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The nonstationary in time series data may be caused by the existence of intervention, outliers, and heteroscedastic effects. The outliers can represent an intervention so that it creates a heteroscedastic process. This research investigates the involvements of these three factors in time series data modelling. It is also reviewed how long the effects of the intervention and outliersfactors will last. The weekly IDR-USD exchange rate in period of May 2015 to April 2020 be evaluated. It is obtained that ARIMA model with the intervention factor gives the best re-estimation result, with smallest average of errors squared. Meanwhile for prediction, the heteroscedastic effect combined with outlier factors gives better results with the lowest percentage of errors. One of the phenomenal interventions in this data is the Covid-19 pandemic, which was started in Indonesia on March 2020. It is found that the effect of the intervention lasts less than five months and the prediction shows that the volatility of IDR-USD exchange rate starts to decline. This shows the stability of the process is starting to be maintained.

COVID-19, CoronaVirus Disease – 2019, belongs to the genus of Coronaviridae. COVID-19 is no longer pandemic but rather endemic with the number of deaths around the world of more than 3,166,516 cases. This reality has placed a massive burden on limited healthcare systems. Thus, many researchers try to develop a prediction model to further understand this phenomenon. One of the recent methods used is machine learning models that learn from the historical data and make predictions about the events. These data mining techniques have been used to predict the number of confirmed cases of COVID-19. This paper investigated the variability of the effect size on the correlation performance of machine learning models in predicting confirmed cases of COVID-19 using meta-analysis. It explored the correlation between actual and predicted COVID-19 cases from different Neural Network machine learning models by means of estimated variance, chi-square heterogeneity (Q), heterogeneity index (I2) and random effect model. The results gave a good summary effect of 95% confidence interval. Based on chi-square heterogeneity (Q) and heterogeneity index (I2), it was found that the correlations were heterogeneous among the studies. The 95% confidence interval of effect summary also supported the difference in correlation between actual and predicted number of confirmed COVID-19 cases among the studies. There was no evidence of publication bias based on funnel plot and Egger and Begg’s test. Hence, findings from this study provide evidence of good prediction performance from the Neural Network model based on a combination of studies that can later serve in the prediction of COVID-19 confirmed cases.

Air pollution is a well-known issue for all countries, including Malaysia. It has been stated that particulate matter that less than 2.5mm known as PM2.5 has a greater effect on health as the smaller particulate size can penetrate deep into the respiratory system and affect the cardiovascular system significantly. Therefore, it is necessary to estimate the concentration of PM2.5 for haze precautions. This study characterizes the pattern of PM2.5 concentrations involving seven stations including Alor Setar, Shah Alam, Pasir Gudang, Ipoh, Kuantan, Kuala Terengganu and Miri with seven indicator parameters (Carbon Monoxide, Ozone, Sulphur Dioxide, Nitrogen Dioxide, Humidity, Temperature and Wind Speed). PM2.5 concentrations were predicted for each station using Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). Descriptive and trend analysis using Mann-Kandell Trend analysis was used to describe the haze characteristics and identify significant trends in the haze selected locations in Malaysia. MLR and ANN were fitted for the data. The performance of both prediction models was compared based on R2 and Mean Square Error (MSE). The results show ANN performed better than MLR with a high value of coefficient determination (R2) and low error measure. The ANN model was used to predict the occurrence of haze for the next day in the Air Quality Index (API).

According to the World Health Organization (WHO), approximately 2 billion people worldwide use drinking water sources that are contaminated with faeces. This is a serious issue since contaminated water may lead to certain waterborne diseases such as cholera, hepatitis A, dysentery, jaundice, and typhoid fever. Therefore, many researchers around the world are interested in studying the water quality. One of the most commonly used approaches is by using machine learning. Machine learning approach has grabbed the interest of many researchers since the last several years due to its power to compute complicated mathematical computations on big data analysis. Therefore, this study explored the correlation between different water quality parameters and Water Quality Index (WQI) in water quality studies that used machine learning by using a meta-analysis approach. This study used estimated variance, heterogeneity index, Chi-squared heterogeneity test and the random effects model. Based on the selected articles, pH, dissolved oxygen (DO) and biochemical oxygen demand (BOD) are the parameters commonly used in water quality studies which use a machine learning approach. This study found that pH is the best chemical factor which greatly affects the Water Quality Index since it has the highest mean correlation and lowest estimated variance due to sampling error. The result showed that the correlation between pH and WQI are heterogeneous across studies based on the Chi-squared of heterogeneity, Q and heterogeneity index, I2 value. The 95% confidence interval of effect summary supports the findings that the correlation of pH is different among the studies. This study also found that there is no evidence of publication bias using Egger and Begg’s test. Therefore, in order to ensure good water quality supply, the local authorities and government agencies should give more attention to this parameter since pH of water plays an important role in determining the water quality status.

The issue of graduates’ employability has become a major concern in many countries as the number of graduates keep increasing over the years. Fresh graduates are experiencing difficulties in finding meaningful employment as the growth in hiring has slowed in the recent period. With the impact of fourth industrial revolution, today graduates face a world transformed by technology. The role of universities in higher education become more challenging in shaping more digital technology graduates with the right skills and knowledge. Problems can be seen at both management level of higher education in producing graduates who can meet the needs of industry while industry is also facing the problem of finding skilled graduates who suit their needs. To examine the work skills demanded from Industry of Malaysian, this study proposed a text mining approach from 465 unstructured data of analyst job vacancies in classifying job titles and extracting vacancy and skills from job descriptions. The methodology involved are text retrieval, text pre-processing, word cloud corpus generation and text clustering. The findings indicate that the selected job posts emphasize more on vacancies information rather than skills needed which further shows the gap existence between the skills demand and supply.

This preliminary study aims to investigate the awareness of the adoption of cloud computing among the academicians in tertiary education in Malaysia. Besides, this study also want to explore the possible challenges faced by the academician while adopting this new technology. The pilot study was done on 40 lecturers in Universiti Teknologi MARA Kampus Kota Bharu (UiTMKB) by using self administered questionnaire. The results found that almost half (40 percent) were not aware on the existing of cloud computing in teaching and learning (T&L) process. The challenges confronting the adoption of cloud computing are data insecurity, data insecurity, unsolicited advertisement, lock-in, reluctance to eliminate staff positions, privacy concerns, reliability challenge, regulatory compliance concerns/user control and institutional culture/resistance to change in technology. This possible challenges can be factorized in two major factors which were security and dependency factor and user control and mentality factor.