Implementation of Machine Learning Using Google's Teachable Machine Based on Android

Agustian, Diki; Pertama, Pande Putu Gede Putra; Crisnapati, Padma Nyoman; Novayanti, Putu Devi

doi:10.1109/icoris52787.2021.9649528

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…In this study, the researcher focused on simulating phonetic recognition of East Sumbanese regional languages, paying particular attention to its basic terminology. Previous studies have validated the efficacy of the Teachable Machine, noting impressive levels of detection accuracy, precision, and sensitivity, ranging from 90% to 100% [6]- [8]. This excellent level of accuracy makes the Teachable Machine a recommended service in this research.…”

Section: Introductionmentioning

confidence: 69%

Teachable Machine: Deteksi Dialek Sumba Timur (Kambera) Menggunakan Layanan Open Source

Edwin Ariesto Umbu Malahina

2023

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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Studi penelitian ini dilaksanakan untuk mengembangkan sistem deteksi fonetik dialek Kambera dari bahasa lokal Sumba Timur berbasis framework TensorFlow yang akan diimplementasikan pada aplikasi mobile. Sebagai bagian dari inisiatif ini, penelitian ini telah mengompilasikan sebuah dataset representatif dari sampel fonetik dialek Kambera. Tujuan utamanya adalah untuk meningkatkan presisi dalam pengenalan fonetik. Menggunakan dialek Kambera sebagai studi kasus, data diekstraksi dan dilatih menggunakan layanan Teachable Machine berbasis open-source. Penelitian ini mengadopsi pendekatan berbasis convolutional neural network (CNN) yang dikombinasikan dengan metode Mel frequency cepstral coefficients (MFCC) untuk ekstraksi fitur yang lebih akurat. Setelah proses pengumpulan data, pelatihan model, pengujian, dan implementasi, model diintegrasikan ke dalam platform Android untuk kepentingan publik yang ingin memahami bahasa dialek Kambera dari Sumba Timur. Pengembangan dan pengujian sistem ini dirancang untuk mendeteksi dan menginterpretasikan fonetik bahasa lokal Sumba Timur dengan dialek Kambera, memberikan kontribusi signifikan dalam optimalisasi pengenalan fonetik, serta menyediakan dataset untuk kepentingan penelitian berkelanjutan. Sistem ini juga berfungsi sebagai alat edukasi linguistik yang dapat diakses dan mendukung inklusi serta diversifikasi linguistik dalam teknologi digital. Evaluasi empiris menunjukkan bahwa tingkat presisi deteksi dialek rata-rata secara keseluruhan mencapai 98,3% hingga 99,6%, dengan tingkat kepuasan pengujian kepada pengguna mencapai 99,33%. Hasil ini menegaskan bahwa sistem yang dikembangkan memiliki kapabilitas deteksi yang sangat efisien dan baik.

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Section: Introductionmentioning

confidence: 69%

Teachable Machine: Deteksi Dialek Sumba Timur (Kambera) Menggunakan Layanan Open Source

Edwin Ariesto Umbu Malahina

2023

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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“…Octopii is an open-source project [11], we appreciate contributions from the community. Octopii relies heavily on machine learning, and there's always room for improvement when training models are used.…”

Section: Acknowledgment Sincementioning

confidence: 99%

Detection and Classification of Personally Identifiable Information in Images Using Artificial Intelligence

Shaikh

2024

Preprint

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Personally Identifiable Information (PII) is any content that is sensitive that needs to be treated as secure and private. When data pieces such as a person’s name, address, Social Security number, phone number, email address, and so on may be used to identify a specific individual, they are deemed PII. As organizations grow, so does their volume of data. This makes identifying and protecting such sensitive resources at a scale quite complex. In this project, we demonstrate where and how PII can be discovered and how we developed a working prototype of a tool that can easily detect PII images using advanced artificial intelligence (AI) techniques such Optical Character Recognition (OCR) and image classification using Convolutional Neural Networks (CNN).

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“…Audio as well as images of humans with detected skeletal poses can also be uploaded (i.e., from a webpage) or can be recorded directly using the computer's microphone or camera. TM is fairly easy to use, is highly intuitive, and does not require background training [38]. At least two classes are needed for categorization (e.g., iceberg vs. mountain), but more can be added to the interface depending on the use case.…”

Section: Using Tm In a Geography Lessonmentioning

confidence: 99%

The Impact of Teachable Machine on Middle School Teachers’ Perceptions of Science Lessons after Professional Development

Kurz,

Jayasuriya,

Swisher

et al. 2024

Education Sciences

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Technological advances in computer vision and machine learning image and audio classification will continue to improve and evolve. Despite their prevalence, teachers feel ill-prepared to use these technologies to support their students’ learning. To address this, in-service middle school teachers participated in professional development, and middle school students participated in summer camp experiences that included the use of Google’s Teachable Machine, an easy-to-use interface for training machine learning classification models. An overview of Teachable Machine is provided. As well, lessons that highlight the use of Teachable Machine in middle school science are explained. Framed within Personal Construct Theory, an analysis of the impact of the professional development on middle school teachers’ perceptions (n = 17) of science lessons and activities is provided. Implications for future practice and future research are described.

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Implementation of Machine Learning Using Google's Teachable Machine Based on Android

Cited by 8 publications

References 8 publications

Teachable Machine: Deteksi Dialek Sumba Timur (Kambera) Menggunakan Layanan Open Source

Teachable Machine: Deteksi Dialek Sumba Timur (Kambera) Menggunakan Layanan Open Source

Detection and Classification of Personally Identifiable Information in Images Using Artificial Intelligence

The Impact of Teachable Machine on Middle School Teachers’ Perceptions of Science Lessons after Professional Development

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