Pembelajaran Robot Mobile: Menggunakan Gaya Belajar Kolb Agar Efektif untuk Pembelajaran Navigasi Algoritma Kontrol Gerak Robot Berbasis Sensor

Faiz Kamal, Cucuk Wawan Budiyanto

Abstract

Aktifitas robot tidak lepas dari suatu gerakan yang sudah direncanakan maupun tanpa direncanakan oleh pembuat algoritma kontrol gerak robot. Algoritma kontrol gerak inilah yang akan mempengaruhi tindakan robot saat masukan robot menerima sinyal dari lingkungan atau kondisi yang telah ditentukan. Faktor umum yang mempengaruhi gerakan tersebut adalah hambatan diam (static) maupun bergerak (dynamic) yang mengganggu fungsi utama robot. Untuk memecahkan masalah tersebut, maka diperlukan penggunaan navigasi dan algoritma gerak kontrol berbasis sensor. Penelitian ini akan menganalisis penggunaan gaya belajar Kolb dan navigasi robot agar efektif dipelajari peserta didik dengan disertai pemahaman logika algoritma. Analisa akan dimulai melalui pemahaman macam-macam gaya belajar peserta didik yang secara garis besar dibagi menjadi 4 yaitu Converger, Diverger, Assimilator, dan Accomodator. Lalu dari hasil review literatur yang ada, dicari cara yang paling efektif dalam menyampaikan pembelajaran ini. Hasilnya peserta didik yang menggunakan accomodators dan convergers direkomemndasikan melakukan pekerjaan lebih awal sehingga divergers dan assimilators dapat meniru pekerjaan mereka. Dimana divigers dan assimilators lebih baik dalam debugging pekerjaan .

 

Kata Kunci: autonomus, gaya-belajar, navigasi, pendidikan, robot

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References

Buschmann, C., Müller, F., & Fischer, S. (2004). Grid-Based Navigation for Autonomous, Mobile Robots. Proceedings of the Workshop on Positioning, Navigation and Communication (WPNC 2004), 157–162.

Felder, R., & Brent, R. (2005). Understanding student differences. Journal of Engineering Education, 94(1), 57–72. https://doi.org/10.1002/j.2168-9830.2005.tb00829.x

Gehlod, L., Jain, V., Dutta, M., & Lal, D. K. (2013). A Grid Based Robot Navigation by Using Priority Algorithm. International Journal of Advanced Research in Computer and Communication Engineering, 2(8).

Giessen, H. W. (2015). Serious Games Effects: An Overview. Procedia - Social and Behavioral Sciences, 174, 2240–2244. https://doi.org/10.1016/j.sbspro.2015.01.881

Gooden, D. J., Preziosi, R. C., & Barnes, F. B. (2009). An Examination Of Kolb’s Learning Style Inventory. American Journal of Business Education, 2(3), 57–62.

Harshini, B., & Ramji, K. (2015). Navigation of Mobile Robot in the Presence of Static Obstacles of Various Shapes, 5(2), 2–6.

Kamil, F., Hong, T. S., Khaksar, W., Moghrabiah, M. Y., Zulkifli, N., & Ahmad, S. A. (2017). New robot navigation algorithm for arbitrary unknown dynamic environments based on future prediction and priority behavior. Expert Systems with Applications, 86, 274–291. https://doi.org/10.1016/j.eswa.2017.05.059

Kleiner, A., & Nebel, B. (n.d.). Search Algorithms and Path- finding Introduction to Multi-Agent Programming.

Kolb, A. Y., & Kolb, D. a. (2005). The Kolb Learning Style Inventory — Version 3 . 1 2005 Technical Specifi cations. LSI Technical Manual. https://doi.org/10.1016/S0260-6917(95)80103-0

Kolb, D. A. (1984). Experiential Learning: Experience as The Source of Learning and Development. Prentice Hall, Inc., (1984), 20–38. https://doi.org/10.1016/B978-0-7506-7223-8.50017-4

Kumar, A. (2010). Computer Basics with Office Automation. New Delhi: I.K. International Publishing House Pvt. Ltd.

Lee, Y. A. (2014). Insight for writing a qualitative research paper. Family and Consumer Sciences Research Journal, 43(1), 94–97. https://doi.org/10.1111/fcsr.12084

Linert, J., & Kopacek, P. (2016). Robots for Education (Edutainment). IFAC-PapersOnLine, 49(29), 24–29. https://doi.org/10.1016/j.ifacol.2016.11.065

Lu, H., Jia, L., Gong, S. H., & Clark, B. (2007). The relationship of Kolb learning styles, online learning behaviors and learning outcomes. Educational Technology and Society, 10(4), 187–196.

Mies, G. (2010). Military robots of the present and the future. Aarms Technology, 9(1), 125–137.

Persson, S. M., & Sharf, I. (2014). Sampling-based A* algorithm for robot path-planning. The International Journal of Robotics Research, 33(13), 1683–1708. https://doi.org/10.1177/0278364914547786

Qureshi, M. O., & Syed, R. S. (2014). The impact of robotics on employment and motivation of employees in the service sector, with special reference to health care. Safety and Health at Work, 5(4), 198–202. https://doi.org/10.1016/j.shaw.2014.07.003

Richmond, A. S., & Cummings, R. (2005). Implementing Kolb ’ s Learning Styles into Online Distance Education. International Journal of Technology in Teaching and Learning, 1(1), 45–54. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.385.3226&rep=rep1&type=pdf

Romanelli, F., Bird, E., & Ryan, M. (2009). Learning styles: A review of theory, application, and best practices. American Journal of Pharmaceutical Education, 73(1). https://doi.org/10.5688/aj730109

Rooks, B. (2002). Mobile robots walk into the future. Industrial Robot: An International Journal, 29(6), 517–523. https://doi.org/10.1108/01439910210449481

Sutton, J., & Austin, Z. (2015). Qualitative Research: Data Collection, Analysis, and Management. The Canadian Journal of Hospital Pharmacy, 68(3), 226–31. https://doi.org/10.1111/acem.12735

Švarcová, E., & Jelínková, K. (2016). Detection of Learning Styles in the Focus Group. Procedia - Social and Behavioral Sciences, 217, 177–182. https://doi.org/10.1016/j.sbspro.2016.02.057

Tabassum, B. F., Lopa, S., Tarek, M. M., & Ferdosi, B. J. (2017). Obstacle Avoiding Robot, 17(1). Retrieved from http://www.instructables.com/id/Arduino-Ultimate-Obstacle-Avoiding-Robot/?ALLSTEPS

Tóth, P. (2012). Learning strategies and styles in vocational education. Acta Polytechnica Hungarica, 9(3), 195–216.

Uppalancha,Kranthi, B. (2015). Optimizing the Robot’s Path Using Dijkstra Algorithm. International Journal of Innovative Research in Science, Engineering and Technology, 4(6), 4423–4430. https://doi.org/10.15680/IJIRSET.2015.0406050

Williams, R. L., Wu, J., Williams, R. L., & Wu, J. (2010). Dynamic Obstacle Avoidance for an Omnidirectional Mobile Robot. Journal of Robotics, 2010(740), 1–14. https://doi.org/10.1155/2010/901365

Wu, F., & Williams, J. (2014). Design and Implementation of a Multi-Sensor Based Object Detecting and Removing Autonomous Robot Exploration System, (May), 8–16.

DOI: https://doi.org/10.20961/uvd.v1i0.15936

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