No abstract

Gold price is important to a country’s economy as it can be used as a hedge against inflation especially during financial turmoil. Besides, the gold price also has an impact on the stock market price. As an investor, to make a good investment plan, information regarding the fluctuation price of gold is necessary to minimize the risk. Therefore, this study proposes to compare two of the forecasting models, namely Holt's Double Exponential Smoothing and Fuzzy Time Series Markov Chain to forecast the price of gold. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are used to determine a better forecasting model with smaller error. Initially, the data price of gold is analysed by using Durbin Watson Test to check the suitability of the data for time series analysis. The finding of this study shows that Fuzzy Time Series Markov Chain is more accurate in predicting gold price as compared to Holt’s Double Exponential Smoothing because it produces smaller values of RMSE and MAPE.

The advancements in technology and high-speed networks give advantages for entrepreneurs to promote their products and services in various forms of posting through social media platforms such as Facebook, Twitter, Instagram and many more. The effectiveness of the video posting in terms of the virality of the video, the time the video reaches the maximum number of viewers and the flow of video spread are very important inputs for the marketers. Therefore, this preliminary study was designed to differentiate the effectiveness of two selected video posting on Facebook promoting two different popular products among women: shawls and slimming product. Susceptible-Infected-Recovered (SIR) models with demography and without demography was used in analysing the data since the nature of the dissemination of the video is similar to the spread of virus. The variables used in the analysis were the number of Facebook users who exposed to the video (Susceptible), received and shared the video (Infected) and stop sharing the video (Recovered). The finding shows the video promoting the shawl is more viral (R0  > 1) as compared to the video promoting the slimming product (R0 < 1) based on both SIR Model. Although the earliest number of users who received the shawl video was lower but the number of users who received and shared that videos increased tremendously until it reached the maximum number of 19.6 million viewers in 2 days and after that the number was slowly decreased. For slimming product, it started with higher number of viewers, but reached the maximum number of viewers of 10.3 million in 8 days and later the number was gradually decreased. Further study should be done because there are a lot of possibilities or factors that contribute to these findings.

Crude oil is one of the important commodities to Malaysia. As a producer and exporter of oil and gas, Malaysia has gained high Gross Revenue from this sector. Crude oil is the global commodity and highly demanded. Therefore, major price changes on the commodity have a significant influence on world economy. Market sentiment, demand, and supply are some elements directly influencing the oil prices. Since crude oil is the backbone of businesses and is extremely important to the economy, it is essential to study the price of crude oil for future planning purposes. For that reason, this study proposes the use of the Fuzzy Time Series Cheng to predict crude oil price in Malaysia. In this study, Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are used to evaluate the forecast performance. The result shows that Fuzzy Time Series Cheng is able to produce a good result in forecasting since the analyses shows that the low value of RMSE and MAPE (less than 10 percent). Although this is the fundamental study but the finding may assist many sectors in Malaysia, such as governments, enterprises, investors, and businesses to produce a better economic planning in the future especially after the pandemic covid-19 phase.

Twitter has been prominently used during the electoral campaigns. Twitter helps the politicians to spread and share their political agenda. Through Twitter, every information is accessible to anyone and anybody around the world in keeping up with the latest information like a manifesto and others that relate to political issues. However, the popularity of political parties and leaders that could raise the winning opportunities in the election is not fully understood. This study aims to analyse the spreading of information during Malaysia’s 14th General Election via Twitter. This study employed an epidemiological model which is a system of differential equations known as Susceptible-Infected-Recovered (SIR). The model involves three state variables, which are active Twitter users (S), the transmission node (I) and immune Twitter users (R). The Twitter accounts owned by two political parties and two political leaders have been followed before and after the election. The influence of contact rate between active Twitter users and the transmission node of Twitter users on Malaysia’s 14th General Election was analysed. The results showed that the contact rate between active Twitter users and the transmission node of the Twitter users has a significant influence on Malaysia’s 14th General Election.