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All Journal Jurnal Rekayasa Hijau MIND (Multimedia Artificial Intelligent Networking Database) Journal Systemic: Information System and Informatics Journal REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Computing and Education Technology Journal
Kurnia Ramadhan Putra
Institut Teknologi Nasional Bandung
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

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Journal : systemic information system and informatics journal

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Sistem Pembelajaran Hukum Baca Al-Qur’an Menggunakan Algoritma LPC dan KNN Hafizh Achmad Dinan; Youllia Indrawaty N; Kurnia Ramadhan Putra
Systemic: Information System and Informatics Journal Vol. 6 No. 1 (2020): Agustus
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i1.927

Abstract

A Muslim must be able to read the verses of the Qur’an properly as taught by the Prophet, Muhammad. Reading the Qur'an in accordance with tajwid is obligatory for every Muslim, if someone reads the Qur'an without using tajwid, the law is sinful. The development of the application of learning the Tajwid of Qur’an is aimed at helping a Muslim to be good at reading the Qur’an that is good and right.Al-Fatihah is uses in this application. Learning the Tajwid of Qur’an Application is using Linear Predictive Coding (LPC) method as sound feature extraction and K-Nearest Neigbor as matching with training data. For testing the pronunciation of the 1st verse obtained data accuracy of 83.3%, the 2nd verse is 86.7%, the 3rd verse is 85%, the 4th verse is 80%, the 5th verse is 88.3%, the 6th verse is 93.3%.
Pengenalan Karakter Huruf Braille dengan Metode Convolutional Neural Network Muhammad Fahmi Herlambang; Asep Nana Hermana; Kurnia Ramadhan Putra
Systemic: Information System and Informatics Journal Vol. 6 No. 2 (2020): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i2.969

Abstract

Braille characters consists of 6 dots that are designed in such way to be a writing system to help blind people. However, learning or reading Braille characters isn’t an easy thing to do, because fingers sensitivity and understanding the writing system are needed to be able to read Braille. Therefore, there are some researches on Braille characters recognition with different methods and technologies, such as deep learning. The Convolutional Neural Network (CNN) is used. CNN method has been used in various recognition researches, such as face recognition, document analysis, image classification, etc. In this research, the CNN method is used to perform Braille characters recognition. The system performs the Braille character recognition process per character based on a model that has been trained using a dataset with the 26 Braille characters. The result of 81.54% accuracy is achieved for Braille character image acquisition with a smartphone with 0 to 4 degrees tilting and 30cm distance with training model using learning rate of 0.0001 and Adam optimizer.
Co-Authors Asep Nana Hermana Barmawi, Mira Musrini Hafizh Achmad Dinan Muhammad Fahmi Herlambang Muhammad Zuhri Catur Candra nur Fitrianti Nurhasanah, Youllia Indrawaty SATRIA NUGRAHA Sofia Umaroh