Claim Missing Document
Check
Articles

Analisis Sentimen Hasil Transkripsi Audio Berbahasa Indonesia Menggunakan T5 (Text-to-Text Transfer Transformer) Suhendar, Hilman; Slamet, Cepy; Syaripudin, Undang
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.v15i01.1521

Abstract

In the digital era, sentiment analysis has become a vital tool for understanding public opinion, particularly from data derived from digital media such as videos. However, voice-based sentiment analysis in the Indonesian language remains uncommon. This research aims to develop the T5 model for sentiment analysis of Indonesian generated from speech using speech-to-text technology. The primary advantages of the T5 model lie in its ability to process lengthy texts, comprehend natural language context, and adapt training for specific tasks such as sentiment analysis. The research dataset was obtained from 20 YouTube videos, segmented into clips of a maximum duration of 15 seconds, resulting in a total of 300 sentences consisting of 150 positive sentiments and 150 negative sentiments. The generated text data was processed using the T5 model, which was specifically trained to detect positive and negative sentiments through the optimization of specific hyperparameters. The results demonstrated that the T5 model achieved an accuracy of 83%, with a precision of 0.85, a recall of 0.83, and an F-measure of 0.83 when tested on datasets different from the training data. This research indicates that the T5 model can be adapted for voice-based sentiment analysis in the Indonesian language with satisfactory results. These findings contribute to the development of voice-based sentiment analysis technology, which can be applied to opinion analysis or product reviews. In the future, improving the pre-processing stage and using more diverse datasets are expected to improve the overall performance of the model.
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.
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.