Perceived Accuracy and User Behavior: Exploring the Impact of AI-Based Air Quality Detection Application (AIKU)

  • Qurotul Aini Satya Wacana Christian University, Indonesia
  • Irwan Sembiring Satya Wacana Christian University, Indonesia
  • Adi Setiawan Satya Wacana Christian University, Indonesia
  • Iwan Setiawan Satya Wacana Christian University, Indonesia
  • Untung Rahardja University of Technology Malaysia, Malaysia
Keywords: accuracy, air quality, artificial intelligence, TAM


The accuracy of air quality detection is a crucial aspect influencing user trust and satisfaction with artificial intelligence (AI) based air quality detection applications. However, only a few studies have tested the effect of the accuracy of AI-based quality detection on users' acceptance and use of these applications. This study aims to fill this gap by addressing the impact of perceived accuracy on behavioral intention and behavior using the AIKU application. This research uses a quantitative approach with the online survey method, distributed in January 2023 - February 2023 to AIKU users. Valid data were 287 respondents from 317 who were received and analyzed using partial least squares structural equation modeling (PLS-SEM). This study uses a modified technology acceptance (TAM) model by adding perceived intelligence as a mediating variable between perceived accuracy and usefulness. The results showed that nine hypotheses were accepted from the 13 hypotheses proposed. The results section of hypothesis testing shows that the effect of perceived AIKU application accuracy on perceived usability and ease of use is insignificant. However, these influences indirectly affect the behavioral intentions and attitudes of users. Even if users do not perceive purity as an essential factor, the user's attitude towards the application is still positive. This study makes a theoretical contribution by developing the TAM model by incorporating variables of perceived accuracy and perceived intelligence relevant to the AI-based context.


Bhalgat, P., Pitale, S., & Bhoite, S. (2019). Air quality prediction using machine learning algorithms. International Journal of Computer Applications Technology and Research, 8(9), 367–370.

Gupta, H., Bhardwaj, D., Agrawal, H., Tikkiwal, V. A., & Kumar, A. (2019). An IoT based air pollution monitoring system for smart cities. 173–177.

Jentzer, J. C., Kashou, A. H., & Murphree, D. H. (2023). Clinical applications of artificial intelligence and machine learning in the modern cardiac intensive care unit. Intelligence-Based Medicine, 100089.

Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2022). What factors contribute to acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 101925.

Lutfiani, N., Wijono, S., Rahardja, U., Iriani, A., Aini, Q., & Septian, R. A. D. (2023). A bibliometric study: Recommendation based on artificial intelligence for ilearning education. Aptisi Transactions on Technopreneurship (ATT), 5(2), 109–117.

Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169.

Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance model of artificial intelligence (AI)-based technologies in construction firms: Applying the Technology Acceptance Model (TAM) in combination with the Technology–Organisation–Environment (TOE) framework. Buildings, 12(2), 90.

Okokpujie, K., Noma-Osaghae, E., Modupe, O., John, S., & Oluwatosin, O. (2018). A smart air pollution monitoring system. International Journal of Civil Engineering and Technology (IJCIET), 9(9), 799–809.

Ottosen, T.-B., & Kumar, P. (2019). Outlier detection and gap filling methodologies for low-cost air quality measurements. Environmental Science: Processes & Impacts, 21(4), 701–713.

Rahardja, U., Aini, Q., Manongga, D., Sembiring, I., & Girinzio, I. D. (2023). Implementation of tensor flow in air quality monitoring based on artificial intelligence. International Journal of Artificial Intelligence Research, 6(1).

Rahardja, U., Aini, Q., Manongga, D., Sembiring, I., & Sanjaya, Y. P. A. (2023). Enhancing Machine Learning with Low-Cost P M2. 5 Air Quality Sensor Calibration using Image Processing. APTISI Transactions on Management, 7(3), 201–209.

Rhee, J. H., Ma, J. H., Seo, J., & Cha, S. H. (2022). Review of applications and user perceptions of smart home technology for health and environmental monitoring. Journal of Computational Design and Engineering, 9(3), 857–889.

Samadbeik, M., Aslani, N., Maleki, M., & Garavand, A. (2023). Acceptance of mobile health in medical sciences students: Applying technology acceptance model. Informatics in Medicine Unlocked, 101290.

Saravanan, D., & Kumar, K. S. (2021). Improving air pollution detection accuracy and quality monitoring based on bidirectional RNN and the Internet of Things. Materials Today: Proceedings.

Sharma, A. K., & Balyan, P. (2020). Air pollution and COVID-19: Is the connect worth its weight? Indian Journal of Public Health, 64(6), 132–134.

Singh, S., & Ananthanarayanan, V. (2020). Air Quality Monitoring System with Effective Traffic Control Model for Open Smart Cities of India. 405–419.

Syuhada, G., Akbar, A., Hardiawan, D., Pun, V., Darmawan, A., Heryati, S. H. A., Siregar, A. Y. M., Kusuma, R. R., Driejana, R., & Ingole, V. (2023). Impacts of Air Pollution on Health and Cost of Illness in Jakarta, Indonesia. International Journal of Environmental Research and Public Health, 20(4), 2916.

Xiang, X., Fahad, S., Han, M. S., Naeem, M. R., & Room, S. (2023). Air quality index prediction via multi-task machine learning technique: Spatial analysis for human capital and intensive air quality monitoring stations. Air Quality, Atmosphere & Health, 16(1), 85–97.

Zhu, D., Cai, C., Yang, T., & Zhou, X. (2018). A machine learning approach for air quality prediction: Model regularization and optimization. Big Data and Cognitive Computing, 2(1), 5.

How to Cite
Aini, Q., Sembiring, I., Setiawan, A., Setiawan, I., & Rahardja, U. (2023). Perceived Accuracy and User Behavior: Exploring the Impact of AI-Based Air Quality Detection Application (AIKU). Indonesian Journal of Applied Research (IJAR), 4(3), 209-224.