Dipo Anugrah Salam, Cepi Riyana, Ellina Rienovita



The Development of MELANI (Medicinal Plant Identifier) Mobile Intelligent Decision Support System is a study on the  design and development of an application for learning medicinal plants. Generally the purpose of this research is to knowing the design and development of MELANI (Medicinal Plant Identifier) Mobile Intelligent Decision Support System application, and also to get review from experts and response from it’s users. Design and development method was used as the research methods together with the use of research design based on a knowledge base that is in line with existing problems as well as the use of creative methods in solving these problems,, in which focused on product design and development in the form of MIDSS applications, followed by a series of assessments, trials, and revisions to the product. Technique that was used for data gathering were  interview, questionnaire, and observation while data reduction, data display, and conclusion was used as data analysis technique. This research resulted in the development of MELANI (Medicinal Plant Identifier) Mobile Intelligent Decision Support System in the form of Android application with the use of Artificial Intelligence (AI) technology in helping it’s users identifying and learning medicinal plants in the field.

 Keywords: Design and Development, Artificial Intelligence, Mobile Intelligent Decision Support System, Medicinal Plant Learning.

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Kaklauskas, , A. (2015). Intelligent decision support systems. In Biometric and intelligent decision making support (pp. 31-85). Springer, Cham.

Liu, Q., Diao, L., & Tu, G. (2010, November). The application of artificial intelligence in mobile learning. In 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization (Vol. 1, pp. 80-83). IEEE.

Murphy, J. (2016). An Overview of Convolutional Neural Network Architectures for Deep Learning. Microway, Inc.

Rusdi, M. (2018). Educational Design and Development Research. Depok: Rajawali Press.

Sandler, , M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510-4520).

Shahroom, A. A., & Hussin, N. (2018). Industrial revolution 4.0 and education. International Journal of Academic Research in Business and Social Sciences, 8(9), 314-319.

Shyshkanova, G., Zaytseva, T., & Frydman, O. (2017). Mobile technologies make education a part of everyday life. Information and Learning Science.

Smaldino, S. E., Lowther, D. L., & Russell, J. D. (2011). Instructional Technology & Media For Learning. Jakarta: Kencana Prenadamedia Group.

Thuseethan., & Kuhanesan, S. (2015). Effective use of human computer interaction in digital academic supportive devices. arXiv preprint arXiv:1501.00529.


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