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All Journal TEKNIK INFORMATIKA TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Algoritma JURNAL ISTEK SMATIKA JOIN (Jurnal Online Informatika) Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada Masyarakat Informatics and Digital Expert (INDEX) Jurnal Algoritma Cendekia: Jurnal Kependidikan dan Kemasyarakatan ISTEK Smatika Jurnal : STIKI Informatika Jurnal
Cepy Slamet
Universitas Islam Negeri Sunan Gunung Djati Bandung
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Social Sciences Other

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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.
Co-Authors , Partono Ade Sutedi Aditya Mahmud Faza Aditya Mahmud Faza, Aditya Mahmud Aedah binti Abdul Rahman Ahmad Nurul Hak Ahmad Taofik Ahmad Zulfikar Aldy Rialdy Atmaja Ali, Hapid Andri Ikhwana Aria, M. Syahrul Anwar Asep Deddy Supriatna Ate Susanto Dani Ginanjar Dendi Ramdani Deni Heryanto Deny Fauzy Deny Fauzy, Deny Dhami Johar Dhamiri Dian Nursantika Dian Nursantika Dian Sa’adillah Maylawati Diena Rauda Ramdania Dini Destiani Siti Fatimah Dini Destiani Siti Fatimah Egi Ferdiana Eri Satria Erwin Gunadhi Firdaus, Muhammad Deden Ichsan Taufik, Ichsan Imam Fahmi Fadillah Imam Haditama Imam Haditama, Imam Imtiyazi, R. Naila Karlina Listiya Resmi Khaerul Manaf M. Irfan M. Syahrul Anwar Aria Ma'ruf, Muh. Arif Mohamad Irfan Muhammad Ali Ramdhani Muhammad Ali Ramdhani Muhammad Deden Firdaus Muhammad Irsyad Muhammad Wisnu Wardana Munaya, Amira Popon Dauni Qomaruddin, Nurhadi Rahman, Aedah binti Abdul Randi septiansah, Muhamad Ridwan Setiawan Rina Kurniawati Salma Nuralisa Habsah Sandi, Gitarja Sansan Yusuf Alamsyah Setia Mulyawan Suhendar, Hilman Taopik Ahmad Undang Syaripudin Undang Syaripudin, Undang Wahyudin Darmalaksana Wildan Budiawan Zulfikar Wisnu Uriawan, Wisnu Yana Aditia Gerhana Yana Aditia Gerhana Yana Aditia Gerhana Yana Aditia Gerhana, Yana Aditia