Klasifikasi Pengucapan Huruf Hijaiyah Berbasis Android Menggunakan CNN dengan Fitur Mel-Spectrogram - Muqsith, Fawwaz Ijlal; Supriyati, Endang; Listyorini, Tri (Jurnal Informatika: Jurnal Pengembangan IT, 2025)
Mastery of Hijaiyah letters is a fundamental basis in learning the Qur'an, but data from the IIQ Community Service Institute 2021/2022 shows that 72.25% of the 3,111 Muslims tested have not been able to read the Qur'an properly. This research aims to develop an Android-based Hijaiyah letter pronunciation classification system using Convolutional Neural Network (CNN) with mel-spectrogram features. The research methodology includes collecting 8,904 voice samples from 53 participants at Pondok Tahfidz Yanbu'ul Qur'an Menawan, preprocessing data using MFCC techniques, developing CNN models, and implementing the system in the form of mobile applications with MVVM architecture. The test results showed promising performance with some classes achieving 100% accuracy and an average overall accuracy of 83.80%, although there were challenges in some classes such as “alif_dommah” and “ghaiin_dommah” which had an accuracy below 40%. The developed system successfully provides an interactive learning platform through the integration of mobile applications with the Flask API, but still requires further development, especially in expanding the dataset to overcome overfitting problems and improve the generalization ability of the model.