainable development goals and promoting eco-friendly innovations.

Automated Controlled Stirred Liquid Baths System for Calibrating Contact Thermometers in the Temperature Range from -80°C to 420°C

2024-10-21T07:04:53+07:00

National Institute of Standards (NIS) is the national metrology institute in Egypt. It consists of more than twenty laboratories working continuously to realize the international standard units (SI units). Thermal metrology laboratory (ThML) is one NIS leading laboratory that has twice responsibilities, one for realizing the thermodynamic temperature and the other for introducing calibration solutions to all organizations in Egypt such as contact, non-contact thermometry, hygrometry, thermal analysis and viscosity. In this study, a newly developed automated firmware was used for controlling and regulating the temperature of stirred liquid baths and thermometry bridge system (SLBTBS) simultaneously and determined precisely the starting time of the measurements according to smart algorisms using compatible parameters of (LabVIEW®) tools. The protocol of measurements was dependent on an automatic investigation of the best stability period whereby the setting point lied in the specific temperature ranges related to hysteresis limits. Characterization tests and assessment for optimum conditions of the SLBTBS were done. The newly designed controller gave temperature stability better than 0.02K in the low-temperature range with better-linked uncertainty.

A Comparative Evaluation of Transformers in Seq2Seq Code Mutation: Non-Pre-trained Vs. Pre-trained Variants

2025-01-02T20:44:01+07:00

Mutation testing (MT) is a gold standard way to assess the efficacy of software test suites. However, the accuracy of mutation score is affected by the presence of trivial mutants which can be “killed” by even the simplest and most basic test suites. Since the existence of trivial mutants is due to the fixed set of mutation operators that constraints the complexity of code mutations, state-of-the-art recurrent neural network (RNN) model is used for sequence-to-sequence (seq2seq) code mutation without relying on mutation operators. However, the quality of the produced mutants is affected by the limitation of RNN in interpreting the relationships between far-apart tokens of the code to be mutated. Transformers that do not have this limitation, have superseded RNN in seq2seq machine translation domains such as natural language processing (NLP). However, to the best of our knowledge, there is still no research that investigates the performance of transformers in seq2seq code mutation. This paper presents a comparison study that involves different variants of the non-pre-trained transformers, the transformers pre-trained with source code, the transformers pre-trained with natural language, and the state-of-the-art RNN model in seq2seq code mutation. The results show that transformers pre-trained with source code, especially CodeT5, demonstrated the best performance, achieving an average character n-gram F-score (CHRF) of 82.89 and superior code mutation complexity. Since the performance of transformers in seq2seq code mutation has not been previously investigated, the primary contribution of this paper is the best performing transformer for seq2seq code mutation. It establishes the foundation for the future research that proposes an integrated solution which addresses both the high-cost problem and the inaccurate mutation score problem of MT simultaneously, unlike existing solutions which only tackle one of the MT problems and give rise to other MT problems.

PV Fault Classification: Impact on Accuracy Performance Using Feature Extraction in Random-Forest Cross Validation Algorithm

2025-01-02T20:53:34+07:00

In light of the escalating global concerns regarding energy security and the irregular distribution of daily irradiance affecting photovoltaic (PV) system output, the demand for effective fault detection and diagnosis techniques in PV management systems is on the rise. Machine learning (ML) has emerged as the preferred approach, attracting extensive research attention. As the adoption of solar PV systems continues to surge, the need for robust fault diagnosis and classification techniques becomes paramount to ensure optimal performance, maintenance and scalability across diverse scales of PV arrays in real-world applications. This paper introduces a Solar PV Smart Fault Diagnosis and Classification (SFDC) model that harnesses the Random Forest (RF) algorithm in conjunction with Cross-Validation (CV) and an optimized feature extraction (FE) set. The deployment of CV serves to assess the model's performance and ensure its resilience. Additionally, an optimized FE set is employed to enhance classification accuracy by selecting the most pertinent features for fault classification. Scalable PV array is modelled with the generated power of 10 kW for small-scale, 250 kW for medium-scale and 2 MW for large-scale. In the training and testing of the models, the RF-CV algorithm with set combination of FE was employed to diagnose and classify different types of faults. In this process, each simulated fault that are line-line faults (LLF), open-circuit faults (OCF), ground faults (GF) and partial shading (PS) dataset was divided, with 80% allocated for training and 20% for testing purposes. As the results, all SFDC models (small, medium, large-scale) developed have achieved 100% accuracy for all fault types in training simulations. While in testing the algorithm, it effectively detected multiple fault types, especially OCF and PS, with a perfect score and slightly lower yet high accuracies for GF. These results indicate the robustness and reliability of the SFDC model in diagnosing and classifying faults in PV systems. This research not only holds the potential to advance the field of solar PV for future energy security but also serves as a valuable reference for researchers and policymakers, aiding in the optimization of PV system maintenance and operation.

Surficial Sediment and Littoral Environment Data Along the Shoreface of Batu Pahat, Johor Coastal

2025-01-03T15:18:21+07:00

The coastline of Peninsular Malaysia is experiencing severe erosion can result in economic stagnation and life-threatening conditions. The shoreline is the interface between water and land area which can be shifted due to numerous coastal processes that influence such as the geotechnical properties and the physical form of the beach. Hence, this paper aims to observe the characteristics of the coastal area and perform a particle size distribution test of Batu Pahat, Johor coast. Data collection for this study is divided into two (2): data collection for Littoral Environment Observation (LEO) and data collection for soil sampling. Selection of coastal area at Pantai Parit Hailam, Pantai Punggur and Pantai Perpat particularly at beach habitats on Malaysia's west coast. A comprehensive dataset of eight (8) surficial sediment samples retrieved from three coastal areas of Batu Pahat, Johor, is presented. Samples were collected between June and July of 2023 at 0.2 m depth of surface using hand auger method. The minimum and maximum average wind speed ranged between 0.89 m/s to 2.5 m/s. The sediment samples consist of Pantai Parit Hailam ranging from loamy sand to silt loam, Pantai Punggur showing mostly sandy loam with some gravelly loam and Pantai Perpat consistently exhibiting sandy loam. The findings obtained from this study provide valuable information in terms of understanding morphological characteristics that will assist in understanding of sediment transport.

A Systematic Literature Review: User Experience in Virtual Reality Prototyping

2025-01-03T15:58:27+07:00

This paper delves into the realm of enhancing user experiences within the domain of virtual reality (VR) prototyping. The study meticulously analyses insights derived from a comprehensive review of user experience in virtual reality prototyping, presenting a consolidated overview of current research findings. A meticulous screening process, involving the shortlisting of 350 papers from Scopus databases and subsequent application of rigorous inclusion and exclusion criteria, culminated in the selection of 7 papers that were thoroughly evaluated for quality. As a stepping stone for future research endeavours, the paper suggests the integration of conference articles and the amalgamation of data from diverse databases to garner a more exhaustive understanding of this dynamic and evolving field. This work contributes to the ongoing discourse on user experiences in VR prototyping, providing a foundation for further exploration and innovation in this transformative technological landscape.

Medical Records System with Blockchain (Health Chain)

2025-01-06T08:58:32+07:00

This study explores the potential of blockchain technology to address key challenges in Malaysia's healthcare system, such as fragmented data, security vulnerabilities and restricted patient data access rights. This paper presents Health Chain, a blockchain-enabled, patient-centric medical record system designed to overcome these significant issues. Health Chain enhances security for patient data by up to 63.2% compared to similar systems, leveraging the immutability and decentralization inherent in blockchain technology. The system employs cryptographic hashing and encryption methods to create a secure architecture and it empowers patients with greater control over their data through smart contracts. Comprehensive evaluation, including User Acceptance Testing (UAT), questionnaires and system testing, revealed promising results: users expressed 17% higher confidence in data security compared to other systems, with 85% finding data management tasks intuitive and easy. Health Chain also optimized administrative procedures, reducing manual data entry time, thereby enhancing healthcare efficiency and potentially leading to cost savings. These findings suggest that Health Chain could revolutionize the healthcare industry by prioritizing data security, empowering users and streamlining processes. Aligned with the vision of SDG 3, which promotes good health and well-being, Health Chain demonstrates significant potential in improving data security, enhancing patient control and reducing administrative burdens. However, further development is needed to address limitations in the scope of services provided. By overcoming these challenges and fostering continuous research and collaboration, Health Chain could play a crucial role in empowering patients, optimizing healthcare efficiency and contributing to a healthier future for all.

Design of Advanced Encryption System (AES) Algorithm Using ASIC Implementation for Internet of Things (IoT) Devices

2025-01-06T09:20:22+07:00

This paper presents an AES-128 configuration designed specifically for IoT devices, utilizing 90nm CMOS technology. The architecture, developed in Verilog HDL and executed through Synopsys tools, incorporates a modified S-Box aimed at enhancing performance, area efficiency, and throughput. The motivation stems from the increasing security requirements of IoT applications, highlighting the need for robust data protection for resource-constrained devices. Versatile for IoT applications, this design handles standard input data block sizes of 128 bits. It stands out as a purpose-built solution for securing digital communications in the IoT, overcoming unique challenges such as limited resources and fluctuating communication environments. The modified S-Box The altered S-Box bolsters security, aids in space optimization, and enhances efficiency compared to prevailing solutions. By using techniques to carefully modify replacement boxes, these designs deliver optimized performance from both a safety and space utilization perspective. Extensive validation work, including Synopsys tool testing and simulation, ensures the reliability of the proposed AES-128 design. It achieves a throughput of 14.54Mbps at a clock frequency of 100MHz while maintaining a compact footprint of 0.4324mm² to meet the constraints of IoT devices. The practical implications of this design lie in the balance between performance and resource utilization, making it suitable for real-world IoT implementations. The utilization of a 128-bit key length augments security, rendering the proposed AES-128 an ideal choice for safeguarding data across diverse IoT applications.

Reviewing the Influence of PM2.5 on Mental Health among Workers in Various Industries: Examining Schizophrenia, Depression and Bipolar Disorder

2025-01-09T10:25:14+07:00

Mental health is a crucial factor of maintaining a healthy life and optimizing the productivity in workplace. A significant number of individuals and workers in urban areas are consistently exposed to PM_2.5, which comprises diverse properties and chemical compounds, its composition varying based on its source. These particulates can be inhaled or ingested reaching different organs within the human body through the respiratory system and blood stream. PM_2.5 has the potential to inflict harms on various body systems, including the respiratory, cardiovascular, reproductive and central nervous systems. High exposure to PM_2.5 was correlated with developing symptoms of many mental disorders which can be life threatening. Each disorder arises due to the impact of PM_2.5 on distinct regions of the brain, leading to alterations in its structure and affecting its functionality. A comprehensive review explores the influence of PM_2.5 on mental health, aiming to gain a deeper understanding of potential contributors to behavioural disorders among workers worldwide at their workplaces from 2018 to 2023. Despite extensive studies on job-related factors and fatigue contributing to workers' behavioural disorders, minimal research directly addresses the impact of PM_2.5 on the mental well-being of individuals across various industries. This oversight is significant, considering that these pollutants may contribute to severe human errors in the workplace.

Parameter Tuning for Genetic Algorithm Applied to Quantum Routing with Multiple Communication Requests Across Two Different Topologies

2025-01-08T23:41:12+07:00

The quantum internet facilitates communication between quantum devices, making its development essential for advancing quantum technologies. However, realizing a fully functional quantum internet requires overcoming several challenges, including establishing efficient routing mechanisms to ensure effective communication. This study proposes a capacity allocation scheduling scheme based on a Genetic Algorithm (GA) to address the issue of non-uniform edge capacity utilization within a practical routing process. However, it is essential to note that the performance of the GA scheduling scheme is influenced by its control parameters, highlighting the need for parameter tuning. In this paper, we conducted simulations to examine how epochs and population sizes impact the GA scheduling performance, specifically in maximizing average capacity utilization and weighted throughput . The configurations were simulated under six and ten communication requests across two topology structures to assess their response to variations in problem characteristics. The simulation results were then compared with an existing capacity allocation scheduling method, Progressive Filling (PF). Our findings reveal that GA outperforms PF in all scenarios and topology structures. Additionally, we deduce that 30 epochs and a population size of 50 are adequate for optimizing average capacity utilization and weighted throughput .

New Approach of Process Cycle Time Measurement System with Sensor Application

2025-01-06T12:10:51+07:00

This paper introduces an innovative process cycle time measurement system with sensor applications designed for production line monitoring in manufacturing industries to address the challenges faced by manufacturing industries, such as inefficient production lines, machine downtime and manual production line recording. To tackle these issues, the research employs a methodology that integrates infrared sensors and microcontrollers, enabling real-time data capture and analysis. Three distinct case studies are conducted, involving balanced production lines, un-balanced production lines and production lines with machine downtime, to assess production line performance and efficiency. The results demonstrate the system's ability to accurately capture process cycle times and highlight areas for improvement in future studies. In conclusion, this research contributes to the field of production line monitoring, offering valuable insights for enhancing manufacturing processes and efficiency, with potential applications in real-world manufacturing industries.

Performance Analysis of Log-Space Harmonic Potentials for Path Planning in Indoor Environment

2025-01-06T15:58:49+07:00

In recent years, there has been an increasing interest in the study of path planning algorithms for autonomous robot systems. The current study aims to examine the simulation performance of path planning using the Successive Overrelaxation (SOR) method and the log-space Gauss-Seidel (LGS) method. The simulation addresses the numerical solution of a boundary value problem for Laplace's equation, utilising Log-space mapping and adhering to Dirichlet boundary conditions within a closed region. The experiment done in both sparse and dense environments shows the benefits of the iterative methods. The paths generated by the SOR and LGS approach are compared using specific numerical examples in smooth trajectory planning. The comparative analysis suggests that the LGS iterative method effectively addresses the issue of achieving high precision in the numerical computation of the gradient of harmonic potentials for generating safe paths in dense environments with narrow corridors and difficult regions. The simulation results also indicate that the LGS employed in this study exhibits faster convergence. Although it has a slower execution time, it is successful in dense environments compared to the SOR method.

Semi-Automatic Sentiment Identification for Malay-English Code-Switched Data

2025-01-06T16:05:08+07:00

Informal online communication on social media platforms like Twitter, Facebook, and YouTube often involves code-switching between languages, notably Malay and English, in Malaysia due to its diverse society. This phenomenon challenges sentiment analysis tasks, as the intermixing of languages within sentences or phrases increases the likelihood of inaccurately classified sentiment. Sentiment analysers built with models trained with monolingual data will cause misclassification due to out-of-vocabulary issues, thus limiting the efficacy of these analysers on code-switched data. Plus, obtaining a code-switched corpus annotated with sentiment labels is scarce, and investigations on sentiment analysis on code-switched social media data are lacking, particularly on Malaysian social media posts. We proposed MESocSentiment, a Malay-English social media corpus with sentiment labels constructed via a semi-automatic sentiment identification method to address this challenge. The framework leveraged existing language identifiers and sentiment analysers to annotate code-switched data. We collected 229,566 tweets containing #Malaysia between August 12, 2022, and May 15, 2023. Using our strategy, we identified 19714 code-switched posts containing Malay and English words from the collection. Our analysis showed that 78.23% of the corpus had neutral sentiments, while 16.32% were positive and 5.44% negative. Furthermore, the descriptive analysis of tweet length revealed a range spanning 43 words, with a mean of 8.92 and a standard deviation of 5.56 words. This comprehensive framework contributes to a deeper understanding of sentiment expression within code-switched social media data, particularly in the context of Malaysia's linguistic and cultural diversity. Additionally, the MESocSentiment corpus is published on GitHub for future research.

Malaysia's Manufacturing Landscape: Current Challenges and Production Strategies

2025-01-09T11:15:56+07:00

Manufacturing is a complex industry where various methods, tools and labour come together to produce large-scale products. Each product and industry have its unique systems, layouts and procedures. Understanding these intricacies is vital for efficiency, product quality and competitiveness among the industries. This paper aims to explore the basics of the local manufacturing sector, the different production processes used and common problems on the manufacturing floor. In a November 2022 survey with about 56 participants in Malaysia, data were collected on how each of the participants' industries operated, along with some common problems. Human, machine and system issues were identified as common concerns among the industries. Surprisingly, despite the rise of automation and Industry 4.0, 10% of production lines still rely entirely on manual labour and 80.4% are semi-automated, highlighting a continued dependence on human labour for various tasks, even in today's technological landscape.

Technological Framework of NFTs and Smart Contracts for Preserving Malaysian Arts and Cultural Heritage

2025-01-09T14:37:58+07:00

Non-Fungible Tokens (NFTs) have been developed over the past few years as an impactful new digital asset class that allows both artists and cultural institutions to securely generate revenue from their digital creations while protecting them. However, limited research exists that fully addresses the integration of NFTs and smart contracts as a technological framework for preserving and promoting artistic and cultural heritage in Malaysia. While there has been much discussion about the economic advantages of NFTs paired with smart contracts, the technical backbone infrastructure remains largely unexplored in many ways for cultural preservation. The research explores NFTs and smart contracts to offer automated royalties and an unprecedented transfer of ownership system with cultural impact. This research, through a mixed-method approach-including a literature review, interviews with experts, and the formulation of relevant mathematical models-conducted an analysis of how smart contracts automate the NFT transaction and royalty distribution. The results have so far shown that, underpinned by blockchain technology, NFTs can offer a promising solution to present and future Malaysian artists and cultural institutions in ensuring the authenticity and financial viability of traditional and modern forms of art. Besides that, scalability, environmental concerns, and legal frameworks are several sober challenges to widespread adoption. Finally, this research concludes that NFTs and smart contracts have the potential to revolutionize Malaysia's cultural economy by offering a sustainable model for heritage preservation and a supporter of the arts in general.

Effects of Surface Roughness on Friction Factors in Circular Galvanized Steel (GS) and Acrylonitrile Butadiene Styrene (ABS) Tubes

2025-01-09T10:10:17+07:00

The study of friction losses in pipes used in engineering and industry is important to improve flow efficiency and reduce the energy required to transport fluids. The surface roughness of a pipe affects its friction coefficient and its efficiency and performance. This numerical study explores numerically the impact of surface roughness on friction factors in circular tubes made of three distinct materials. The research employs computational fluid dynamics (CFD) ANSYS Fluent software to simulate turbulent flows, incorporating various surface roughness models in Galvanized steel (GS) and Acrylonitrile Butadiene Styrene (ABS) circular tubes. The simulation setup encompasses tube geometry, fluid properties, and boundary conditions. The simulation setup involves the pipe shape, fluid characteristics, and boundary conditions. The results indicate a precise link between surface roughness and friction factors, providing insights into pressure drop and flow characteristics. Validation against experimental data and known correlations enhances the dependability of the results. The findings help to optimise fluid transport systems and open the way for future study in this important technical subject. The friction factor decreases consistently with the increasing Reynolds number, and it is lower than the theoretical value. Galvanized steel (GS), with an absolute roughness of 0.15 mm, exhibits an 8% higher friction factor compared to Acrylonitrile Butadiene Styrene (ABS). This validation of the experimental data and known correlations enhances the reliability of the results.

Effect of Cement Plaster and Insulation Materials on the Thermal Performance of Masonry Wall

2025-01-09T10:14:38+07:00

The thermal performance of masonry walls plays a critical role in maintaining indoor comfort and minimizing energy consumption. However, in many developing countries, construction practices often overlook the importance of thermal insulation and mass, leading to inefficient energy use in buildings. This study investigates the effect of cement plaster thickness and insulation materials on the thermal properties of masonry walls constructed from clay bricks. The primary objective is to assess how varying plaster thickness and the inclusion of insulation materials impact thermal conductivity and heat transfer. Two methods were employed for data collection: the use of thermocouples to measure spot temperature differences and a thermal imaging camera to create surface temperature maps. The findings reveal that thicker cement plaster and insulation materials, particularly polystyrene boards, enhance the wall's thermal resistance, reducing heat transfer. Specifically, a 20mm polystyrene board reduced heat transmission by up to 51.7%. These results underscore the importance of integrating effective insulation materials in building design to promote energy efficiency and improve thermal comfort. Further research into innovative insulation solutions is recommended to optimize performance and cost-effectiveness.

Heave Response Amplitude Operator of Point Absorber Wave Energy Converter in Low Wave Energy

2025-01-09T10:18:40+07:00

Point Absorber Wave Energy Converter (PAWEC) is a floater device that moves up and down with waves. The device absorbs the orbital motion of the wave and utilize the heaving motion to drive the generator, converting the mechanical energy to generate electricity. PAWEC has been famously used by researchers as it is easy for fabrication and installation. Most of the PAWEC prototype and research recently used wave data from European seas which have high wave energy. The objectives of this paper are to analyse the heaving performance of existing PAWEC design in low wave condition in Malaysia and its average of oscillation. There are three existing designs used i.e. Wavestar, Aquabuoy and Ocean Power Technology (OPT). Three wave conditions were considered in this research i.e. 0.03 m, 0.09 m and 0.15 m. The different bottom parameters of the floater play an important role such as curved, cone and flat for effective absorption to produce energy by converted it to electricity. The results show Wavestar and OPT design point absorber Heave Response Amplitude Operator (HRAO) is improved to absorb in low wave height compared to Aquabuoy by average HRAO efficiency 33% and power output of 4(Mw). This analysis highlights the extensive research being done to bring point absorbers closer to technical maturity, paving the way for commercialization and mass production for low wave energy in Malaysia.