Article Per Year (5 Year)
p-Index From 2020 - 2025
4.511
P-Index
This Author published in this journals
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 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
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

Published : 46 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 46 Documents
Search

 1   2   3   4   5 
Sistem kehadiran karyawan berbasis aplikasi mobile Zanuar Ekaputra Rus’an; Aldy Rialdy Atmadja
INTEGRATED (Journal of Information Technology and Vocational Education) Vol 4, No 1 (2022)
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/integrated.v2i1.27602

Abstract

Perusahaan di Indonesia memiliki banyak sekali karyawan. Karyawan merupakan suatu variabel yang sangat penting. Setiap perusahaan harus selalu mengontrol setiap karyawannya. Mengingat jumlah perusahaan dan karyawan akan selalu meningkat dari waktu ke waktu, pengontrolan absensi karyawan sangatlah berpengaruh terhadap sebuah perusahaan. Selain itu, masalah lain yang timbul yaitu kurangnya kontrol terhadap kehadiran karyawan, sistem kehadiran manual dapat dengan mudah dimanipulasi dan tidak terlalu efektif.  Untuk mempermudah pengontrolan karyawan tersebut diperlukan suatu sistem yang dapat dipakai oleh setiap karyawan untuk melakukan kontrol kehadiran ketika datang dan juga pulang. Sistem yang dirancang bertujuan untuk dapat memenuhi kebutuhan pengguna seperti permasalahan yang sudah dipaparkan. Disamping itu aplikasi harus bisa melakukan check in dan juga check out untuk karyawan untuk melakukan kontrol kehadiran. Sistem kehadiran ini dibuat dengan menggunakan Flutter dan diimplementasikan dalam perangkat Android. Selain itu sistem juga dibuat untuk mengontrol kehadiran karyawan, kedisiplinan karyawan, dan meningkatkan efektifitas dan efisiensi sistem kehadiran. Dalam aplikasi ini, karyawan dapat melakukan check in serta check out untuk melakukan kontrol kehadiran.
Desain interface website satpolpp dinas komunikasi informatika dan statistik (dkis) kota cirebon Alfi Dawa Mumtaazy; Aldy Rialdy Atmadja; Rifqi Syamsul Fuadi
INTEGRATED (Journal of Information Technology and Vocational Education) Vol 5, No 2 (2023)
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/integrated.v5i1.60338

Abstract

Satuan Polisi Pamong Praja (Satpol PP) menjadi bagian dari perangkat daerah yang bertugas untuk menegakan Peraturan Daerah dan menyelenggarakan ketertiban umum serta menjaga perlindungan di lingkungan masyarakat. Website baik digunakan sebuah interface yang praktis dan mudah dimengerti oleh user, dalam merancang sebuah interface yang baik tentunya tidak lepas dari sebuah perancangan UI/UX yang baik pula. Dalam perancangan desain UI prototype website Satpol PP untuk memberi solusi untuk merancang desain dengan user interface yang menarik, minimalis dan modern. Dalam perancangan ini software editing yang digunakan adalah Figma, dengan informasi dan fitur yang berisi Home/Beranda, dashboard, profil, agenda kerja (kalender kegiatan, penambahan tugas, laporan kegiatan). Dengan perancangan ini memiliki tujuan kepada user yang menggunakan dapat meningkatkan dan membantu kinerja, meningkatkan kualitas pelayanan serta kualitas sumber daya manusia yang pada akhirnya dapat mengoptimalkan kegiatan kerja dari Satpol PP dalam penegakan peraturan daerah di daerah setempat. Kesimpulan penelitian bahwa diperlukan Website Pusat Informasi yang dapat dijadikan suatu wadah agar para anggota dari Satpol PP dapat saling menerima dan menyampaikan informasi dengan lebih efisien, dilihat dari hasil data yang sudah dikumpulkan oleh pihak stakeholder dari Satpol PP Kota Cirebon.
Fake News Detection in the 2024 Indonesian General Election Using Bidirectional Long Short-Term Memory (BI-LSTM) Algorithm Arkaan, Shabiq Ghazi; Atmadja, Aldy Rialdy; Firdaus, Muhammad Deden
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 2 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i2.9987

Abstract

The advancement of information technology provides convenience, but it also brings about problems. One area affected by this is the election process in Indonesia, which has seen a rise in fake news often used to discredit political opponents. Fake news misleads the public into believing incorrect information related to the election. To address this issue, a system is needed to detect fake news in the 2024 election to help the public differentiate between true and false information. This system is developed using an artificial intelligence and deep learning approach trained to do text classification on fake news detection. The training data consists of 1999 entries obtained from the Global Fact-Check Database from turnbackhoax.id, detik.com, and cnnindonesia.com. The machine learning model is built using the Bidirectional Long Short-Term Memory (BI-LSTM) algorithm, which is suitable for processing text data. This study compares two types of feature representations: TF-IDF and contextual embeddings with the IndoBERT model. The study results in the best model for text classification with an accuracy of 92% and a loss of 42.92%, achieved by the model using TF-IDF feature representation. The implementation of this system aims to enhance the integrity of the election process by minimizing the spread of misinformation. Future work will focus on refining the model and expanding the dataset to include more diverse sources for improved accuracy and robustness.
Designing a Website for the Alumni Association using Software Development Life Cycle Iqbal, Arif Muhamad; Aldy Rialdy Atmadja
CoreID Journal Vol. 1 No. 3 (2023): November 2023
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v1i3.24

Abstract

The rapid development of technology has penetrated into various sectors, including education. Universitas Islam Negeri Sunan Gunung Djati Bandung realizes the importance of participating in technological advances to improve information and communication services. One of the efforts made is to utilize the website to introduce and manage alumni data. This research is focused on the analysis and design of the Informatics Study Program alumni website, with the hope that it can support facilities at the university and provide benefits, especially for alumni of the Informatics Department. The results showed that the development of the Alumni Association website can be done well as a means of facilities for the Alumni Association in informing its work program. In addition, website development is carried out by making front-end with ReactJS and back-end with NodeJS Express on the website can run completed and implemented quite well.
Analyzing PEGASUS Model Performance with ROUGE on Indonesian News Summarization Kartamanah, Fatih Fauzan; Atmadja, Aldy Rialdy; Budiman, Ichsan
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14303

Abstract

Text summarization technology has been rapidly advancing, playing a vital role in improving information accessibility and reducing reading time within Natural Language Processing (NLP) research. There are two primary approaches to text summarization: extractive and abstractive. Extractive methods focus on selecting key sentences or phrases directly from the source text, while abstractive summarization generates new sentences that capture the essence of the content. Abstractive summarization, although more flexible, poses greater challenges in maintaining coherence and contextual relevance due to its complexity. This study aims to enhance automated abstractive summarization for Indonesian-language online news articles by employing the PEGASUS (Pre-training with Extracted Gap-sentences Sequences for Abstractive Summarization) model, which leverages an encoder-decoder architecture optimized for summarization tasks. The dataset utilized consists of 193,883 articles from Liputan6, a prominent Indonesian news platform. The model was fine-tuned and evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric, focusing on F-1 scores for ROUGE-1, ROUGE-2, and ROUGE-L. The results demonstrated the model's ability to generate coherent and informative summaries, achieving ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.439, 0.183, and 0.406, respectively. These findings underscore the potential of the PEGASUS model in addressing the challenges of abstractive summarization for low-resource languages like Indonesian language, offering a significant contribution to summarization quality for online news content.
Integrasi Kamus Multibahasa pada Feed Forward Neural Network dan IndoBERT dalam Pengembangan Chatbot Mobile Pamungkas, Arba Adhy; Alam, Cecep Nurul; Atmadja, Aldy Rialdy; Juliansyah, Roby
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27886

Abstract

The development of digital technology drives the need for efficient and responsive communication services that support multilingual. This study aims to develop a chatbot that facilitates communication and operational tasks for users of the DigiTeam application by integrating a multilingual dictionary into the Feed Forward Neural Network (FFNN) model and IndoBERT. The research method used is CRISP-DM, a systematic approach in data exploration, preparation, modeling, and implementation. The DigiTeam application was developed using the Agile methodology to gradually enhance the features and functionalities of the application. The dataset consists of 456 patterns and 106 tags containing common and operational work-related questions. This study utilizes a multilingual dictionary with 309 words to improve the chatbot's context understanding and response accuracy to user queries. The test results show that integrating the multilingual dictionary into the FFNN and IndoBERT models yields an accuracy of 95.45% with balanced precision and recall, demonstrating the chatbot's ability to understand and respond to multilingual queries in real-time, while also improving operational efficiency and information access in the workplace.
Pemanfaatan Transformer untuk Peringkasan Teks: Studi Kasus pada Transkripsi Video Pembelajaran Fadlilah, Muhammad Furqon; Atmadja, Aldy Rialdy; Firdaus, Muhammad Deden
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6342

Abstract

Abstract−In the digital era, learning videos are increasingly being used, however, they often contain irrelevant information, making it difficult to comprehend the content. This study proposes an approach based on the Whisper and T5 models to generate text summaries from YouTube educational video transcripts. Whisper is used for speech-to-text transcription, focusing on model variants that offer a low Word Error Rate (WER) and time efficiency. Subsequently, the T5 model is fine-tuned to produce accurate text summaries, with a strategy of segmenting the transcript to address input length limitations. Text preprocessing is not applied as it resulted in better evaluation quality. The results show that the combination of Whisper Turbo and the optimized T5 model provides the best performance, with F1-Scores on the ROUGE metrics of 39.23 (ROUGE-1), 13.17 (ROUGE-2), and 23.84 (ROUGE-L). This approach successfully generates more relevant and comprehensive text summaries, enhancing the effectiveness of video-based learning. Therefore, this research makes a significant contribution to the development of text summarization technology for learning videos.
A Deep Learning Approach Using VGG16 to Classify Beef and Pork Images Zulfikar, Wildan Budiawan; Angelyna, Angelyna; Irfan, Mohamad; Atmadja, Aldy Rialdy; Jumadi, Jumadi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2848

Abstract

There are 87.2% of the Muslim population in Indonesia, which makes Indonesia one of the countries with the largest Muslim population in the world. As a Muslim, it is supposed to carry out and stay away from the commands that Allah SWT commands, one of which is in QS. Al-maidah:3, one of the commands in the verse is not to consume haram food such as pork. Even so, it turns out that many traders in Indonesia still cheat to get more significant profits, namely by counterfeiting beef and pork. The lack of public knowledge supports this situation to differentiate between the two types of meat. Therefore, the classification process is used to distinguish the two kinds of meat using the convolutional neural network approach with VGG16 with several preprocessing stages. Two primary stages are used during the preprocessing stage: scaling and contrast enhancement. The VGG16 algorithm gets very good results by getting an accuracy value of 99.6% of the test results using 4,500 images for training data and 500 images for testing data. To compare the effectiveness of these techniques, it is recommended to use alternative CNN architectures, such as mobilNet, ResNet, and GoogleNet. More investigation is also required to gather more varied datasets, enabling the ultimate goal to achieve the best possible categorization, even when using cell phone cameras or with dim or fuzzy photos.
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.
Intelligent Traffic Management System Using Mask Regions-Convolutional Neural Network Pasha, Muhammad Kemal; Atmadja, Aldy Rialdy; Firdaus, Muhammad Deden
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2233

Abstract

Urban centers worldwide continue to face challenges in traffic management due to outdated traffic signal infrastructure. This study aims to develop an intelligent traffic management system by implementing the Mask Regions-Convolutional Neural Network (MR-CNN) algorithm for real-time vehicle detection and traffic flow optimization. Utilizing the CRISP-DM framework, this research processes CCTV footage from the Pasteur-Pasopati intersection in Bandung to identify and quantify vehicles dynamically. The proposed system leverages an enhanced Mask R-CNN model with a ResNet-50 FPN backbone to improve detection accuracy. Experimental results demonstrate an 80% vehicle detection accuracy, with a macro-average precision of 0.89, recall of 0.83, and an F1-score of 0.82. These findings highlight the system’s capability to replace conventional fixed-time traffic signals with a more adaptive approach, adjusting green light durations based on real-time traffic density. The proposed solution has significant practical implications for reducing congestion and improving traffic flow efficiency in urban environments.
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 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 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