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Zuraidah Derasit
Universiti Teknologi MARA Shah Alam

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Determining Hotspots of Road Accidents using Spatial Analysis S.Sarifah Radiah Shariff; Hamdan Abdul Maad; Nursyaza Narsuha Abdul Halim; Zuraidah Derasit
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 1: January 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i1.pp146-151

Abstract

Road accidents continuously become a major problem in Malaysia and consequently cause loss of life or property. Due to that, many road accident data have been collected by highway concessionaries or build–operate–transfer operating companies in the country meant for coming up with proper counter measures. Several analyses can be done on the accumulated data in order to improve road safety. In this study the reported road accidents cases in North South Expressway (NSE) from Sungai Petani to Bukit Lanjan during 2011 to 2014 period is analyzed. The aim is to determine whether the pattern is clustered at certain area and to identify spatial pattern of hot spots across this longest controlled-access expressway in Malaysia as hotspot represents the location of the road which is considered high risk and the probability of traffic accidents in relation to the level of risk in the surrounding areas. As no methodology for identifying hotspot has been agreed globally yet; hence this study helped determining the suitable principles and techniques for determination of the hotspot on Malaysian highways. Two spatial analysis techniques were applied, Nearest Neighborhood Hierarchical (NNH) Clustering and Spatial Temporal Clustering, using CrimeStat® and visualizing in ArcGIS™ software to calculate the concentration of the incidents and the results are compared based on their accuracies. Results identified several hotspots and showed that they varied in number and locations, depending on their parameter values. Further analysis on selected hot spot location showed that Spatial Temporal Clustering (STAC) has a higher accuracy index compared to Nearest Neighbor Hierarchical Clustering (NNH). Several recommendations on counter measures have also been proposed based on the details results.
Fuzzy time series forecasting in determining inventory policy for a small medium enterprise (SME) company S.Sarifah Radiah Shariff; Nurul Nadiah Abdul Halim; Siti Meriam Zahari; Zuraidah Derasit
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i3.pp1654-1660

Abstract

Fuzzy time series models have been widely used to handle forecasting problems, such as forecasting consumer demand and production volume. It is of greater benefits if we have good forecasting accuracy rates especially in managing inventory in a Small and Medium Enterprise (SME) company.  This study focuses on multiple products with single production line.  The aims of this study are to propose the appropriate the forecasting method for the products, to develop new inventory policy that minimizes the total inventory cost for the company.  Simple forecasting methods like trend line, three month moving average (MA (3) and fuzzy time series forecasting are used in this study.  The result shows that fuzzy time series forecasting model is suitable to be used in forecasting future demand for all products.  The proposed inventory policy is based on the number of cycle per year and the number of production for each product has helped the company to minimize total inventory cost and schedule the production process accordingly.  The proposed inventory policy resulted in lower total inventory cost when compared to current practice. 
Co-Authors Hamdan Abdul Maad Nursyaza Narsuha Abdul Halim Nurul Nadiah Abdul Halim S.Sarifah Radiah Shariff S.Sarifah Radiah Shariff Siti Meriam Zahari