Classification of the Correct Quranic Letters Pronunciation of Male and Female Reciters

Khairuddin, Safiah; Ahmad, Salmiah; Embong, Abdul Halim; Hashim, Nik Nur Wahidah Nik; Altamas, Tareq M.K.; Badaruddin, Syarifah N. S.; Hassan, Surul Shahbudin

doi:10.1088/1757-899x/260/1/012004

Cited by 8 publications

(6 citation statements)

References 4 publications

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“…Salah satu fungsi yang banyak diambil dari al-Quran adalah estetika bacaan yang terkandung dalam setiap susunan sintaksis setiap ayat al-Qur'an. Estetika bacaan itulah yang dikenal dengan tilawah gaya mujawwad [1]. Selama ini pencapaian seseorang dalam hal membaca al-Quran khusunya dalam seni membaca al-Quran atau yang disebut dengan tilawah gaya mujawwad masih minim.…”

Section: Kata Kunciunclassified

Identifikasi Irama Tilawah al-Quran dengan Gaya Mujawwad Menggunakan Naive Bayes Classifier

2022

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Selama ini, pencapaian seseorang dalam hal membaca al-Quran dengan tilawah gaya mujawwad perlu untuk ditingkatkan. Hal ini disebabkan adanya masalah dalam hal pemahaman membedakan jenis irama dalam seni membaca al-Quran. Menurut para ahli qurro di Indonesia, irama bacaan al-Quran terbagi menjadi tujuh macam bagian dan menjadi pilar dasar tilawah gaya mujawwad, yakni: Bayati, Shoba, Hijaz, Nahawand, Rost, Jiharkah, dan Sikah. Oleh karenanya, penelitian ini mengusulkan sistem cerdas menggunakan naïve bayes classifier yang diharapkan dapat membantu dalam identifikasi jenis irama tilawah berbasis gaya mujawwad. Hasil penelitian ini diperuntukkan bagi para pentilawah gaya mujawwad yang ingin mengetahui jenis irama tilawah yang dibacakan. Dalam pengembangannya, dataset didapatkan dari 10 pentilawah yang membacakan masing-masing irama untuk tiga ayat al-Quran yang berbeda, sehingga didapatkanlah data irama dalam ekstensi waveform audio format sejumlah 210 data irama. Tahapan metode dalam penelitian ini antara lain: 1) identifikasi masalah; 2) pengumpulan data tilawah gaya mujawwad; 3) validasi data tilawah gaya mujawwad; 4) ekstraksi fitur data tilawah gaya mujawwad; 5) imlementasi naïve bayes classifier; kemudian diakhiri dengan 6) pengujian. Pengujian dilakuman terhadap 63 data uji. Diperoleh hasil bahwa terdapat 36 data rekaman diidentifikasi dengan benar dan terdapat 27 data uji yang salah identifikasi. Maka didapatkan prosentase akurasi adalah 56,7%, prosentase presisi sebesar 56,8% serta prosentase recall adalah 33,4%.

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Section: Kata Kunciunclassified

Identifikasi Irama Tilawah al-Quran dengan Gaya Mujawwad Menggunakan Naive Bayes Classifier

2022

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“…The approach of analysis concerns on sequences of voiced or unvoiced sound because of recitation are related to phonetic and prosody. The sequences are segmented in a series of frames and represented by formant frequencies [3]and [4]. The production of voice or speech involves the movement of air from the lung to vocal tract towards the lips.…”

Section: Momentous Fragmentary Mfcc-formantsmentioning

confidence: 99%

Analysis of Momentous Fragmentary Formants in Talaqi-like Neoteric Assessment of Quran Recitation using MFCC Miniature Features of Quranic Syllables

Adam¹,

Shafie²,

Abas³

et al. 2021

IJACSA

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The use of technological speech recognition systems with a variety of approaches and techniques has grown rapidly in a variety of human-machine interaction applications. Further to this, a computerized assessment system to identify errors in reading the Qur'an can be developed to practice the advantages of technology that exist today. Based on Quranic syllable utterances, which contain Tajweed rules that generally consist of Makhraj (articulation process), Sifaat (letter features or pronunciation) and Harakat (pronunciation extension), this paper attempts to present the technological capabilities in realizing Quranic recitation assessment. The transformation of the digital signal of the Quranic voice with the identification of reading errors (based on the Law of Tajweed) is the main focus of this paper. This involves many stages in the process related to the representation of Quranic syllable-based Recitation Speech Signal (QRSS), feature extraction, non-phonetic transcription Quranic Recitation Acoustic Model (QRAM), and threshold classification processes. MFCC-Formants are used in a miniature state that are hybridized with three bands in representing QRSS combined vowels and consonants. A human-guided threshold classification approach is used to assess recitation based on Quranic syllables and threshold classification performance for the low, medium, and high band groups with performances of 87.27%, 86.86%and 86.33%, respectively.

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“…Besides, ways to evaluate different mental disorders need a great effort on part of specialists due to the lack of availability of patients for obtaining the necessary information. This means that it largely depends on the patient’s efforts to cooperate in communicating their symptoms and problems [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Convolutional Neural Network for Depression Episodes Detection in Real Time Using Motor Activity Time Series of Depresjon Dataset

et al. 2022

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Depression is a common illness worldwide, affecting an estimated 3.8% of the population, including 5% of all adults, in particular, 5.7% of adults over 60 years of age. Unfortunately, at present, the ways to evaluate different mental disorders, like the Montgomery–Åsberg depression rating scale (MADRS) and observations, need a great effort, on part of specialists due to the lack of availability of patients to obtain the necessary information to know their conditions and to detect illness such as depression in an objective way. Based on data analysis and artificial intelligence techniques, like Convolutional Neural Network (CNN), it is possible to classify a person, from the mental status examination, into two classes. Moreover, it is beneficial to observe how the data of these two classes are similar in different time intervals. In this study, a motor activity database was used, from which the readings of 55 subjects of study (32 healthy and 23 with some degree of depression) were recorded with a small wrist-worn accelerometer to detect the peak amplitude of movement acceleration and generate a transient voltage signal proportional to the rate of acceleration. Motor activity data were selected per patient in time-lapses of one day for seven days (one week) in one-minute intervals. The data were pre-processed to be given to a two-dimensional convolutional network (2D-CNN), where each record of motor activity per minute was represented as a pixel of an image. The proposed model is capable of detecting depression in real-time (if this is implemented in a mobile device such as a smartwatch) with low computational cost and accuracy of 76.72% In summary, the model shows promising abilities to detect possible cases of depression, providing a helpful resource to identify the condition and be able to take the appropriate follow-up for the patient.

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Classification of the Correct Quranic Letters Pronunciation of Male and Female Reciters

Cited by 8 publications

References 4 publications

Identifikasi Irama Tilawah al-Quran dengan Gaya Mujawwad Menggunakan Naive Bayes Classifier

Identifikasi Irama Tilawah al-Quran dengan Gaya Mujawwad Menggunakan Naive Bayes Classifier

Analysis of Momentous Fragmentary Formants in Talaqi-like Neoteric Assessment of Quran Recitation using MFCC Miniature Features of Quranic Syllables

Two-Dimensional Convolutional Neural Network for Depression Episodes Detection in Real Time Using Motor Activity Time Series of Depresjon Dataset

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