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All Journal Jurnal ULTIMA Computing Prosiding Konferensi Nasional PKM-CSR
MB Nugraha
Universitas Multimedia Nusantara
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Environmental Science Nursing Public Health Social Sciences

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Journal : Jurnal ULTIMA Computing

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Passive Keyless Entry Locking Door With ESP32 Arya Wibisono; MB Nugraha
Ultima Computing : Jurnal Sistem Komputer Vol 12 No 1 (2020): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (445.482 KB) | DOI: 10.31937/sk.v12i1.1613

Abstract

in this modern era, technology is increasingly sophisticated and can be used by anyone, for example, is a lock that can be bought by anyone and is easy to learn. Therefore, the physical key is getting old and easy to be broken by anyone. By implementing a passive keyless entry system that has been applied to today's cars, adding to the safety and comfort of the car user. we try to apply the system to ESP32 and make digital keys in this research still lacking in card security systems using digital signature algorithms, passive keyless entry systems run as expected by using RFID as input identifier from the UID of each RFID card which is proven to be unique and easy to use.
Preliminary Study on Indonesian Word Recognition for Elder Companion Robot MB Nugraha; Dyah Ayu Anggreini Tuasikal; Ni Made Satvika Iswari; Luthfialmas Fakhrizki
Ultima Computing : Jurnal Sistem Komputer Vol 14 No 1 (2022): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v14i1.2696

Abstract

Word recognition using deep learning is a simple approach to speech recognition in general. From this word-level recognition, the emotional expression recognition model. The emotion recognition model can be used to describe the important level of action on future planned hardware implementation. This research was conducted using MFCC as the feature extraction method from the audio data and using the CNN-LSTM approach for the emotional expression classifier. The model itself will be implemented into a humanoid robot to become a companion robot for the elderly. The model itself has 67% accuracy for emotion recognition and 97% accuracy for word recognition. However, the model only attained 20% accuracy in real-life testing using the humanoid robot as the model tends to overfitting as a result of the lack of data used in model training.
Co-Authors Ahmad Syahril Muharom Arya Wibisono Dwi Dharma Arta Kusuma Dyah Ayu Anggreini Tuasikal Indiwan Seto Wahjuwibowo Luthfialmas Fakhrizki Mohammad Shobri Ni Made Satvika Iswari