Zalina Mohd Ali

Importance-performance analysis and Customer Satisfaction Index for express bus services

A study was carried out to investigate customer satisfaction level towards the public transportation services provided by Consortium Express Bus Company. 200 respondents consisting of 65 men and 135 women participated in the study survey. The survey was conducted in three bus stations in Kuala Lumpur and Selangor areas, namely Pudu Bus Station, Kajang Bus Station, and Putra Bus Station. Importance-Performance Analysis was used to identify customer perceived factors that needed to be prioritized for improvement. From this analysis, drivers and counter staffs were categorized as factors which needed to be given consideration. Customers were satisfied with the comfort, security, and ticket prices; thus the kind of services given must be maintained. Customer Satisfaction Index was used to analyze customer satisfaction level and was found to be 64.1%. This indicates that overall, the bus express services were at a moderate level of satisfaction.

Temporal Water Quality Assessment of Langat River from 1995-2006

Water quality is generally described according to biological, chemical and physical properties (Coke et al 2005). Based on these properties, the quality of water can be expressed via a numerical index (i.e. Water Quality Index, WQI) by combining measurements of selected water quality variables. The index is important in evaluating the water quality of different sources and in observing the changes in the water quality as a function of time and other influencing factors (Sarkar and Abbasi 2006). The time when samples are taken is one of the contributing factors that can influence the concentration of a particular water quality variable (Coke et al 2005). Thus, temporal assessment is a good indication in determining the presence or absence of trend and seasonality to which water quality is responding to changes in the catchment and time.

Robust principal component analysis in water quality index development

Some statistical procedures already available in literature are employed in developing the water quality index, WQI. The nature of complexity and interdependency that occur in physical and chemical processes of water could be easier explained if statistical approaches were applied to water quality indexing. The most popular statistical method used in developing WQI is the principal component analysis (PCA). In literature, the WQI development based on the classical PCA mostly used water quality data that have been transformed and normalized. Outliers may be considered in or eliminated from the analysis. However, the classical mean and sample covariance matrix used in classical PCA methodology is not reliable if the outliers exist in the data. Since the presence of outliers may affect the computation of the principal component, robust principal component analysis, RPCA should be used. Focusing in Langat River, the RPCA-WQI was introduced for the first time in this study to re-calculate the DOE-WQI. Results show that the RPCA-WQI is capable to capture similar distribution in the existing DOE-WQI.

Water quality study at Skudai River, 2002 – 2006

Assessing the level of river water quality periodically is important in ensuring that the river water is safe for consumption. This study investigates the level of water quality at Skudai River from year 2002 to 2006. Twenty parameters were identified and used in this analysis. These twenty parameters are recorded at nine monitoring stations located along the Skudai River. Multivariate statistical techniques i.e. factor and cluster analysis were used in this analysis. From this analysis we managed to group the monitoring stations into three categories: seriously polluted, moderately polluted and clean.

Forecasting Malaysian foreign exchange rate using artificial neural network and ARIMA time series

In view of today’s global economy, countries around the world are getting more connected and linked to each other via trade, technically known as import and export activities. To get ahead in the w...

Bayesian model-based approach for developing a river water quality index

Six main pollutants have been previously identified by expert opinion to determine river condition in Malaysia. The pollutants were Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended Solid (SS), potential of Hydrogen (pH) and Ammonia (AN). The selected variables together with the respective weights have been applied to calculate the water quality index of all rivers in Malaysia. However, the relative weights established in DOE-WQI formula are subjective in nature and not unanimously agreed upon, as indicated by different weight being proposed for the same variables by various panels of experts. Focusing on the Langat River, a Bayesian model-based approach was introduced for the first time in this study to obtain new objective relative weights. The new weights used in WQI calculation are shown to be capable of capturing similar distributions in water quality compared with the existing DOE-WQI.

Discriminant Analysis of Water Quality Data in Langat River

River monitoring processes usually generate large databases of water quality variables. These water quality variables are important to identify the status of the river. Due to multidimensionality of complex characteristics in river water, meaningful information from a large database can be extracted by using multivariate statistical methods, i.e., discriminant analysis (DA). Appropriate univariate transformation and data screening for multivariate outlier’s detection were considered. The DA approach used in this study gave better information on river water quality, especially concerning the contribution of the variables in discriminating between the three spatial areas in Langat River.

The selection of the Bayesian coincident-index models using model comparison criterion with application in Langat river water quality data

In this study, three types of Bayesian coincident-index models were designed based on modification from the so-called Stock-Watson (S-W) index, i.e. the established economic coincident-index. The S-W index approach is commonly used to estimate parameters using sufficient time series data in composite index. However, the S-W approach in small time series data sets, irregular observations and unbalanced data across time is not well-studied. Essentially, all the problem situations are easily handled under the proposed class of Bayesian models. Therefore, we were motivated to design a new Bayesian S-W coincident-index with application in river water quality index. The river water quality data has been chosen due to the availability of small, unbalanced data sets and irregular observations at particular time in selected sampling sites. We tested all the new algorithms which are based on the three coincident-index model i.e. model with no lag, model with first order lag in the water quality variables and model with a lag for unobserved water quality index. We used Bayesian model comparison criterion to choose the best coincident-index model. Deviance Information Criteria (DIC) was performed as a criterion for selecting models since its application to a variety of data-diagnostic models was not well-discussed. Sensitivity model analyses for all models were also reported. The results showed that the model with σ prior 2 was the most appropriate for the Langat river water quality data in the selected sampling sites. The results also showed that that the third model with σ prior 2 for all sampling sites was the most adequate.