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A customer trouble ticketing system (CTT) is an organization's tool to track the detection, reporting, and resolution of tickets submitted by customers. It also comprises a summary of the issue reported, the status of the ticket, the incident information, and the approach that was previously utilized to resolve the problems. The technician's skill set and experience rely solely on completing the task without the right direction on which area to focus on first. As a result of this manual classification of a trouble ticket, it will be necessary to build methodologies for predicting future resolution codes. The research for this report is mainly focused on one of the telco companies in Malaysia. This study result assists the telco engineer, and the specialists resolve each issue in a very short amount of time. Additionally, the classification of the trouble ticket resolution code method used in this study will indicate the characteristics of each issue that is being investigated. The relationship between events is feasible to discover by exploring the root cause. It is critical to establish a link between recent events and events in the previous. Because of current data mining limitations, the study needs to be more comprehensive. Data processing methods are being implemented within big data platforms to overcome the limitation of data scalability, enhance classification accuracy, and increase computation speed. The research work will continue to progress in the direction of big data centricity. Some of the most effective approaches for big data integration and machine learning will be discussed in this paper. Throughout the experiment, any problems will be explained, as well as the solutions to each situation. A wide range of research subjects will be discussed, including construction classification models for trouble tickets. To achieve reasonable accuracy, a few customized transformations are required. The data set's custom parameter optimization process will further increase the classification trouble ticket's efficiency. However, greater processing capacity is necessitated to use multiple parallel classifiers such as Bayes, Decision-Tree, and Rule-Based with help of bigdata framewrks such as Spark. According to the study, an increase of 8% classification performance substantially influences service recovery time, customer satisfaction, and preventative maintenance expenses in the telco industry. [ABSTRACT FROM AUTHOR]

Business Intelligence (BI) encompasses a suite of strategies, processes, applications, and technologies that convert raw data into valuable insights for business analysis. By aiding in informed decision-making, BI empowers organizations to enhance their competitive edge and streamline internal operations. It not only uncovers new prospects but also refines customer service and bolsters profitability. Through historical, real-time, and predictive perspectives on business operations, BI facilitates well-informed choices. Leveraging BI tools, businesses dissect consumer behavior, detect trends, and devise potent marketing tactics. BI amplifies comprehension of customers, rivals, and markets, leading to astute decisions that foster profitability. This study amassed data via a survey of 162 IT managers in multinational corporations within Malaysia. Employing partial least squares (PLS) through SmartPLS software, the analysis reveals that BI, along with its insights, contributes to effective management practices. Notably, the information requisites may shift based on the balance between uncertainty and ambiguity in an organization's practices. Nevertheless, the research gap remains concerning the relationship between BI, its significance, and business performance. [ABSTRACT FROM AUTHOR]

Air is the most crucial element for the survival of life on Earth. The air we breathe has a profound effect on our ecosystem biodiversity. Consequently, it is always prudent to monitor the air quality in our environment. There are few ways can be done in predicting the air pollution index (API) like data mining. Therefore, this study aimed to evaluate three types of support vector regression (linear, SVR, libSVR) in predicting the air pollutant concentration and identify the best model. This study also would like to calculate the API by using the proposed model. The secondary daily data is used in this study from year 2002 to 2020 from the Department of Environment (DoE) Malaysia which located at Petaling Jaya monitoring station. There are six major pollutants that have been focusing in this work like PM10, PM2.5, SO2, NO2, CO, and O3. The root means square error (RMSE), mean absolute error (MAE) and relative error (RE) were used to evaluate the performance of the regression models. Experimental results showed that the best model is linear SVR with average of RMSE = 5.548, MAE = 3.490, and RE = 27.98% because had the lowest total rank value of RMSE, MAE, and RE for five air pollutants (PM10, PM2.5, SO2, CO, O3) in this study. Unlikely for NO2, the best model is support vector regression (SVR) with RMSE = 0.007, MAE = 0.006, and RE = 20.75% in predicting the air pollutant concentration. This work also illustrates that combining data mining with air pollutants prediction is an efficient and convenient way to solve some related environment problems. The best model has the potential to be applied as an early warning system to inform local authorities about the air quality and can reliably predict the daily air pollution events over three consecutive days. Besides, good air quality plays a significant role in supporting biodiversity and maintaning healthy ecosystems. [ABSTRACT FROM AUTHOR]

Determining the true reasons for happiness that exist in different social groups is important, especially in the case of those groups where poverty strongly dominates people's well‐being and happiness. To understand the factors that determine happiness among poor communities, this study collected data in the form of 1,793 responses from low‐income communities, who constitute 40% of Malaysia's workforce. To ascertain the feelings and emotional responses of the target group, primary source data were collected based on a questionnaire survey utilising the Lokman Emotion Importance Quadrant (LEIQ™) model. The data were analysed using text mining techniques based on R‐Language software. According to the findings, happiness in Malaysian low‐income communities is primarily shaped by concerns about family matters such as success, care issues, education and so on. Interestingly, government financial support, money, income and work matters are only fifth in importance, and lastly, the least emphasis was put on the public transportation issue. To ensure the true happiness and wellbeing of people who are struggling financially, policymakers must provide not only financial assistance but also ensure a better future for their families: better healthcare, better incomes, keeping a roof over their heads, etc. This study will be useful to the government, development agencies, NGOs, international organisations and other stakeholders wanting to realise the United Nations' Sustainable Development Goals (SDGs) in Malaysia and similarly affected countries. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement. [ABSTRACT FROM AUTHOR]

Port State Control (PSC) inspection data is used for determining the inspection pattern of PSC in Malaysia and identifying the relationship between the inspection place, flag state, number of deficiency, detention result, and ship risk profile. Based on 8,089 inspection reports from 2015 to 2019, the mining association rule is proposed as a learning approach due to its determination pattern in the information bank. The learning of association rules of PSC inspections is performed primarily on the Apriori Algorithm, in order to produce alluring rules. Inspection patterns of Malaysian ports revealed that flag state, ship risk profile, and inspection place generally lead to no detention result, as well as zero deficiency recorded on-board. The reported quantity of detention was significantly related to the high number of deficiencies raised for ships registered under blacklisted countries. Furthermore, the analysis of deficiency discovered the pattern of inspection at Malaysian ports is frequently related to zero and a low number of deficiencies raised by inspectors. Lastly, five major ports were selected for providing a useful rule to help PSC officers in organising an effective inspection plan. A similar approach can also be used for other ports beyond Malaysia for comparative analysis. [ABSTRACT FROM AUTHOR]