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All Journal Jurnal Algoritma JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JTT (Jurnal Teknologi Terpadu) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research Jurnal Algoritma Journal of Law, Poliitic and Humanities SENTRI: Jurnal Riset Ilmiah Coreid Journal Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Integrated : Journal Of Information Technology and Vocational Education
Aldy Rialdy Atmadja
UIN Sunan Gunung Djati Bandung
Aerospace Engineering Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Rekognisi Tulisan Kaligrafi Dengan Menggunakan Metode Convolutional Neural Network Arsitektur MobileNetv2 Irhamnillah, Sami; Atmadja, Aldy Rialdy; Taufik, Ichsan
Jurnal Teknologi Terpadu Vol 13, No 2 (2025): JTT (Jurnal Terpadu Terpadu)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32487/jtt.v13i2.2616

Abstract

This research aims to develop an automatic classification model to recognize the type of Arabic calligraphy writing using MobileNetV2 Convolutional Neural Network (CNN) architecture. Arabic calligraphy has a visual uniqueness and complexity of letterforms that become a challenge in the classification process, especially for ordinary people. The four main calligraphy types used in this research are Tsulust, Naskhi, Diwani, and Kufi. The research follows the CRISP-DM stages which include business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset used is the HICMA dataset consisting of 5,031 calligraphy images. The data is processed through cropping, normalization, and resizing to 224x224 pixels. The model was trained with epoch variations (10, 20, 30, and 40) to obtain the best configuration. The results show that the model at the 20th epoch has the most optimal performance with a testing accuracy of 97.52%. Evaluation of classification metrics showed high F1-Score values in the majority classes. The previously low-performing Kufi class was improved through data augmentation techniques to obtain an F1-Score value of 0.99. The model is then integrated into a Flask-based web application that allows users to upload images and receive classification results directly. The results of this research show that MobileNetV2 is effective for Arabic calligraphy type classification and can be practically implemented for educational purposes as well as digital preservation of Islamic culture.
Sistem Pendukung Keputusan Penentu Dosen Penguji Dan Pembimbing Tugas Akhir Menggunakan Fuzzy Multiple Attribute Decision Making dengan Simple Additive Weighting (Studi Kasus: Jurusan Teknik Informatika UIN SGD Bandung) Septiana, Ian; Irfan, Mohamad; Atmadja, Aldy Rialdy; Subaeki, Beki
JOIN (Jurnal Online Informatika) Vol 1 No 1 (2016)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v1i1.10

Abstract

Penentuan dosen penguji dan pembimbing skripsi adalah hal yang harus dilakukan disetiap universitas untuk membantu mahasiswa dalam menyelesaikan skripsinya. Dalam menentukan hal tersebut kadang terjadi keputusan yang kurang optimal dimana dosen yang ditunjuk kurang sesuai dengan topik skripsi mahasiswa akibatnya dapat mengurangi kualitas karya ilmiah mahasiswa. Untuk memecahkan masalah tersebut maka dibutuhkan sistem pendukung keputusan yang dapat memberikan rekomendasi dosen penguji dan pembimbing. Salah satu metode yang dapat digunakan adalah FMADM (Fuzzy Multiple Attribute Decission Making). Proses penentuan rekomendasi dosen penguji dan pembimbing dilakukan dengan mencari alternatif terbaik berdasarkan kriteria-kriteria yang telah ditentukan melalui metode SAW (Sample Additive Weighting). Adapun metode FMADM dipilih karena mampu menyeleksi alternatif terbaik dari sejumlah alternatif. Dengan mencari nilai bobot untuk setiap atribut, kemudian dilakukan proses perangkingan yang menghasilkan alternatif yang optimal, untuk menentukan dosen penguji dan pembimbing
Comparison of Template Matching Algorithm and Feature Extraction Algorithm in Sundanese Script Transliteration Application using Optical Character Recognition Gerhana, Yana Aditia; Atmadja, Aldy Rialdy; Padilah, Muhamad Farid
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.580

Abstract

The phenomenon that occurs in the area of West Java Province is that the people do not preserve their culture, especially regional literature, namely Sundanese script, in this digital era there is research on Sundanese script combined with applications using Feature Extraction algorithm, but there is no comparison with other algorithms and cannot recognize Sundanese numbers. Therefore, to develop the research a Sundanese script application was made with the implementation of OCR (Optical Character Recognition) using the Template Matching algorithm and the Feature Extraction algorithm that was modified with the pre-processing stages including using luminosity and thresholding algorithms, from the two algorithms compared to the accuracy and time values the process of recognizing digital writing and handwriting, the results of testing digital writing algorithm Matching algorithm has a value of 87% word recognition accuracy with 236 ms processing time and 97.6% character recognition accuracy with 227 ms processing time, Feature Extraction has 98% word recognition accuracy with 73.6 ms processing time and 100% character recognition accuracy with 66 ms processing time, for handwriting recognition in feature extraction character recognition has 83% accuracy and 75% word recognition , while template matching in character recognition has an accuracy of 70% and word recognition has an accuracy of 66%.
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%.
Evaluating End-to-End ASR for Qur'an Recitation Using Whispers in Low Resource Settings Abdullah Azzam; Ichsan Taufik; Aldy Rialdy Atmadja
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.561

Abstract

This study investigated the use of End-to-End Automatic Speech Recognition (E2E ASR) for Qur'an recitation under low resource conditions using the Whisper model. This study follows the CRISP-DM methodology, starting with defining the research gap and preparing a curated dataset of 200 verses from Juz 30. These verses were chosen because of their short and consistent structure, allowing for efficient experimentation. Audio and transcription pairs are verified and cleaned to ensure alignment and quality. The modeling was done using Whisper in Google Colaboratory, leveraging its pre-trained architecture to reduce training time and computing costs. Evaluations use the Character Error Rate (CER) metric to measure transcription accuracy. The results showed that Whisper achieved an average CER of 0.142, corresponding to a transcription accuracy of about 85%. However, the average processing time per father is 11 seconds, almost double the time it takes for a human readout. Although Whisper provides strong accuracy for Arabic transcription, its runtime efficiency remains a challenge in real-time applications. This research contributes reproducible channels, validated datasets, and performance benchmarks for future studies of the Qur'anic ASR under computational constraints.
Analysis of Living Organism in Arabic Vocabulary Meaning Perspective Biology and Lexical Meaning Akmaliyah, Akmaliyah; Teti Ratnasih; Ayuni Adawiyah; Aldy Rialdy Atmadja; Amiq; Hendar Riyadi
Journal of Law, Politic and Humanities Vol. 3 No. 2 (2023): (JLPH) Journal of Law, Politic and Humanities (February 2023)
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jlph.v3i2.177

Abstract

Most of Arabs people has spoken Arabic for communication. The Arabic word has a meaning to express feelings and interact socially. Lexical meaning in Arabic word has related to a specific of gender and a characteristic of living organism. The purpose of this study was to observed several meanings from the Arabic words and their relationship. In this study, semantic approach are used to analyze words lexically to find the meanings that indicate the characteristics and gender of living organism. The study found that the Arabic word has a real and a derivative meaning. As in the word father which in Arabic means Al-Abu, but it has a derivative meaning rooster. A rooster have similar character and behavior as a father in the role of the group and family member. Derivative meaning and reak meaning show that have a close relationship observed from characteristic and behavior of a living organism.
Klasifikasi Tulisan Tangan Huruf Hijaiyah Anak Usia 6-8 Tahun Menggunakan Metode Support Vector Machine Roofiad, Ahmad Maulidi; Alam, Cecep Nurul; Atmadja, Aldy Rialdy
SENTRI: Jurnal Riset Ilmiah Vol. 4 No. 12 (2025): SENTRI : Jurnal Riset Ilmiah, Desember 2025
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v4i12.5077

Abstract

This study aims to develop a handwritten Hijaiyah letter classification system for children aged 6–8 years using the Support Vector Machine (SVM) algorithm. The main problem in elementary education is the difficulty children face in recognizing and writing Hijaiyah letters due to the similarity of their shapes and variations in handwriting. The research process uses the CRISP-DM stages, consisting of problem understanding, data collection and preparation, modeling with SVM (GridSearch for hyperparameter tuning), and evaluation using a confusion matrix and f1-score. The dataset used consists of 2,100 images of handwritten letters from elementary school students. The results show that the SVM model with RBF kernel, C=10, and gamma="scale" achieved the highest accuracy of 83.57%. This study demonstrates that an SVM-based machine learning approach can assist in recognizing Hijaiyah letters, making it a practical solution for teachers in teaching Hijaiyah writing.
Co-Authors Aaz Muhammad Hafidz Azis Abdullah Azzam Adawiyah, Ayuni Adilla Febrina Agung Wahana Agustin, Ilham Rizky Akmaliyah Akmaliyah Alfi Dawa Mumtaazy Amiq Angelyna, Angelyna Arkaan, Shabiq Ghazi Asep Rohimat Ayu Puji Rahayu Azis, Aaz Muhammad Hafidz Beki Subaeki, Fatkhan Gunawan, Aldy Rialdy Atmadja, Beki Cecep Nurul Alam, Cecep Nurul Dede Kurniadi Deden Suparman, Deden Diena Rauda Ramdania Dini Destiani Siti Fatimah Dyka Afan Afthori Dzulfikar, Wildan Budiawan Fadlilah, Muhammad Furqon Firdaus, Muhammad Deden Fridayanti Fridayanti Hani Hamidah Hendar Riyadi Ian Septiana, Ian Ichsan Budiman Ichsan Taufik Ichsan Taufik, Ichsan Iqbal, Arif Muhamad Irhamnillah, Sami Juliansyah, Roby Jumadi Jumadi Kartamanah, Fatih Fauzan Leni Fitriani Lupi Krisrupianti Mohamad Irfan Mohamad Irfan, Mohamad Muhamad Farid Padilah Muhamad Fawaz Nurfauzan Muhammad Adam Dzulqarnain Muhammad Adi Nugraha Muhammad Dzulfi Muwaffaq Muhammad Fauzi Rachman Muhammad Luthfi Nadia Rohimah Nurfikri Habibulloh Padilah, Muhamad Farid Pamungkas, Arba Adhy Pasha, Muhammad Kemal Pebri Alkautsar Ratnasih, Teti Rifan Alamsyah Rifqi Syamsul Fuadi Roofiad, Ahmad Maulidi Sipa Almasik Sri Rahayu Sri Sulastri Wildan Budiawan Zulfikar Wisnu Uriawan Wisnu Uriawan, Wisnu Yana Aditia Gerhana Yana Aditia Gerhana Yana Aditia Gerhana, Yana Aditia Yeni Pariyatin Yeni Pariyatin Zanuar Ekaputra Rus’an