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Haze alarm visual map (HazeViz): an intelligent haze forecaster Mohd Said Syukri Morsid; Syeril Azira Jamaluddin; Nur Azmina Hood; Norshahida Shaadan; Yap Bee Wah; Muthukkaruppan Annamalai
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

The haze problem has intensified in recent years. The particulate matter of less than 10 microns in size, PM10 is the dominant air pollutant during haze. In this paper, we present the development of HazeViz, a Haze Alarm Visual Map forecaster, which is based on PM10. The intelligent web application allows users to visualize the pattern of PM10 in a region, forecasts PM10 value and alarms bad haze condition. HazeViz was developed using HTML, Java Script, PHP, MySQL, R Programming and Fusionex Giant. The SARIMA statistical forecasting models that underlie the application were developed using R. The PM10 trend analysis, and the consequential map and chart visualizations were implemented on the Fusionex GIANT Big Data Analytics platform. HazeViz was developed in the context of the Klang Valley, our case study. The dataset was obtained from Department of Environment Malaysia, which contains a total of 157,680 hourly PM10 data for six stations in Klang Valley, for the years 2013 to 2015. The SARIMA models were developed using maximum daily PM10 data for 2013 and 2014, and the 2015 data was used to validate the model. The fitting models were determined based on the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). While the selected models were implemented in HazeViz and successfully deployed on the web, the results show that the selected models have MAPE ranging between 35 percent and 45 percent, which implies that the models are still far from robust. Future work can consider augmented SARIMA models that can yield improved results.

Haze alarm visual map (HazeViz): an intelligent haze forecaster Mohd Said Syukri Morsid; Syeril Azira Jamaluddin; Nur Azmina Hood; Norshahida Shaadan; Yap Bee Wah; Muthukkaruppan Annamalai
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

The haze problem has intensified in recent years. The particulate matter of less than 10 microns in size, PM10 is the dominant air pollutant during haze. In this paper, we present the development of HazeViz, a Haze Alarm Visual Map forecaster, which is based on PM10. The intelligent web application allows users to visualize the pattern of PM10 in a region, forecasts PM10 value and alarms bad haze condition. HazeViz was developed using HTML, Java Script, PHP, MySQL, R Programming and Fusionex Giant. The SARIMA statistical forecasting models that underlie the application were developed using R. The PM10 trend analysis, and the consequential map and chart visualizations were implemented on the Fusionex GIANT Big Data Analytics platform. HazeViz was developed in the context of the Klang Valley, our case study. The dataset was obtained from Department of Environment Malaysia, which contains a total of 157,680 hourly PM10 data for six stations in Klang Valley, for the years 2013 to 2015. The SARIMA models were developed using maximum daily PM10 data for 2013 and 2014, and the 2015 data was used to validate the model. The fitting models were determined based on the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). While the selected models were implemented in HazeViz and successfully deployed on the web, the results show that the selected models have MAPE ranging between 35 percent and 45 percent, which implies that the models are still far from robust. Future work can consider augmented SARIMA models that can yield improved results.

Haze alarm visual map (HazeViz): an intelligent haze forecaster Mohd Said Syukri Morsid; Syeril Azira Jamaluddin; Nur Azmina Hood; Norshahida Shaadan; Yap Bee Wah; Muthukkaruppan Annamalai
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

The haze problem has intensified in recent years. The particulate matter of less than 10 microns in size, PM10 is the dominant air pollutant during haze. In this paper, we present the development of HazeViz, a Haze Alarm Visual Map forecaster, which is based on PM10. The intelligent web application allows users to visualize the pattern of PM10 in a region, forecasts PM10 value and alarms bad haze condition. HazeViz was developed using HTML, Java Script, PHP, MySQL, R Programming and Fusionex Giant. The SARIMA statistical forecasting models that underlie the application were developed using R. The PM10 trend analysis, and the consequential map and chart visualizations were implemented on the Fusionex GIANT Big Data Analytics platform. HazeViz was developed in the context of the Klang Valley, our case study. The dataset was obtained from Department of Environment Malaysia, which contains a total of 157,680 hourly PM10 data for six stations in Klang Valley, for the years 2013 to 2015. The SARIMA models were developed using maximum daily PM10 data for 2013 and 2014, and the 2015 data was used to validate the model. The fitting models were determined based on the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). While the selected models were implemented in HazeViz and successfully deployed on the web, the results show that the selected models have MAPE ranging between 35 percent and 45 percent, which implies that the models are still far from robust. Future work can consider augmented SARIMA models that can yield improved results.

Bridging the Expectation Gap of the Institutional Donors and Charity Management: Preliminary Insights Evidence Saunah Zainon; Ruhaya Atan; Yap Bee Wah
Journal of Accounting, Business and Management (JABM) Vol 19 No 1 (2012): April
Publisher : STIE Malangkucecwara

Show Abstract | Download Original | Original Source | Check in Google Scholar

It is commonly believed that donors would donate more to charity if they were assured that the funds will be utilised properly and not wasted. Evidence from previous literature also shows that the donors tend to give more support and contribution to the charity if they were equipped with charity information. As far as the charity sector is concerned, the core competency of the charity sector is to build strong relationships with donors. Their ability to build this relationship will contribute to a strong sustainable income for the charity to operate. In Malaysia, there is no avenue for the stakeholders, particularly the donors as the key stakeholders to obtain information on charity especially with regard to the financial information. This study seeks to develop insights into institutional donors? expectations and the information that charities offers, bridging the expectation gap between the donors and the charity management. Insights evidence, both from the donors and the charity management were provided in order to bridge the gap between the donors? expectations and the charity management?s offers of information, so as the result can be used for better charity reporting in the future. The finding shows that both financial and non-financial information are seen as important by the institutional donors but not the major criterion concerned by the charity management. This study tries to fill the gap.

A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets Muhamad Hasbullah Bin Mohd Razali; Rizauddin Bin Saian; Yap Bee Wah; Ku Ruhana Ku-Mahamud
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp412-419

Ant-tree-miner (ATM) has an advantage over the conventional decision tree algorithm in terms of feature selection. However, real world applications commonly involved imbalanced class problem where the classes have different importance. This condition impeded the entropy-based heuristic of existing ATM algorithm to develop effective decision boundaries due to its biasness towards the dominant class. Consequently, the induced decision trees are dominated by the majority class which lack in predictive ability on the rare class. This study proposed an enhanced algorithm called hellinger-ant-tree-miner (HATM) which is inspired by ant colony optimization (ACO) metaheuristic for imbalanced learning using decision tree classification algorithm. The proposed algorithm was compared to the existing algorithm, ATM in nine (9) publicly available imbalanced data sets. Simulation study reveals the superiority of HATM when the sample size increases with skewed class (Imbalanced Ratio < 50%). Experimental results demonstrate the performance of the existing algorithm measured by BACC has been improved due to the class skew-insensitiveness of hellinger distance. The statistical significance test shows that HATM has higher mean BACC score than ATM.

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