ABSTRACT
The Coronavirus Disease 2019 (Covid-19) pandemic outbreak has brought a significant impact to the tourism industry due to travel restrictions across the country and internationally. The implementation of the new norm and Standard Operating Procedures (SOPs) to contain the pandemic have also made the public avoiding travels to local destinations and overseas that could negatively impact Malaysia's tourism industry. The use of Augmented Reality (AR) technology as an alternative in delivering tourism experience has promising potentials as users can explore the places of interest without having to physically be in that place. However, before the deployment of such technology, the readiness of the public in using the technology must be measured. This research uses the Technology Readiness Index (TRI) 2.0 to measure the public readiness in embracing AR as the delivery medium for their tourism experience. Analysis revealed that the public is ready for the technology, but also anxious on how the technology can impact their lives. © 2023 Author(s).
ABSTRACT
This study investigated the challenges encountered by educators conducting online teaching during the COVID-19 pandemic in Malaysian and Indonesian higher learning institutions. Quantitative and qualitative methods were used to obtain information in this study. The respondents comprised 250 educators from Malaysian and Indonesian higher learning institutions. A self-developed Likert-scale online questionnaire was given to the respondents. The study findings revealed that Malaysian educators faced greater challenges in mental health, time management, and assessments. In comparison, Indonesian educators experienced more challenges in demonstrating compassion to students during online teaching. Educators in both countries encountered poor internet connectivity, lack of interaction and engagement with students, stress, and anxiety. Opportunities created by the COVID-19 pandemic comprise exploring and learning online teaching tools, producing online teaching and learning materials, conducting research, and writing research papers for publication. Recommendations for addressing online teaching challenges and suggestions for future research are also discussed. © 2023, Institute of Advanced Engineering and Science. All rights reserved.
ABSTRACT
Developmental disability are disorders caused by a deficiency in physical, learning, language, or behavior. These conditions create many difficulties for individuals in certain areas of life, particularly in language, mobility, learning, self-help, and independent living. Early intervention offers comprehensive assistance to children and families, especially children with developmental disabilities. Due to the COVID-19, these services faced difficulties in delivering direct contact solutions, including early detection and diagnosis following social distancing measures such as the movement control order. This project is being directed to assist parents and teachers to detect either the student is having any developmental disabilities mainly focusing on three types of developmental disabilities, which are dyslexia, autism, and ADHD (Attention Deficit Hyperactivity Disorder), by providing a set of questions to both parents and teachers as an early phase of diagnosis of the children. The project aims to develop a web-based developmental disorders diagnosis system for parents and teachers with children from 5 to 6 years old. In order to accomplish in developing the system, waterfall model is applied. This system produces similarity percentages based on parents' and teachers' answers, and the diagnosis results. In the future, this project can grow more significant by collaborating with healthcare organizations. © 2022 Author(s).
ABSTRACT
Real-time face mask types detection using image processing and deep learning model had seen enormous promise in real-world applications. Due to the spread of Covid-19, the practice of wearing face masks in public areas is used to safeguard people from the virus. However, to manually detect the type of face masks used can be difficult, hence this project aims to design and develop a real-time face mask detection model that can detect types of face masks worn by an individual which include 1) surgical masks, 2) KF94, 3) N95, 4) cloth or 5) double-masking. It could also identify if an individual is wearing the face masks incorrectly. This project is developed using the modified waterfall methodology. There are four phases in the methodology: (i) Requirement Analysis, (ii) Design, (iii) Implementation, and (iv) Testing. The data used for training and testing in this project was collected from available images on the internet. The data were pre-processed to remove any unwanted images and each image is then annotated with appropriate classes. The detection model was built using the You Only Look Once version 3 (YOLOv3) framework. © 2022 IEEE.
ABSTRACT
The objective of this research is to design and implement a computational model to determine DNA barcodes by utilizing the Particle Swarm Optimization (PSO) algorithms implemented on Big Data Platforms, namely Apache Hadoop and Apache Spark. The steps are as follows: (i) inputting DNA sequences to Hadoop Distributed File System (HDFS) in Apache Hadoop, (ii) pre-processing data, (iii) implementing PSO by utilizing the User Defined Function (UDF) in Apache Spark, (iv) collecting results and saving to HDFS. After obtaining the computational model, two following simulations have been done: the first scenario is using 4 cores and several worker nodes, meanwhile, the second one consists of a cluster with 2 worker nodes and several cores. In terms of computational time, the results show a significant acceleration between standalone and big data platforms with both experimental scenarios. This study proves that the computational model built on the big data platform shows the development of features and acceleration of previous research.
ABSTRACT
Semester planner plays an essential role in students’ society that might help students have self-discipline and determination to complete their studies. However, during the COVID-19 pandemic, they faced difficulty organizing time management and doing a manual schedule. It resulted in substantial disruptions in learning, internal assessment disturbances, and the cancellation of public evaluations. Hence, this research aims to optimize the recommended semester planner, Timetable Generator using a greedy algorithm to increase student productivity. We identified three-set control functions for each entered information: 1) validation for the inserted information to ensure valid data and no redundancy, 2) focus scale, and 3) the number of hours to finish the activity. We calculate the priority task sequence to achieve the best optimal solution. The greedy algorithm can solve the optimization problem with the best optimal solution for each situation. Then, we executed it to make a recommended semester planner. From the test conducted, the functionality shows all the features successfully passed. We validate using test accuracy for the system’s reliability by evaluating it compared to the Brute Force algorithm, and the trends increase from 60% to 100%. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.
ABSTRACT
Tourism is considered one of the hardest hit sectors during Covid-19 as it impacts both demand and tourism supply in various aspects. In the aftermath of the pandemic crisis, significant actions have been made to encourage people back to travel by shifting the advertising of tourist attractions via virtual tourism. It can be helpful in attracting and navigating travellers to the desired places. Some of the most exciting advancements in technology aim to maximizing the travel quality by providing valuable information to travellers before the places of interest are reached and businesses will have incredible marketing tool by promoting interactive marketing experience. Therefore, the purpose of this study is to analyse several virtual and interactive platforms in tourism which includes the virtual reality (VR), augmented reality (AR), 360-degree video and hologram. The result of the research provides an insight for the tourism industry to revolve due to the aftermath of the pandemic Covid-19. Both advantages and disadvantages of these platforms in enhancing audience experience and engagement are discussed. © 2021 Author(s).
ABSTRACT
As a result of the pandemic Covid19 and the enforcement of Movement Control Order (MCO) in Malaysia, it is difficult for the Ministry of Education to explain the selection of courses at the university. Current systems such as “Selangkah ke UiTM,” “eSemak Politeknik” and “EduAdvisor” concentrate only on SPM graduates. As a result, this study proposes a recommendation system that can provide recommendations for the course to suit students’ personalities. Diploma of Computer Science students from the Faculty of Computer and Mathematical Science (FSKM) at UiTM Melaka are selected as a case study. This system uses the rule-based approach that took psychometric test results: “Inventori Minat Kerjaya” and mapped it to the traits needed for the five courses offered at the faculty to help students determine the most appropriate course. This prototype system consists of 170 test questions, and the results of the top 3 IMK personalities are chosen to be mapped to the courses offered. Usability and accuracy testing has been carried out at an average rate of 77.5%, and accuracy is 66.7%.
ABSTRACT
Malaysia’s tourism is affected by the Covid19 pandemic and the MCO implementation, where borders are closed and non-essential activities are halted. Negative effects are also felt by Malaysians and are reflected in social media. This study examines two research questions, finding the issues that Twitter users have been addressing on tourism activities during the MCO period and analyze users’ sentiment regarding their ability to travel after MCO. 5000 data were extracted manually from 11357 data scraped from Twitter, of which 3243 were pre-processed keywords using RapidMiner. The results show that the topic of the debate focuses on three themes, namely the destination of tourism, future planning, and public emotions. In addition, 63% gave a positive view and 22% negative sentiment on domestic tourism. Overall, users of Twitter gave an optimistic outlook on domestic travel and hoped that Covid19 would soon be over.
ABSTRACT
A viral infection which is named as Coronavirus disease 2019 (COVID-19) is triggered by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). To date, almost two million cases and over 100,000 deaths from the disease caused by this virus were reported worldwide. The environmental and meteorological factors are claimed to stimulate the spread of the virus in which the transmissibility in terms of climatic fluctuations increases exponentially with high humidity and low temperature. In an attempt to understand this epidemic, there is a need to investigate the factors that could impact the spread and death of COVID-19. We, therefore, proposed to investigate global geographical climate impacts on the COVID-19 spread and death in Asia and America. The Artificial Neural Network (ANN) is a network that seeks to replicate neuronal functionality in the human brain. It is the preferred instrument for several predictive applications of data mining, due to its strength, versatility, and simplicity. A dataset of COVID-19 cases and deaths revealed from 49 states in America and 41 countries in Asia during April 2020 were tested. Nine covariates were used in the networks which are Cases, Death, High Temperature, Low Temperature, Average Temperature, Population, and Percentage of Cases over Population, Percentage of Death over Population, and Total Cases. Based on the analysis conducted, the global geographic climate is observed to have the least impacts on the COVID-19 spread and death in Asia and America particularly. However, different results could be reflected by different datasets used in the future. © 2021 Shafaf Ibrahim et al.
ABSTRACT
This paper describes the development of Non-Immersive Virtual Reality (NIVR) for the Malay and Islamic World Museum in Malacca in Melaka, Malaysia. The Covid-19 outbreak has caused the most impact in the tourism industry in the first half of 2020, and the situation will not be the same as post-pandemic. Thus, we have developed NIVR to attract the tourist or anyone interested in knowing more in-depth information about the heritage culture available in the museum. The application was designed in the 3D environment platform and focused on the Malay warfare weapons, Keris, Lembing, Tumbuk Lada, and Tekpi. We evaluate the motivation of NIVR, particularly towards user experience on the functionality and system usability score. As a result, the system features are functioning, and we manage to get a usability score of 77.08%, which rating as a good score. It means that the NIVR for Malay and Islamic World Museum Malacca system has good system usability since the user can learn more about the artifacts compared to the physical museum where they can only view the artifacts outside the glass container. © 2021 Khyrina Airin Fariza Abu Samah et al.
ABSTRACT
Due to the pandemic of Covid-19, the tourism sector faces difficulties sustaining the tourism attraction in Melaka, Malaysia's cultural heritage. Tourists could not visit the physical places easily and need to follow a standard operating procedure such as not encourage children to be around in the public area and limit the number of visitors in one time with a limited time duration. Therefore, this paper focuses on the sustainability of the Melaka cultural heritage for Hang Tuah Village (HTV) history through applying the virtual reality (VR) platform. HTV Digital tourism can be an innovative way to preserve historical artifacts and increase the location's popularity. With the development of immersive VR, tourists can explore the HTV and interact with historical objects. The historical background of HTV exploration can be done without traveling and expose to the risk of Covid-19 and damaging the historic site. The functionality of the VR tested using usability testing through the User Experience Questionnaire (UEQ), which eventually has 16 dimensions. The five dimensions applied are attractiveness, perspicuity, dependability, usefulness, and novelty. The evaluation is using a 5-point Likert scale with a total number of 17 questionnaires. As a result, the UEQ acceptance of the system is 'High', with 3.875 average overall of the mean score, and 77.5% of the respondents were overwhelming good feedback based on the experience. Thus, with the high acceptance received, it concluded that the adaptation of immersive VR could help sustain the tourism industry to preserve the history of HTV.
ABSTRACT
This paper describes a preliminary study on the adaptation of data visualization and the Internet of Things (IoT) to monitor the soil moisture of plant irrigation to sustain botanic tourism. It is compulsory to perform a daily irrigation schedule until the plants can live independently. However, due to the Covid-19 pandemic, insufficient infrastructure, work rotation at the share-farm, and unpredictable weather, it caused inconvenience for the workers to fulfill the irrigation routines twice a day. Hence, this research aimed to design and develop a mobile-based application with monitoring features that could help workers to monitor and visualize the soil moisture at the botanical area. It is using data visualization technique and enables them to perform irrigation remotely through the application via the IoT connection that linked to a water pump. It is proven that all the features of the system were functioning well and managed to receive good system usability of 77.4%. © 2020, World Academy of Research in Science and Engineering. All rights reserved.
ABSTRACT
The increase in patients with COVID-19 is overwhelming in healthcare systems around the world. Due to the large number of people affected by this pandemic, the medical and healthcare departments are facing a delay in the detection of COVID-19. Besides, it is not an easy task to clarify the images from the radiograph on what types of infection between bacteria pneumonia and COVID-19. The automatic feature analysis can help physicians more precisely in the treatment and diagnosis of diseases. In this research, Local Binary Pattern (LBP) texture features algorithm has been proposed to automate the current manual approach. This process starts by extracting the intensity grayscale texture from the normal, bacteria pneumonia and COVID-19 chest x-ray images. To prove the accuracy of LBP, a simple classifier k-Nearest Neighbour (k-NN) has been implement to classify the chest x-ray images into normal, bacterial and pneumonia class. The 10-fold cross validation has been used to validate the chest x-ray images. From the evaluation, 96% accuracy can be achieved by using LBP as a feature extraction feature. It shows that LBP is a powerful texture features to detect COVID-19 from the x-ray images. More samples will be collected in the future and neural network approach is suggested as a classifier in the future due to its ability to imitate human respond. © 2020, World Academy of Research in Science and Engineering. All rights reserved.
ABSTRACT
This paper presents to fill the gap and proposes a new conceptual model in developing an application to visualizing the reputation of communication service providers (CSP) during the Covid-19 pandemic. The outbreak of the COVID-19 caused a significant increase in the usage of voice and data using CSP. Regardless of it is seems under a protective umbrella during the pandemic, the increasing demand for CSP in a pandemic may cause customers to switch for better service. CSP companies have an abundance of data about their customers;however, the social element mainly the pithy, real-time commentary express via networks such as Twitter is often overlooked. It is due to the widely used NPS (Net Promoter Score) to measure their customers' loyalty and satisfaction. Even some of the telecommunication has started venturing into social media data analytics, the improvements required in detecting the combination of many languages used in blogs and forums. This gap inclusive the short words, not enough sentiment analytics for non-English languages, and obviously, social media in non-English languages favoured comparing to English languages. Therefore, we proposed a comprehensive conceptual model that adapted from two existing conceptual models, Simulation in Modeling CM (2008) and Integrated Framework for CM (2016). We believed it could be a guideline in visualizing the reputation of CSP that involves extracting public tweets from twitter sentiment analysis. As a result, CSP companies can get a more unobstructed view of their reputation, insights about the products and services that their customers appreciate. © 2020, World Academy of Research in Science and Engineering. All rights reserved.
ABSTRACT
As the world’s coronavirus disease 2019 (COVID-19) case total and death toll continue to climb, an increasing data collection and analysis are providing insights into the pandemic. Although outbreaks continue to develop rapidly, and researchers' understanding of the virus is increasing, a consensus is emerging on certain main aspects of the spread, symptoms, and deadliness of the virus. Enormous global data distribution on COVID-19 is made available online with a combination of global climate data, which creates an opening for further analysis to be conducted. To date, the global climate change has been studied widely, particularly regarding its influences on the distribution of species. This reflects the need for an analysis that is best suited to big data analysis which offers high performance and efficiency in understanding this pandemic issue. The state-of-art in data mining and statistics areas show that the adaptation of these methods could be the most suitable candidate for this purpose. We, therefore, proposed to investigate the influences of the global geographical climate towards the COVID-19 spread and death using a technique of Artificial Neural Network (ANN). It is believed that the proposed study could introduce a new suggestive strategy in improving the precaution measures, enhancing the new normal living activities, and to increase the performance scalability of big data processing comprehensively. © 2020, World Academy of Research in Science and Engineering. All rights reserved.