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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Khazanah Journal of Religion and Technology
Agustin, Ilham Rizky
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Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Engineering Social Sciences

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Klasifikasi Irama Murottal Al-Quran Menggunakan Metode CNN dengan Perbandingan Arsitektur ResNet50 dan VGG16 Agustin, Ilham Rizky; Wahana, Agung; Atmadja, Aldy Rialdy
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6440

Abstract

The understanding of murottal Al-Quran among the Indonesian population remains relatively limited. One contributing factor is the difficulty in distinguishing between different murottal rhythms, which requires specialized expertise. Additionally, traditional murottal learning methods necessitate direct interaction with expert teachers, which is not always accessible to everyone. These challenges highlight the importance of developing technology to assist in identifying murottal rhythms. This study developed a murottal rhythm classification model using Convolutional Neural Networks (CNN) with transfer learning, employing two popular architectures: VGG16 and ResNet50. Audio data were processed using Short-Time Fourier Transform (STFT) and Mel-Frequency Cepstral Coefficients (MFCC) feature extraction for analysis.The results showed that the ResNet50 architecture with MFCC-extracted data achieved the best performance, with a training accuracy of 92%, validation accuracy of 85%, and testing accuracy of 86%. Additionally, the model achieved precision, recall, and F1-score values of 0.87 and 0.86, indicating strong generalization capabilities. Conversely, the VGG16 architecture with STFT and MFCC-extracted data demonstrated lower accuracy compared to ResNet50. The findings are expected to provide an innovative solution for developing a self-learning system based on technology to facilitate understanding of murottal rhythms in the Al-Quran.
Prediction of Skin Diseases using Convolutional Neural Networks as an Effort to Prevent Their Spread in Islamic Boarding School Environments Agustin, Ilham Rizky; Putra, Muhammad Bayu Nurdiansyah
Khazanah Journal of Religion and Technology Vol. 1 No. 2 (2023): December
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kjrt.v1i2.296

Abstract

Skin disease is a common health problem in Islamic boarding school environments. This disease can spread quickly among students due to close contact and sharing the same facilities. Preventing the spread of skin diseases is a top priority in efforts to maintain the health and welfare of students in Islamic boarding schools. In this research, we propose the use of machine learning techniques to predict skin diseases in Islamic boarding school students. The main goal of this research is to develop a predictive model that can help identify skin diseases quickly and accurately. It is hoped that this will enable the prevention of the spread of skin diseases in the Islamic boarding school environment. The method used in this research involves the following steps: skin disease image data collection, data processing and cleaning, feature extraction from patient data, and machine learning model training and evaluation. We will use a Convolutional Neural Network (CNN) machine learning algorithm to build a predictive model. The dataset used in this research consists of images of melanoma, acne and acne skin diseases. In addition, validation will be carried out using data that has never been seen before to test the performance of the predictive model. It is hoped that the results of this research can make a significant contribution in preventing the spread of skin diseases in the Islamic boarding school environment. With accurate predictive models, health workers in Islamic boarding schools can take appropriate preventive measures to control skin diseases effectively. Apart from that, this research can also be a basis for developing a health information system that supports preventive measures for skin diseases in Islamic boarding schools more widely.
Co-Authors Agung Wahana Aldy Rialdy Atmadja Putra, Muhammad Bayu Nurdiansyah