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In a broad scope, the term Information System (IS) is a scientific field of research study that approaches the scope of managerial, strategic, and operational activities complex in the storing, processing, distributing, gathering, and utilizing of knowledge and its associated technologies in organizations and industry. The model of railway supplier selection using BI-KM framework is situated on a horizontal structure of the organization and its technology transformation to execute the organization goal, with technology as enabler and driver (technology adoption), organization as the principal environment (business process analysis), and Information Management (data modeling). This study is significant in supporting data scenarios by focusing on the heuristic view of an industry approach to problem-solving management issues. Furthermore, the research development was to identify integrated framework adoption that contributes to strategic performance diagnostics dashboard. By understanding the factors of theoretical framework adoption, these conceptual frameworks assure competitive advantage. Besides, this railway supplier selection excellence model analyzed the extent and provides a potential solution to strategic decision-making issues. The study directs to regulate the adoption of the theoretical framework towards conceptual framework by using the role of Business Intelligence (BI) to analyze the quality of data presented as the railway supplier selection criteria from operational management through data analytics. Moreover, this will be united to help the best cycles and instruments in essential execution by the executives of a railway supplier selection dashboard for simulating data as interactive supplier performance.

The main intention of this research is to discover the significance of sentiment analysis of Twitter data whether it is positive, neutral or negative. The sentiment analysis is dependent mining about textual content which being extracted and identified as contextual and subjective knowledge in such perceptible origin of recent rapid expanding computer science research. We started with a systematic literature review, where we had adopted both qualitative coding and text mining by scrutinizing 3282 of input of textual data retrieved from Twitter Streaming API. We perceived the problem as the decision trees kind of sentiment analysis in learning and information gaining. Therefore, we showed how basic decision trees are built to calculate the sentiment values of Twitter data. Sentiment analysis has transformed from interpreting online textual output analysis into perceiving contextual social media texts for example from Twitter. Hence, two decision trees were built to observe the performance and information gaining of decision trees. Thus, the precision of both decision trees led to the precision percentage that will be respectively stated, and the best decision tree can be obtained.