<p><em>Abstrak</em><strong> </strong>- <strong>Pelajaran utama dalam membaca Al Qur'an adalah mengenali dan </strong><strong>melafalkan</strong><strong> huruf-huruf Hijaiyah. Beberapa fakta menunjukkan bahwa pengucapan yang salah dapat memengaruhi makna secara har</strong><strong>a</strong><strong>fiah. <em>Speech Recognition</em>, sebagai teknologi saat ini, dapat digunakan untuk memeriksa kesalahan dalam melafalkan surat Hijaiyah melalui pengenalan suara atau ucapan. Itu dapat dikonversi menjadi data yang dapat dipahami oleh sistem. Tujuan dari penelitian ini adalah untuk menerapkan <em>Speech Recognition</em> dengan <em>Hidden Markov Model</em> untuk pelafalan huruf Hijaiyah ketika belajar membaca Alquran. Pengenalan ucapan dan Model Hidden Markov dilakukan untuk mengembangkan sistem antar</strong><strong> </strong><strong>muka mesin berbasis suara. Dalam penelitian ini juga menggunakan metode <em>Fast Fourier Transform</em> (FFT) untuk mengekstraksi sifat. <em>Hidden Markov Model</em> (HMM) yang digunakan dalam proses pelatihan. Juga, menghasilkan karakteristik khusus untuk setiap huruf Hijaiyah. Dan kemudian, <em>Euclidean Distance</em> (ED) untuk klasifikasi akhir dalam mendeteksi pelafalan huruf Hijaiyah. Hasil penelitian menunjukkan bahwa hasil tes huruf Hijaiyah pada tingkat akurasi yang sama adalah 100%, sedangkan pengujian huruf yang berbeda adalah 54,6%. Dengan demikian, penelitian ini akan memberikan kontribusi kepada siswa yang sedang belajar membaca Al-Qur'an untuk dapat mengenali dan me</strong><strong>lafalkan</strong><strong> huruf-huruf Hijaiyah</strong><strong><em>.</em></strong></p><p><em>Abstract</em> – <strong>The main lesson in reading the Al Qur'an is recognizing and reciting the letters Hijaiyah. Some facts show that incorrect pronunciation can affect meaning literally. Speech Recognition, as the current technology, can be used to check the mistakes in pronouncing the Hijaiyah's letter through recognizing the voice or speech. It can convert into data that can be understood by the system. The purpose of this study is to implement Speech Recognition with Hidden Markov Model for Hijaiyah letter pronunciation when learning to read the Qur'an. Speech recognition and Hidden Markov Models were carried out to develop a sound-based machine interface system. In this study also used the Fast Fourier Transform (FFT) method to extract traits. Hidden Markov Model (HMM) used in the training process. Also, produced the especially characteristics for each letter of Hijaiyah. And then, Euclidean Distance (ED) for the final classification in detecting Hijaiyah letter pronunciation. The results of the study show that the results of the Hijaiyah letter test on the same level of accuracy are 100%, while the testing of different letters is 54.6%. Thus, this study will contribute to students who are learning to read Al-Qur'an to be able to recognize and recite the Hijaiyah letters</strong><strong><em>.</em></strong></p><p><strong><em>Keywords</em></strong> - <em>Speech Recognition, Hidden Markov Model, Recognizing, Reciting, Letter Hijaiyah </em></p>
Damerau Levenshtein Distance (DLD) is an algorithm for writing-error correction. The errors happened due to insertions, deletion, exchange, and substitution of alphabet within a word. It may occur because of the loss of space between two words. DLD alone is unable to overcome the loss psace problems. Thus, this paper aims to combine DLD with Empirical Method to fix this specific error. As a result, the combination algorithm may outperform the original DLD in checking the spelling errors of Indonesian Text with an accuracy of 97%.
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