Main Article Content
Abstract
Every area of the community there is information, news, announcements or notifications through print media such as pictures, banners, posters and paper. However, there are many foreign languages (English) in the information which makes people not understand the language, because people only understand their own regional language (Sundanese) and the lack of media to help people understand the regional language (Sundanese) to a foreign language (English). Therefore, the author takes research on android-based text detection. Text detection or called Optical Character Recognition (OCR) is a system that can detect text into data files that can be processed in such a way. The method of collecting data or information in this study uses qualitative, the method for developing this system uses the waterfall method which has the advantage of being able to accelerate system development and using a backpropagation algorithm for text recognition. Backpropagation algorithm is an algorithm that can minimize errors, so as to make text detection more accurate. The results of the text detection accuracy are 97% and the error accuracy results are 3%, the text accuracy results are taken from a total of 14 samples, and 68 words. The results of text detection are affected by several cases such as the type of text font, the level of light in the image and the direction in which the photo or image is taken.
Keywords
Article Details
References
- Alda, M. (2019). Sistem Informasi Laundry Menggunakan Metode Waterfall Berbasis Android Pada Simply Fresh Laundry. Jurnal Teknologi Informasi, 3(2), 122. https://doi.org/10.36294/jurti.v3i2.934
- Backpropagation, B., & Perangkat, P. (2020). Perancangan Aplikasi Optical Character Recognition, 14(2), 195–202.
- Bawazir, F. Y., & Wijaya, I. G. P. S. (2021). Pengenalan Pola Tulisan Tangan Aksara Arab Menggunakan Ekstraksi Fitur Discrete Cosine Transform Dan Klasifikasi Backpropagation Artificial ( Handwritten Pattern Recognition Using Discrete Cosine Transform Feature, 3(1), 43–50.
- Carbonell, M., Fornés, A., Villegas, M., & Lladós, J. (2020). A neural model for text localization, transcription and named entity recognition in full pages. Pattern Recognition Letters, 136, 219–227. https://doi.org/10.1016/j.patrec.2020.05.001
- Edy Umar, & Titin Fatimah. (2017). Text Recognition Dengan Klasifikasi Neural Network Dan Text-To-Speech Pada Huruf Alphabet. Telematika Mkom, 9(3), 119–124.
- Ghifari, M. A. H., & Susilo, A. (2020). CORA: Aplikasi Baca Untuk Lansia Berbasis Android Menggunakan Teknologi Optical Characteristic Recognition (OCR). ISSN : 1693-3672, 6(1), 1–5.
- Ghozali, I., & Adikara, P. P. (2018). Implementasi Metode Backpropagation untuk Mengenali Teks pada Natural Scene Image. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 2(8), 2527–2533.
- Nasution, A., Efendi, B., & Kamil Siregar, I. (2019). Pelatihan Membuat Aplikasi Android Dengan Android Studio Pada Smp Negeri 1 Tinggi Raja. Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal, 2(1), 53–58. https://doi.org/10.33330/jurdimas.v2i1.321
- Nugroho, Firmanda Mulyawan Kharisma, A. P., & Wardhono, W. S. (2019). Pengembangan Aplikasi Pembelajaran Kanji menggunakan MLKit Text Recognition , Text-to-Speech dan Kanji Alive API. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(6), 5421–5429. Retrieved from j-ptiik.ub.ac.id
- Pratama, A., Hadista, A., Swedia, E. R., Cahyanti, M., Studi, P., Informatika, T., … Gunadarma, U. (2019). Menggunakan Implementasi Firebase Ml Kit Berbasis Android.
- Seminar, P., Aplikasi, N., Ernawati, S., Wati, R., Maulana, I., Bina, U., & Informatika, S. (2021). Penerapan Model Fountain Untuk Pengembangan Aplikasi Text Recognition Dan Text To Speech Berbasis Android, 178–186.
- Shadiq, J., Safei, A, & Wahyudin Ratu Loly, Aplikasi Peminjaman Kendaraan Operasional Kantor Menggunakan BlackBox Testing, P. (2021). Pengujian Aplikasi Peminjaman Kendaraan Operasional Kantor Menggunakan BlackBox Testing.5(2), 97–110.
- Utami, A. E., Nurhayati, O. D., & Martono, K. T. (2016). Aplikasi Penerjemah Bahasa Inggris – Indonesia dengan Optical Character Recognition Berbasis Android. Jurnal Teknologi Dan Sistem Komputer, 4(1), 167. https://doi.org/10.14710/jtsiskom.4.1.2016.167-177
- Yousaf, A., Khan, M. J., Khan, M. J., Javed, N., Ibrahim, H., Khurshid, K., & Khurshid, K. (2019). Size invariant handwritten character recognition using single layer feedforward backpropagation neural networks. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies, ICoMET 2019, (iCoMET). https://doi.org/10.1109/ICOMET.2019.8673459
References
Alda, M. (2019). Sistem Informasi Laundry Menggunakan Metode Waterfall Berbasis Android Pada Simply Fresh Laundry. Jurnal Teknologi Informasi, 3(2), 122. https://doi.org/10.36294/jurti.v3i2.934
Backpropagation, B., & Perangkat, P. (2020). Perancangan Aplikasi Optical Character Recognition, 14(2), 195–202.
Bawazir, F. Y., & Wijaya, I. G. P. S. (2021). Pengenalan Pola Tulisan Tangan Aksara Arab Menggunakan Ekstraksi Fitur Discrete Cosine Transform Dan Klasifikasi Backpropagation Artificial ( Handwritten Pattern Recognition Using Discrete Cosine Transform Feature, 3(1), 43–50.
Carbonell, M., Fornés, A., Villegas, M., & Lladós, J. (2020). A neural model for text localization, transcription and named entity recognition in full pages. Pattern Recognition Letters, 136, 219–227. https://doi.org/10.1016/j.patrec.2020.05.001
Edy Umar, & Titin Fatimah. (2017). Text Recognition Dengan Klasifikasi Neural Network Dan Text-To-Speech Pada Huruf Alphabet. Telematika Mkom, 9(3), 119–124.
Ghifari, M. A. H., & Susilo, A. (2020). CORA: Aplikasi Baca Untuk Lansia Berbasis Android Menggunakan Teknologi Optical Characteristic Recognition (OCR). ISSN : 1693-3672, 6(1), 1–5.
Ghozali, I., & Adikara, P. P. (2018). Implementasi Metode Backpropagation untuk Mengenali Teks pada Natural Scene Image. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 2(8), 2527–2533.
Nasution, A., Efendi, B., & Kamil Siregar, I. (2019). Pelatihan Membuat Aplikasi Android Dengan Android Studio Pada Smp Negeri 1 Tinggi Raja. Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal, 2(1), 53–58. https://doi.org/10.33330/jurdimas.v2i1.321
Nugroho, Firmanda Mulyawan Kharisma, A. P., & Wardhono, W. S. (2019). Pengembangan Aplikasi Pembelajaran Kanji menggunakan MLKit Text Recognition , Text-to-Speech dan Kanji Alive API. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(6), 5421–5429. Retrieved from j-ptiik.ub.ac.id
Pratama, A., Hadista, A., Swedia, E. R., Cahyanti, M., Studi, P., Informatika, T., … Gunadarma, U. (2019). Menggunakan Implementasi Firebase Ml Kit Berbasis Android.
Seminar, P., Aplikasi, N., Ernawati, S., Wati, R., Maulana, I., Bina, U., & Informatika, S. (2021). Penerapan Model Fountain Untuk Pengembangan Aplikasi Text Recognition Dan Text To Speech Berbasis Android, 178–186.
Shadiq, J., Safei, A, & Wahyudin Ratu Loly, Aplikasi Peminjaman Kendaraan Operasional Kantor Menggunakan BlackBox Testing, P. (2021). Pengujian Aplikasi Peminjaman Kendaraan Operasional Kantor Menggunakan BlackBox Testing.5(2), 97–110.
Utami, A. E., Nurhayati, O. D., & Martono, K. T. (2016). Aplikasi Penerjemah Bahasa Inggris – Indonesia dengan Optical Character Recognition Berbasis Android. Jurnal Teknologi Dan Sistem Komputer, 4(1), 167. https://doi.org/10.14710/jtsiskom.4.1.2016.167-177
Yousaf, A., Khan, M. J., Khan, M. J., Javed, N., Ibrahim, H., Khurshid, K., & Khurshid, K. (2019). Size invariant handwritten character recognition using single layer feedforward backpropagation neural networks. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies, ICoMET 2019, (iCoMET). https://doi.org/10.1109/ICOMET.2019.8673459