Pengenalan Bahasa Isyarat Indonesia menggunakan Mediapipe dengan Model Random Forest dan Multinomial Logistic Regression

https://doi.org/10.35912/jisted.v1i1.1899
Published: Feb 23, 2023

Abstract:

Purpose: This research aims to create a random forest machine learning model and logistic regression that can perform the sign language recognition of the Indonesian Sign Language System (SIBI) using a regular RGB camera with the MediaPipe framework.

Research methodology: Both variables in this study are measured using Innovative Work Behavior (IWB) Scale from Janssen (2000) and Connor-Davidson Resilience Scale (CD-RISC) from Connor & Davidson (2003) that was distributed through Google Form link. The data analysis is done with the support of the 25th version of SPSS (Statistical Package for Social Science).

Results: Resilience has a significant correlation with innovative work behavior among college students.

Limitations: No strict controls of questionnaire administration, the questionnaire consists of 6 different measurements from the research team, and can't be fully generalized to the college students population.

Contribution: New findings of correlation between two variables among new samples.

Keywords:
1. Sign Language Recognition
2. Indonesian Sign Language
3. SIBI
4. MediaPipe
Authors:
1 . Imam Suyudi
2 . Sudadio Sudadio
3 . Suherman Suherman
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How to Cite
Suyudi, I. ., Sudadio, S., & Suherman, S. (2023). Pengenalan Bahasa Isyarat Indonesia menggunakan Mediapipe dengan Model Random Forest dan Multinomial Logistic Regression . Jurnal Ilmu Siber Dan Teknologi Digital, 1(1), 65–80. https://doi.org/10.35912/jisted.v1i1.1899
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Issue & Section
Vol. 1 No. 1 (2022): November
Articles
References

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  1. Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools.
  2. Breiman, L. (2001). Random forests. Machine Learning 2001 45:1, 45:5–32.
  3. Handhika, T., Zen, R. I., Murni, Lestari, D. P., and Sari, I. (2018). Gesture recognition for indonesian sign language (bisindo). Journal of Physics: Conference Series, 1028:012173.
  4. Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Courna- peau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peter- son, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825):357–362.
  5. Humaira, F., Supria, S., Herumurti, D., and Widarsono, K. (2018). Real time sibi sign language recognition based on k-nearest neighbor. undefined, 5.
  6. Kulkarni, A., Chong, D., and Batarseh, F. A. (2020). Foundations of data imbalance and solutions for a data democracy. Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering, pages 83–106.
  7. Liu, D. C. and Nocedal, J. (1989). On the limited memory bfgs method for large scale optimization. Mathematical Programming 1989 45:1, 45:503–528.
  8. Lugaresi, C., Tang, J., Nash, H., Mcclanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M. G., Lee, J., Chang, W.-T., Hua, W., Georg, M., Grun- dmann, M., and Research, G. (2019). Mediapipe: A framework for building per- ception pipelines.
  9. Misra, S. and Li, H. (2020). Noninvasive fracture characterization based on the clas- sification of sonic wave travel times. Machine Learning for Subsurface Characte- rization, pages 243–287.
  10. Murray, J. (2018). 70 million deaf people. 200+ sign languages. unlimited potential.
  11. Ng, A. Y. (2004). Feature selection, l1 vs. l2 regularization, and rotational invariance. Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004, pages 615–622.
  12. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blon- del, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Co- urnapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825– 2830.
  13. Putra, T. I. Z. M. ., Suprapto, S., & Bukhori, A. F. . (2022). Model Klasifikasi Berbasis Multiclass Classification dengan Kombinasi Indobert Embedding dan Long Short-Term Memory untuk Tweet Berbahasa Indonesia. Jurnal Ilmu Siber Dan Teknologi Digital, 1(1), 1–28. https://doi.org/10.35912/jisted.v1i1.1509
  14. Rastgoo, R., Kiani, K., and Escalera, S. (2021). Sign language recognition: A deep survey. Expert Systems with Applications, 164:113794.
  15. Ridwang, R. (2017). Pengenalan bahasa isyarat indonesia (sibi) menggunakan leap motion controller dan algoritma data mining naïve bayes. Jurnal INSYPRO (Infor- mation System and Processing), 2.
  16. Rokach, L. and Maimon, O. (2005). Decision trees. Data Mining and Knowledge Discovery Handbook, pages 165–192.
  17. Setiawan, E. ., Nurhatisyah, N. ., & Nanra, S. . . . (2023). Pengontrolan Bahaya Kebakaran Berbasis IOT pada Ruang Server SMFR Balai Monitor Spektrum Frekuensi Radio Kelas II Batam . Jurnal Ilmu Siber Dan Teknologi Digital, 1(1), 41–51. https://doi.org/10.35912/jisted.v1i1.1800
  18. Suharjito, Thiracitta, N., and Gunawan, H. (2021). Sibi sign language recogni- tion using convolutional neural network combined with transfer learning and non- trainable parameters. Procedia Computer Science, 179:72–80.
  19. Zhang, F., Bazarevsky, V., Vakunov, A., Tkachenka, A., Sung, G., Chang, C.-L., and Grundmann, M. (2020). Mediapipe hands: On-device real-time hand tracking.