Search

Traffic congestion has become a pervasive problem in the world including Malaysia for the past few years while present indication shows that it is expected to get worse. The phenomenon of being stuck in traffic congestion for an extension period also causes stress and tiredness, Due to this, there is no denying that this problem is significantly contributes to negative social, economic, and environmental impacts. This study aims to reduce traffic congestion at Jalan Raja Ashman Shah, Ipoh by implementing an intelligent fuzzy logic traffic light system. The system aims to reduce waiting times and improve traffic flow by automatically adjusting green light durations based on real-time traffic conditions. Data on the number of vehicles and queue lengths during peak hours were collected to compare congestion levels before and after the intervention. The results of this study demonstrate the effectiveness of deploying an intelligent traffic light system with fuzzy logic in minimizing traffic congestion and reducing waiting times. By dynamically adjusting green light duration based on real-time traffic conditions, the system optimized traffic flow and improved overall congestion. A comparison of congestion levels before and after the introduction showed a significant reduction in congestion and an improvement in traffic flow. The adaptability of the intelligent traffic light system makes better use of road capacity, reducing waiting times and minimizing congestion. As a result, we found a significant reduction in waiting time compared to the previous static system. Consequently, this study gives great impact to Ipoh citizens if the fuzzy logic method is implemented in traffic light system. Due to this, improving the transportation system and the quality of life for road users.

Many workplaces encounter complex problems in preparing an optimal work scheduling to meet the 24 hours work demand especially in shift working hours. The schedule needs to consider many constraints and multi objectives at the same time. A mathematical model such as Goal programming is able to cater this kind of problems. Thus, this study was designed to provide a systematic and optimal schedule for police officers at Criminal Unit, IPD Kuala Muda, Kedah. This study is aimed to formulate the best model for the shift rotating schedule of the police officers and to find the best way to optimize the police scheduling related to the limitations, requirements of the police station and the preferences of the police. Lingo software is used to run the model. However, only one out of three goals set for the study was achieved. The new schedule obtained shows that all police officers have the same number of working days, which is 21 days in the 28-day planning period. The new schedule produced is better than the previous manual schedule since it takes less time to prepare it without neglecting the constraints involved. To improve efficiency and flexibility on the generated schedules, it is recommended to use other methods such as hybrid swarm-based optimization and many new limitations and preferences should be also considered in the analysis.

The effects of Covid-19 are not only in terms of health, but many things arise along with its existence. One of them is poverty. Due to this, Universiti Teknologi MARA(UiTM) has taken the initiative to offer several financial aids to the affected students. However, the students must fill in the application form to obtain the aid. They need to provide the supporting documents such as parents pay slip, information on family dependent and so on, which will be inspected, graded and selected by the committee to determine the qualification for the financial aids. Due to this, it will be a long process to select the rightful applicants, which involves a significant length of time as it consisted of hundreds of applicants. Hence, the aim of this study is to select and rank the most eligible candidates among UiTM Perlis students with fairness, fast and accurate manner. In this study, the Fuzzy Technique for Others Preference by Similarity to Ideal Solution (TOPSIS) method was used to solve the problem. The model was run using Microsoft Excel. The selection of UiTM Perlis students were based on a set of criteria that had been predetermined which includes family income, occupation and the number of dependent in the family. The findings of the study showed that from 35 samples of UiTM Perlis students, the highest-ranking was student 35 (S35) with 1.0000 relative closeness. Meanwhile, the lowest ranking was student 30 (S30) with 0.6478 relative closeness. It is also shown that all samples are qualified to receive financial aid due to sufficient allocation amount provided by UiTM Perlis. As a result, the mistakes during the selection process can be reduced by using this method compared to manual selection. Thus, making it easier and faster to channel the aid. All in all, it has been demonstrated that this method is very suitable to be used in this study.

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.

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.