Tech Horizons

Schizophrenia is one of the mental health disorders that has gone unnoticed due to a lack of understanding about the illness. Furthermore, there is a lack of digital games that can assist in spreading the word about schizophrenia. Thus, the WHTA, a 2D platform narrative game, was produced to raise awareness about schizophrenia among Malaysian citizens. The player takes on the role of Charlie, an 18-year-old young adult on a quest to find his companion Aubrey, who has gone missing. The players need to escape each level by finding enough clues that will reveal information about schizophrenia by fighting obstacles. The GDLC had been used as a guideline in the development process which consists of pre-production, production, post-production, and testing. Then, the playtesting had been conducted using the UEQ that measures the six elements including (i) attractiveness, (ii) perspicuity, (iii) efficiency, (iv) dependability, (v) stimulation, and (vi) novelty. A total of 33 respondents were involved during the playtesting. The game received positive reviews and comments in terms of the game design. For future work, the game can be enhanced including the game content, game-level design, and game feedback.

With the ongoing swift growth of computer technology and the growing degree of automation in manufacturing processes, it is apparent that software applications will continue to take over a rising number of jobs and roles. MATLAB is a widely used software tool in chemical processes to enhance product quality, reduce manufacturing costs, shorten operational time, and reduce environmental impact. This study investigated the application of MATLAB-based simulation in image processing algorithms for materials’ characteristics. MATLAB is a useful tool for precisely characterizing material properties with less simulation time, accuracy, easy and quick visualization, and lower labor and expense.

Electronic commerce is one of the implementations of information technology with the internet in the business sector. The rapid increase in e-commerce can significantly contribute to the economic sector. The e-marketplace is a new opportunity for online sellers to market and sell products without investing in a selling platform. This study aims to find literature on the factors influencing sellers in choosing e-marketplaces to sell their products. The guidelines used in this literature study are the selected reporting items for systematic review and meta-analysis guidelines, and the articles used are articles published in 2018–2022. There are 125 articles obtained from various databases, including IEEE Explore, Science Direct, and others. Validation and testing were conducted to obtain 36 articles that could be used as primary studies. As a result, ten factors influence sellers choosing an e-marketplace: platform, trust, service operations, marketing, and sales, quality of information, products, product reviews, perceived risk, ease of use, and payment channels.

This study examines the application of data analytics in tax administration. The paper describes how data analytics methods such as predictive modeling, data mining, and machine learning have altered the way tax authorities operate by enhancing efficiency and accuracy while decreasing the amount of time and resources required for tax compliance. In addition, the article investigates how big data analytics has enabled tax authorities to scan massive volumes of data, including unstructured data in order to discover potential noncompliance and assess tax risks. The conclusion of the study is that the application of data analytics in tax administration has revolutionized tax administration by increasing compliance, decreasing expenses, and raising overall efficiency. It is anticipated that, as technology continues to improve, the application of data analytics in tax will continue to evolve, thereby boosting the efficiency of tax administration.

Aircraft mechanics need digital competencies for maintaining sophisticated, interactive, and collaborative aircraft engines and systems. The RMAF fundamental training does not assess the digital skills of technicians. Therefore, this study devised survey assessment components based on IR4.0 that incorporate digital skills and competencies for aviation maintenance tasks. This study employed seven criteria to evaluate the skills and competencies of aircraft maintenance technicians: problem solving, communication, active learning, technical knowledge, analytical and critical thinking, technological skills and experience, and lessons learned. Three expert review panels on aircraft maintenance and IR4.0 readiness validated the developed survey. The study found that ITAS technicians had the lowest mean evaluation score, 15.441 and the highest standard deviation, 8.630, indicating a wide range of variance compared to other selected squadrons. The F-statistic and p-value of the ANOVA analysis indicate < 0.001 which significantly shows that assessment scores differed across groups. Thus, in line with IR 4.0 requirements, enhancement trainings are desirable to upskill the digital skills of technicians.

The Internet of Things (IoT) technology has received a lot of attention in business, smart cities, smart grids, autonomous vehicles, industrial internet and academic worlds in recent years. This gives the idea to make a tracking system with IoT applications that consist of real-time visualization where it can monitor and track a person using the device. Nowadays, every parent allows and trains their children to explore everywhere, for example, playing with friends in the playground, going to school and even to the nearby shop to buy some necessities. However, this situation often makes parents worry about their children's safety when they are outside the residential area. As reported by Bukit Aman’s criminal investigation department (CID), an average of 78 cases of abduction and disappearance of children under the age of 12 were reported every month in 2022. Therefore, this project aims to assist parents in tracking the location of their children's position also used to be a smart tracker for visualization detection and could track the location of vehicles. The main components used are the ESP32 microcontroller as the integrated wireless communication and the Global Positioning System (GPS) as the geolocation and time provider. This proposed project can help parents monitor the whereabouts of their children through the internet and keep track of the children in real time.

Enterprise architecture (EA) integrates and develops organisational components for strategic planning. It helps define IT component linkages and engage paramedical workers in healthcare. It also creates the greatest healthcare enterprise architectural framework. Commonly, the senior management must grasp enterprise architecture to choose a business-IT-aligned organisation. The study examines healthcare organisations’ EA adoption issues before, during, and after. To fix these difficulties and streamline the process. The report recommends healthcare EA implementation. To improve implementation, these principles streamline processes, stimulate collaboration, and improve patient outcomes. EA boosts healthcare performance, but IT-human interaction must be reviewed. The healthcare businesses must overcome challenges to deploy smoothly and achieve great results.

The aim of the study is to identify the factors that accurately predict academic performance and the contribution that each factor makes to overall academic success. The collected dataset consists of 21 attributes for 97 students in one of the public universities in Malaysia. Several data pre-processing and feature selection tasks have been performed to ensure the quality of the data. A regression model is developed to predict the cumulative grade point average (CGPA) using the selected variables. The result discovered that variables such as gender, absence rate and GPA affect the CGPA of the students. Model evaluation also proves that it can be utilized to predict the CGPA of students. This study is expected to help educational institutions, particularly academic advisors, in identifying students who are at risk of failure. Thus, an early effective program can assist students in improving their academic performance.

Transgenerational entrepreneurship entails the proactive development of a self-sustaining family-owned business that can be passed down through multiple generations, whereas family business succession refers to the transfer of business ownership and management to the next generation through inheritance. Choosing a successor in small and medium-sized enterprises requires a more comprehensive approach than in larger corporate firms due to fewer potential successors. The purpose of this research is to identify the challenges and factors that incumbents must consider when selecting a successor and implementing a succession plan. The study identifies challenges such as the involvement of an informal family member in the selection process, internal conflicts between the family and the business, the incumbent’s attachment to their leadership legacy, and the successor's readiness to assume responsibility. When it comes to succession planning, education, competence, demographic factors, relationships with family members and incumbents, experience, integrity, birth order, and primogeniture are all important factors to consider.

Higher education institutions are facing challenges to prepare students for the rapidly changing world of work in the era of Industry 4.0. At the same time, these institutions have access to unprecedented amounts of data that can help them better understand student needs and outcomes, as well as the effectiveness of teaching and learning strategies. This paper used a rapid scoping review to explore the current landscape of data utilization in higher education, specifically in terms of how data is being used to inform decision-making and improve outcomes. The findings suggest that data utilization in higher education requires more attention from researchers and practitioners to fully realize its potential. This paper concludes by discussing the implications of the findings and suggesting areas for future research.

Cardiovascular diseases are among the killer diseases in the world. The diseases caused a lot of death, and disabilities, and contributed to high costs of treatment. Early treatment of cardiovascular diseases by knowing the risk factors for disease susceptibility will facilitate treatment and healing. This study aims to develop a cardiovascular disease prediction model using the Sugeno's adaptive neuro-fuzzy inference system (ANFIS). The grid partition and sub-clustering were used in the developed ANFIS. The data set comprises clinical data from UCI Global Data. The result analysis of the prediction model generated Fuzzy Inference System (FIS) uses grid partition with optimization backpropagation, grid partition with optimization hybrid, sub-clustering with optimization backpropagation, sub-clustering with optimization hybrid values of root mean square error are 0.7059, 0.2579, 0.7071, and 0.2576, respectively.

The island of Bali is very famous in foreign countries for its tourism. The Balinese economy depends on tourism. However, the global health disaster of the COVID-19 pandemic has devastated Bali's economy. To prevent the pandemic's spread and impact, the government is collaborating with Customary Villages and Customary Banjar. The Banjar government system is believed to preserve tradition and culture. The existence of Banjar can be a driving force for a community-based economy. Developing a smart concept can be a solution to optimize the strength of existing indigenous people with the support of technology. Especially for Bali, it can be developed into a Smart Banjar. ICT adoption is a component of the Smart Banjar. Qualitative research with interviews was conducted to answer the phenomenon. The findings in this study state that it is necessary to evaluate the level of ICT adoption. Furthermore, it can develop into an appropriate future ICT adoption. The community's proficiency in ICT literacy must be programmed continuously. Banjar's involvement in microenterprise digitization and synergy with community-based microfinance can achieve community welfare.

There is a potential risk of theft and trespassing occurring on industrial premises because various business operations are handled. Several methods are adopted to deal with this issue, such as using the services of security guards and installing security alarms for security purposes. However, it involves a less cost-effective electronic system. Therefore, this security monitoring system that uses lasers was proposed for the purpose of dealing with this issue. Laser light is used to cover large areas because lasers can travel long distances without scattering effects, have sufficient energy to trigger the security system in a small zone and the existence of laser light is not clearly noticeable compared to the cable connection concept. Thus, intruders cannot detect that there is a security alarm installed in the area. This project aims to design and develop a security alarm laser-triggered system using an Arduino UNO microcontroller and IoT technology interface with a camera that could monitor the movement through camera footage on mobile apps. The main components used are an Arduino to execute commands and a web camera that displays a video when the intruder crosses the laser. The proposed project is beneficial for recent security issues regarding high-rated intruders on industrial premises.

The efficient prediction of short-term photovoltaic (PV) energy output is a pressing challenge in the renewable energy sector. Accurate PV energy forecasts are pivotal for optimizing grid integration, minimizing energy wastage, and reducing operational costs in solar power plants. This study addresses these challenges by leveraging machine learning (ML) techniques and comparing the performance of three ML models, namely linear regression, random forest, and gradient boosting, with the objective of identifying the most effective model for short-term PV energy prediction during the given timeframe. The study utilizes 5 MWp PV power plant data collected in Melaka, Malaysia, over a daily period from 1st September 2013 until 31st January 2014 providing a robust dataset for training and testing the models. The primary evaluation metrics used in this analysis are the root mean squared error (RMSE), R-squared (R²) score, and the mean absolute percentage error (MAPE). The findings reveal that the gradient boosting (GB) model outperforms both linear regression (LR) and random forest (RF) in terms of predictive accuracy; RMSE (1380.13), R-squared (R²) (0.8), and MAPE (4.3%). This suggests that GB is the most suitable ML model for accurate short-term PV energy prediction in the context of Melaka’s PV power plant data.

The aim of this study is to increase comprehension and provide a systematic review of insider threat prediction techniques explored by previous researchers. The advantages and disadvantages of machine learning techniques, statistical techniques, hybrid techniques, and knowledge-based techniques are highlighted. In addition, prospective work obstacles and suggestions have been discussed. This study examined insider threat prediction trends in scholarly articles published between 2007 and 2022. Researchers, practitioners, and policymakers who are interested in predicting insider threats should find this study beneficial.