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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Bimas Islam International Journal of Islamic Khazanah
Diena Rauda Ramdania
UIN Sunan Gunung Djati Bandung
Religion Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Social Sciences Other

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Multiscale Retinex Application to Analyze Face Recognition Supriyanto, Supriyanto; Harika, Maisevli; Ramadiani, Maya Sri; Ramdania, Diena Rauda
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.668

Abstract

The main challenge that facial recognition introduces is the difficulty of uneven lighting or dark tendencies. The image is poorly lit, which makes it difficult for the system to perform facial recognition. This study aims to normalize the lighting in the image using the Multiscale Retinex method. This method is applied to a face recognition system based on Principal Component Analysis to determine whether this method effectively improves images with uneven lighting. The results showed that the Multiscale Retinex approach to face recognition's correctness was better, from 40% to 76%. Multiscale Retinex has the advantage of dark facial image types because it produces a brighter image output.
Automatic Detection of Hijaiyah Letters Pronunciation using Convolutional Neural Network Algorithm Gerhana, Yana Aditia; Azis, Aaz Muhammad Hafidz; Ramdania, Diena Rauda; Dzulfikar, Wildan Budiawan; Atmadja, Aldy Rialdy; Suparman, Deden; Rahayu, Ayu Puji
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.882

Abstract

Abstract— Speech recognition technology is used in learning to read letters in the Qur'an. This study aims to implement the CNN algorithm in recognizing the results of introducing the pronunciation of the hijaiyah letters. The pronunciation sound is extracted using the Mel-frequency cepstral coefficients (MFCC) model and then classified using a deep learning model with the CNN algorithm. This system was developed using the CRISP-DM model. Based on the results of testing 616 voice data of 28 hijaiyah letters, the best value was obtained for accuracy of 62.45%, precision of 75%, recall of 50% and f1-score of 58%.
Optimizing Quranic Literacy with the Tamam Method: Leveraging Artificial Intelligence for Handwritten Arabic Recognition Gina Giftia Azmiana Delilah; Diena Rauda Ramdania; Ichsan Budiman; Maisevli Harika
International Journal of Islamic Khazanah Vol. 15 No. 2 (2025): IJIK
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/ijik.v15i2.52522

Abstract

Indonesia, boasting the world's largest Muslim population, has witnessed a swift augmentation in its Muslim demographic. As of 2020, Muslims in Indonesia numbered 209 million, which surged to 219 million in 2021. Such an observation is alarming, especially given the Quran's centrality in Islamic teachings and the profound link between grasping its tenets and the capability to read and write its verses. This paper introduces an innovative application employing the Tamam method, optimized for enhancing Quranic literacy through the recognition of handwritten Arabic texts using Convolutional Neural Networks (CNN). Involving a cohort of 144 participants, who answered 65 questions, a dataset encompassing 3,842 data points was curated for testing and validation. Preliminary results showcased the model's evolution, with a notable rise in accuracy from 14.27% in the initial epoch to 88.87% in the 20th epoch. Despite such advancements, fluctuations in the validation data hinted at potential overfitting scenarios. This study demonstrates the feasibility of integrating the Tamam method with AI-based handwritten Arabic recognition as a supportive tool for Quranic writing practice. It paves the way for more resilient and adaptive Quranic educational tools, ensuring learners grasp the Holy Text in its true essence.
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 Aaz Muhammad Hafidz Azis Agung Wahana Agus Sulaeman Aldy Rialdy Atmadja Ayu Puji Rahayu Azis, Aaz Muhammad Hafidz Busro, B. Cepy Slamet Deden Suparman, Deden Dhanis Al Ghifari Dzulfikar, Wildan Budiawan Fahmi Faturahman Nul Hakim Faiz M. Kaffah Ferry Feirizal Fitri Diani Gina Giftia Azmiana Delilah Gina Giftia Azmiana, Gina Giftia Ichsan Budiman Ichsan Budiman Indah Sri Lestari, Indah Sri Irfan Safrudin Jumadi Jumadi Khaerul Manaf Kokoy Siti Komariah Maisevli Harika Maisevli Harika Maisevli Harika Maylawati, Dian Sa'adillah Mintarsih, Mimin Mi’raj Fuadi Mohamad Irfan Muhammad Ali Ramdhani Muhammad Fasya Adzkia Muhammad Insan Al-Amin Nurfiani, Indri Rahmadika, Sandi Ramadiani, Maya Sri Rifaldo Sukma Hidayat Rohmat Mulyana Sapdi Safira Oktarini Salma Nuralisa Habsah Sandi Rahmadika Setyawan, Alfin Yogi Supriyanto Supriyanto Taufik, Ichsan Undang Syaripudin, Undang Wildan Budiawan Zulfikar Wisnu Uriawan, Wisnu Yana Aditia Gerhana Yana Aditia Gerhana, Yana Aditia Yudi Widhiyasana