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Abstract
Penelitian ini membahas integrasi Teachable Machine dengan Arduino untuk pengklasifikasian bentuk objek secara real-time. Dengan memanfaatkan model pembelajaran mesin yang dilatih menggunakan Teachable Machine, sistem ini mampu mengenali berbagai bentuk objek seperti buku, pulpen, dan spidol, serta kondisi tanpa objek (no object), dan merespons dengan cepat melalui komunikasi serial Arduino. Metode yang digunakan meliputi pengumpulan data gambar untuk pelatihan model, implementasi model ke dalam format TensorFlow.js, dan penggunaan pustaka p5.js serta ml5.js untuk membuat aplikasi web interaktif yang menggunakan pembelajaran mesin, termasuk penggunaan pustaka p5.serialport untuk komunikasi serial antara model dan Arduino. Komunikasi serial antara Arduino dan p5.js dilakukan melalui p5.serialcontrol, yang memastikan transfer data yang cepat dan andal. Hasil pengujian menunjukkan akurasi pengenalan di atas 90% dengan waktu respons yang minimal, memungkinkan aplikasi dalam berbagai konteks seperti pendidikan dan otomatisasi. Penelitian ini menegaskan bahwa kombinasi Teachable Machine dan Arduino merupakan solusi efisien untuk pengenalan objek dalam aplikasi real-time, dengan potensi untuk pengembangan lebih lanjut di bidang interaksi manusia-komputer (Human Machine Interface).
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References
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References
Teachable Machine, “What is Teachable Machine?,” Google. Accessed: Feb. 22, 2024. [Online]. Available: https://teachablemachine.withgoogle.com/
M. Carney et al., “Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification,” in Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, in CHI EA ’20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 1–8. doi: 10.1145/3334480.3382839.
Arduino, “What is Arduino? | Arduino.” Accessed: Feb. 20, 2023. [Online]. Available: https://www.arduino.cc/en/Guide/Introduction
E. A. U. Malahina, R. P. Hadjon, and F. Y. Bisilisin, “Teachable Machine: Real-Time Attendance of Students Based on Open Source System,” IJICS (International J. Informatics Comput. Sci., vol. 6, no. 3, p. 140, 2022, doi: 10.30865/ijics.v6i3.4928.
M. B. Baihaqi, Y. Litanianda, and A. Triyanto, “Implementasi Tensor Flow Lite Pada Teachable Untuk Identifikasi Tanaman Aglonema Berbasis Android,” Komputek, vol. 6, no. 1, p. 70, 2022, doi: 10.24269/jkt.v6i1.1143.
N. M. Farhan and B. Setiaji, “Deteksi Penyakit Tanaman Padi Berbasis Android Melalui Pemanfaatan Teachable Machine,” Indones. J. Comput. Sci., vol. 12, no. 2, pp. 284–301, 2023, [Online]. Available: http://ijcs.stmikindonesia.ac.id/ijcs/index.php/ijcs/article/view/3135
C. Chazar and M. H. Rafsanjani, “Penerapan Teachable Machine Pada Klasifikasi Machine Learning Untuk Identifikasi Bibit Tanaman,” in Prosiding Seminar Nasional Inovasi dan Adopsi Teknologi (INOTEK), 2022, pp. 32–40. doi: https://doi.org/10.35969/inotek.v2i1.207.
B. Jiang, X. Li, L. Yin, W. Yue, and S. Wang, “Object Recognition in Remote Sensing Images Using Combined Deep Features,” in 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2019, pp. 606–610. doi: 10.1109/ITNEC.2019.8729392.
M. Dol and A. Geetha, “A Learning Transition from Machine Learning to Deep Learning: A Survey,” in 2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI), 2021, pp. 89–94. doi: 10.1109/ICETCI51973.2021.9574066.
A. Ng, Machine Learning Yearning. 2016. doi: 10.1007/978-981-10-1509-0_9.
Arduino, “Arduino ® Nano Arduino ® Nano Features,” Arduino, pp. 1–13, 2022.
Interactive Telecommunications Program (ITP) at NYU, “Using the p5.serialport library.” Accessed: Jun. 10, 2024. [Online]. Available: https://itp.nyu.edu/physcomp/labs/labs-serial-communication/lab-serial-input-to-the-p5-js-ide/
Mozilla, “The WebSocket API (WebSockets),” MDN web docs. Accessed: May 10, 2024. [Online]. Available: https://developer.mozilla.org/en-US/docs/Web/API/WebSockets_API
P5js, “p5.js,” p5js.org. Accessed: May 10, 2024. [Online]. Available: https://p5js.org/
ml5js.org, “ml5.” GitHub, 2022. [Online]. Available: https://github.com/ml5js/ml5-library
S. Van Every, “p5.serialport.” GitHub, 2022. [Online]. Available: https://github.com/p5-serial/p5.serialport