## 1 Introduction  
Manufacturing plays an important role in economic development and is still considered crucial to economic growth in the globalization era [1; 2]. It has a positive impact on the growth of both developed and developing countries [3; 4]. Emerging technologies are utilized by the manufacturing industry to enhance the economic competitiveness of individual manufacturers and the sustainability of the entire industrial sector. The adoption of information and communication technology (ICT) in manufacturing enables a transition from traditional to advanced manufacturing processes [5]. Monitoring systems, as part of ICT application, play an important part in manufacturing process control and management. Recent developments in information technology enable the integration of various monitoring applications into one complex system for the whole supply chain [6]. In general cases, the application of a monitoring system plays an important role in predicting disease [7], improving production, reducing cost [8], and providing an early warning system [9; 10]. Recent technologies such as Internet of Things (IoT)-based sensors can be utilized and integrated with monitoring systems. Studies have been conducted in the manufacturing industry and showed significant benefits from the use of IoT-based sensors for monitoring such as working condition improvements [11], error design prevention [12], fault diagnosis [13], quality prediction [14], and helping managers with better decision making [15]. With the increasing number of IoT sensing devices available, data generated from the manufacturing industry (i.e., process logs, events, images and sensor data) are expected to grow exponentially. This type of data is called \"big data\" [16]. Big data analysis has led to significant improvements in the manufacturing industry, such as reducing energy consumption [17], improving production scheduling and logistics planning [18], mitigating social risks [19], and facilitating better decision making [20]. Previous studies have shown significant benefits from several big data technologies in processing and storing large volumes of data quickly, such as with the application of Apache Kafka [21; 22; 23; 24; 25; 26], Apache Storm [27; 28; 29; 30; 31], and NoSQL MongoDB [32; 33; 34; 35; 36; 37]. Previous studies showed significant advantages from the integration of big data technologies such as reducing the processing time for home automation systems [38], providing effective and efficient solutions for processing IoT-generated data for smart cities [39], and handling large amounts of smart environmental data in real-time [40]. The aforementioned big data technologies have been integrated in data processing systems, resulting in significant advantages due to processing large amounts of streaming spatiotemporal data [41] as well as processing massive amounts of manufacturing sensor data efficiently [42]. Therefore, it is necessary to integrate Apache Kafka, Apache Storm, and MongoDB in big data processing systems for the manufacturing industry so that large amounts of streaming manufacturing sensor data can be promptly processed, stored, and presented in real-time. Data (e.g., sensor data from the production line, environmental data, etc.) generated by the manufacturing industry needs to be analyzed to help managers with decision making. Machine learning methods can be considered advanced technology with great potential for data analysis and has been successfully applied in various areas such as fault detection [43], quality prediction [14; 44], defect classification [45], and visual inspection [46]. In the case of fault prediction, machine learning algorithms such as Random Forest are highly effective in detecting abnormal events in a process and thus can help avoid productivity loss [47; 48; 49]. However, machine learning algorithms encounter problems with outlier data, which can reduce the accuracy of the classification model.