Predicting the Number of Passengers of MRT Jakarta Based on the Use of the QR-Code Payment Method during the Covid-19 Pandemic Using Long Short-Term Memory

The trend of using public transportation has been rising over the last several decades. Because of increased mobility, public transportation has now become more crucial. In modern environments, public transportation is not only used to carry people and products from one location to another but has also evolved into a service company. In Jakarta, Mass Rapid Transit Jakarta (MRTJ) started to operate in late 2019. Recently, they updated their payment gateway system with QR codes. In this study, we predicted the hourly influx of passengers who used QR codes as their preferred payment method. This research applied machine learning to perform a prediction methodology, which is proposed to predict the number of passengers using time-series analysis. The dataset contained 7760 instances across different hours and days in June 2020 and was reshaped to display the total number of passengers each hour. Next, we incorporated time-series regression alongside LSTM frameworks with variations in architecture. One architecture, the 1D CNN-LSTM, yielded a promising prediction error of only one to two passengers for every hour

A review of data mining in knowledge management: applications/findings for transportation of small and medium enterprises

A core subfeld of knowledge management (KM) and data mining (DM) constitutes an integral part of the knowledge discovery in database process. With the explosion of information in the new digital age, research studies in the DM and KM continue to heighten up in the business organisations, especially so, for the small and medium enterprises (SMEs). DM is crucial in supporting the KM application as it processes the data to useful knowledge and KM role next, is to manage these knowledge assets within the organisation systematically. At the comprehensive appraisal of the large enterprise in the transportation sector and the SMEs across various industries—it was gathered that there is limited research case study conducted on the application of DM–KM on the transportation SMEs in specifc. From the extensive review of the case studies, it was uncovered that majority of the organisations are not leveraging on the use of tacit knowledge and that the SMEs are adopting a more traditional use of ICTs to its KM approach. In addition, despite DM–KM is being widely implemented—the case studies analysis reveals that there is a limitation in the presence of an integrated DM–KM assessment to evaluate the outcome of the DM–KM application. This paper concludes that there is a critical need for a novel DM–KM assessment plan template to evaluate and ensure that the knowledge created and implemented are usable and relevant, specifcally for the SMEs in the transportation sector. Therefore, this research paper aims to carry out an in-depth review of data mining in knowledge management for SMEs in the transportation industry

Big data analytics — A review of data-mining models for small and medium enterprises in the transportation sector.

The need for small and medium enterprises (SMEs) to adopt data analytics has reached a critical point, given the surge of data implied by the advancement of technology. Despite data mining (DM) being widely used in the transportation sector, it is staggering to note that there are minimal research case studies being done on the application of DM by SMEs, specifically in the transportation sector. From the extensive review conducted, the three most common DM models used by large enterprises in the transportation sector are identified, namely “Knowledge Discovery in Database,” “Sample, Explore, Modify, Model and Assess” (SEMMA), and “CRoss Industry Standard Process for Data Mining” (CRISP-DM). The same finding was revealed in the SMEs’ context across the various industries. It was also uncovered that among the three models, CRISP-DM had been widely applied commercially. However, despite CRISP-DM being the de facto DM model in practice, a study carried out to assess the strengths and weakness of the models reveals that they have several limitations with respect to SMEs. This paper concludes that there is a critical need for a novel model to be developed in order to cater to the SMEs’ prerequisite, especially so in the transportation sector context