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All Journal JOIN (Jurnal Online Informatika) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Nurfiani, Indri
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Computer Science & IT Engineering

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PEMANFAATAN STFT DAN CNN DALAM PENGOLAHAN DATA SUARA UNTUK MENGKLASIFIKASIKAN SUARA BATUK Nurfiani, Indri; Jumadi, Jumadi; Deden Firdaus, Muhammad
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 9 No 2 (2024): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v9i2.4729

Abstract

This research aims to develop an automatic cough sound evaluation system to improve the accuracy of respiratory disease diagnosis. In this study, the Short-Time Fourier Transform (STFT) and Convolutional Neural Network (CNN) methods were used to classify cough sounds into dry and wet coughs. The Naïve Bayes model was then used to identify respiratory diseases based on the cough classification results. Testing was conducted using the available cough sound dataset, resulting in a cough classification accuracy of 82% and a respiratory disease identification accuracy using Naïve Bayes of 71.43%. The evaluation results indicate that the developed system can accurately classify cough types and identify diseases. This system has the potential to enhance the prevention and management of respiratory diseases in resource-limited areas and can be a significant tool in medical practice for faster and more accurate diagnoses. Furthermore, this research opens opportunities for further development in disease detection and diagnosis technology through sound analysis, providing wide-ranging benefits for society and the healthcare sector.
Convolutional Neural Networks for Measuring Service Satisfaction of Hajj Pilgrims through Facial Expression Analysis Syaripudin, Undang; Jumadi, Jumadi; Ramdania, Diena Rauda; Lestari, Indah Sri; Nurfiani, Indri; Setyawan, Alfin Yogi; Harika, Maisevli; Mintarsih, Mimin
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1677

Abstract

Facial expressions serve as important non-verbal indicators of human emotions and can be leveraged to assess satisfaction levels in service environments. In the context of Hajj and Umrah, where verbal feedback may be limited due to language barriers or cultural factors, facial expression recognition offers a non-intrusive method to evaluate service quality. This study proposes a Convolutional Neural Network (CNN)-based model to detect emotional states such as happiness and dissatisfaction through facial expressions of pilgrims. A quantitative approach was adopted, employing preprocessing techniques including normalization, augmentation, and image resizing. The CNN architecture comprised multiple convolutional, pooling, and fully connected layers. The model was evaluated using accuracy, precision, recall, and F1-score metrics. Experiments with varying batch sizes (32, 64, 128, 256) across 50 epochs revealed that the optimal performance was achieved with a batch size of 64, resulting in an accuracy of 63%, precision of 66%, recall of 60%, and F1-score of 62%. During deployment, the model correctly classified 12 out of 16 real-world images, achieving a real-time accuracy of 78%. Therefore, the deployment results should be considered preliminary. Future studies will involve larger deployment samples, n-fold stratified cross-validation to obtain statistically reliable model performance, and subgroup analyses (e.g., lighting, facial pose, age, and gender) to better understand model behavior under diverse real-world conditions. All deployment images were collected with participant consent and processed without storing biometric data. These findings suggest that CNN-based emotion recognition can support real-time service evaluation and enhance the quality of pilgrim services during the Hajj and Umrah.
Co-Authors Diena Rauda Ramdania Firdaus, Muhammad Deden Indah Sri Lestari, Indah Sri Jumadi Jumadi Maisevli Harika Mintarsih, Mimin Setyawan, Alfin Yogi Undang Syaripudin, Undang