Vol.9 (2019) No. 5 ISSN: 2088-5334 Implementation of Data Abstraction Layer Using Kafka on SEMAR Platform for Air Quality Monitoring Yohanes Yohanie Fridelin Panduman # , Mochamad Rifki Ulil Albaab # , Adnan Rachmat Anom Besari + , Sritrusta Sukaridhoto +,* , Anang Tjahjono # , Rizqi Putri Nourma Budiarti & # Electrical Engineering Department, Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia E-mail: yohanyfp@pasca.student.pens.ac,id, mochrifkiulila@pasca.student.pens.ac.id, anang_tj@yahoo.co.id + Informatics and Computer Department, Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia E-mail: anom@pens.ac.id, * dhoto@pens.ac.id (corresponding author) & Department of Engineering, Nahdlatul Ulama University of Surabaya, Surabaya, 60237, Indonesia E-mail: rizqi.putri.nb@unusa.ac.id Abstract—Urbanization and fast-growing industries causing air quality in urban areas to be bad and even tend to be dangerous. In addition, the largest percentage of energy emissions come from the transportation sector, specifically on road transportation. Therefore, the need for a quality detection system that is capable of distributing and displaying large data information in real-time cannot be resolved by the system currently used by the government. This research offers a solution to the implementation of data abstraction in cloud computing which is built using the concept microservice architecture and integrated with mobile-based sensors to detect air quality in real-time. This solution consists of integrated cloud computing services using Smart Environment Monitoring and Analytical in Real-time (SEMAR) and Vehicles as Mobile Sensor Networks (VaaMSN) to detecting air quality. SEMAR was built with microservice references consist of data abstraction, communication, data analytical with business analytics proccess, data storage with Big data service and also real-time visualization in maps, chart, and table through dasboard website. Through the experiments that we did show that the microservice of data abstraction layer can be installed at the SEMAR stage indicating that the average delay in sending information is around 0.09 ms (90μs), this indicates that the system can be said to be real-time. With specific and real-time locations in data visualization, the government can use this method as an new alternative method of air quality. Keywords— cloud computing; air quality; Kafka; data abstraction; internet of things. I. INTRODUCTION The transportation development increase the urban people health. In 2010, Jakarta's population was 9,607,787, and 57.8% of the population experienced the effects of air pollution. These people suffer from various diseases related to air pollution, causing an increase in health costs up to Rp. 38.5 trillion / USD54 billion [2]. Also, Indonesia is the 6th largest emitter of greenhouse gases in the world (IEA 2015) where 40% of Energy Emission Percentages come from the transportation sector, and 90% of these transportation emissions come from road transportation. The current government uses several air quality sensor that installed on the air monitoring station in a fixed position to measure air condition. However, the system has weaknesses like difficult to maintenance, high costs because it requires a lot of devices, only cover a small area and need more effort to maintain the station. The system does not use the Big Data environment but uses a general database. Therefore it does not apply the concept of data abstraction for data distribution and machine learning methods for air quality analysis. The air quality parameters in Indonesian air pollution rules are Particulate (   ), Carbon monoxide (CO), Ozone (   ), Sulfur dioxide (   ) and Nitrogen dioxide (  ) [3]. There are several studies on real-time environmental monitoring used cloud computing that implements with big data concept, one of which is Smart Environment Monitoring and Analytics in Real-time System (SEMAR) [4-8]. SEMAR is used to detect river water quality using portable devices integrated with water quality sensors [4] and also ROV (small robot submarine) that are installed with water quality sensors [5], [6]. This is an alternative solution to assist governments in monitoring water environments in urban areas. Water quality samples detected using a water quality sensor were installed in the ROV, then sent into 1520