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All Journal SMATIKA RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Building of Informatics, Technology and Science ISTEK
Firdaus, Muhammad Deden
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering

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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.
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.
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.
Klasifikasi Fake dan Real Menggunakan Vision Transformer dan EfficientNet-B0 pada Gambar Asli dan Generatif AI Aria, M. Syahrul Anwar; Slamet, Cepy; Firdaus, Muhammad Deden
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1531

Abstract

Advances in artificial intelligence (AI) technology have enabled the creation of synthetic images that resemble real images, posing challenges in detecting and classifying such images. This study aims to develop an EfficientNet-B0 and Vision Transformer (ViT) based classification model to distinguish between real images and images generated by generative AI. The data used consists of 30,401 original images from the MSCOCO 2017 dataset and 30,401 generative AI-generated images from the SyntheticEye AI-Generated Images Dataset on Kaggle. The results showed that the ViT model achieved 98% accuracy and EfficientNet-B0 achieved 96% accuracy in classifying the images. The conclusion of this research is that both models have great potential in detecting digital media manipulation, with ViT showing superior performance. The practical implication of this research is the development of more advanced technologies for detecting generative images, which can be used in various real applications such as digital security and media verification.
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.
Implementasi Teknologi Blockchain dalam Pengembangan Aplikasi Web Terdesentralisasi untuk Pengelolaan Data Pos Pelayanan Terpadu: Studi Kasus: Posyandu Mawar Lingkungan Gibug Qomaruddin, Nurhadi; Gerhana, Yana Aditia; Taufik, Ichsan; Slamet, Cepy; Firdaus, Muhammad Deden
ISTEK Vol. 14 No. 1 (2025)
Publisher : Fakultas Sains dan Teknologi UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/istek.v14i1.2112

Abstract

The Integrated Service Post (Posyandu) is a community-based health service established by the government, playing an important role in monitoring child health, including efforts to reduce infant and child mortality rates. However, data management at Posyandu is generally still conducted manually using paper-based records, making it prone to data loss and inefficient in terms of access and tracking. One common approach to overcoming these challenges is the use of distributed data systems, which allow data storage and processing to occur across multiple computers in different locations. Nevertheless, many of these systems still rely on centralized servers, making them vulnerable to data breaches and manipulation due to the single point of storage. To address this issue, this research proposes the development of a decentralized web application based on blockchain technology as a solution for secure, transparent, and traceable data management. The application is developed using smart contracts written in Solidity, deployed on the Ethereum blockchain, with Hardhat as the backend framework and React.js as the user interface. The system was developed using a prototyping methodology and evaluated through black-box testing to assess its functional performance. Test results show that the application is capable of managing data effectively, while maintaining a high level of security and transparency. By adopting blockchain technology, the system enhances the effectiveness and efficiency of Posyandu’s data management, while ensuring data integrity and traceability within a decentralized environment.
Co-Authors Aldy Rialdy Atmadja Aria, M. Syahrul Anwar Arkaan, Shabiq Ghazi Cepy Slamet Fadlilah, Muhammad Furqon Ichsan Taufik, Ichsan Jumadi Jumadi Nurfiani, Indri Pasha, Muhammad Kemal Qomaruddin, Nurhadi Yana Aditia Gerhana, Yana Aditia