Article Per Year (5 Year)
p-Index From 2021 - 2026
3.127
P-Index
This Author published in this journals
All Journal Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Creative Information Technology Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi Informasi Cyberku Tedc Jurnal Pengabdian Kesehatan Masyarakat Mulawarman Scientific Journal of Informatics
Guruh Fajar Shidik
Universitas Dian Nuswantoro
Religion Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 31 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Jurnal Teknik Informatika (JUTIF)

 1 
COMPARATIVE ANALYSIS OF LSTM, BILSTM, GRU, CNN, AND RNN FOR DEPRESSION DETECTION IN SOCIAL MEDIA Muhammad Huda, Alam; Shidik, Guruh Fajar; Praskatama, Vincentius
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.4060

Abstract

The prevalence of mental health issues and the increasing use of social media provide an opportunity to leverage technology for early detection of depression. This study evaluates and compares five deep learning models, LSTM, BiLSTM, GRU, CNN, and RNN for detecting depressive tendencies from over 10,000 annotated social media messages. These models were trained on preprocessed data using standard techniques, including cleansing, tokenization, and padding. Evaluation metrics such as accuracy, precision, recall, and F1-score were utilized. BiLSTM emerged as the best-performing model with an accuracy of 98.45% and an F1-score of 96.37%, attributed to its bidirectional architecture for contextual analysis. In contrast, CNN achieved high precision (98.55%) but struggled with recall (15.14%), while RNN and GRU exhibited limitations in capturing complex patterns, with GRU showing no measurable performance. These findings establish BiLSTM as a robust tool for mental health monitoring. Future research could explore transformer-based models such as BERT or multilingual datasets for enhanced applicability.
Imperceptible Watermarking Using Discrete Wavelet Transform and Daisy Descriptor for Hiding Noisy Watermark Abdussalam, Abdussalam; Umam, Chaerul; Sari, Wellia Shinta; Rachmawanto, Eko Hari; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Lestiawan, Heru; Islam, Hussain Md Mehedul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4423

Abstract

This research aims at overcoming the challenge of improving security and robustness in digital image watermarking, a critical activity in protecting intellectual property against misuse and manipulation. In a move to overcome such a challenge, this work introduces a new form of watermarking that incorporates Discrete Wavelet Transform (DWT) and Daisy Descriptor, with a view to enhancing both durability and invisibility of the watermark. The proposed method embeds a noise-variant watermark into selected frequency sub-bands using DWT, while the Daisy Descriptor enhances resistance to noise-based attacks. Testing conducted with three grayscale images, namely Lena, Cameraman, and Lion, each with a resolution of 512 × 512 pixels, showed that the proposed DWT-Daisy Descriptor outperforms current methodologies, producing high Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values. In fact, in Lena, a PSNR value of 63.71 dB and an SSIM value of 1 were attained, with Cameraman having a PSNR value of 68.33 dB and an SSIM value of 1. As for attack resistivity, a high PSNR value of 50.11 dB under Gaussian attack and 55.70 dB under Salt-and-Pepper attack, with SSIM values approaching 1, confirm the robustness of the proposed scheme. This study highlights the significance of an efficient and secure watermarking technique that not only preserves image quality but also withstands various distortions, making it highly relevant for digital content protection in modern multimedia applications.
Securing Medical Images Using Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) for Image Steganography Pramudya, Elkaf Rahmawan; Handoko, L. Budi; Harjo, Budi; Sani, Ramadhan Rakhmat; Sari, Christy Atika; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Sarker, Md. Kamruzzaman
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4426

Abstract

Steganography is a technique for embedding secret information into digital media, such as medical images, without significantly affecting their visual quality. The primary challenge in medical image steganography is preserving the quality of the cover image while ensuring robustness against distortions such as compression or data manipulation attacks, which may impact diagnostic accuracy. This study proposes an enhanced steganographic method based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) to improve the security and robustness of medical image embedding. DWT decomposes the medical image into four frequency sub-bands (LL, LH, HL, HH), while SVD is applied to embed the secret image while maintaining essential medical features. Experimental results show that the proposed method achieves a PSNR value of up to 78 dB and an SSIM value approaching 1, indicating that the stego image quality is nearly identical to the original cover image. Compared to previous DCT-SVD and IWT-SVD-based approaches, the DWT-SVD method offers superior robustness and imperceptibility, particularly in preserving image quality in complex-textured medical images. This method contributes to enhancing data security in telemedicine and AI-based medical imaging applications by ensuring that sensitive medical data remains protected while preserving image integrity for diagnostic use.
Co-Authors Abdussalam Abdussalam, Abdussalam Affandy Affandy Aisyatul Karima Andrean, Muhammad Niko Andreas Wilson Setiawan Anggraini, Fitria Anhsori, Khusman Astuti, Yani Parti Azzahra, Tarissa Aura Budi Harjo Cahaya Jatmoko Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto Chaerul Umam Chaerul Umam Christy Atika Sari Dewi Pergiwati Dliyauddin, Muhammad Doheir, Mohamed Dwi Eko Waluyo Dwi Puji Prabowo, Dwi Puji Dzaky, Azmi Abiyyu Edi Noersasongko Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erlin Dolphina Erna Zuni Astuti Fafaza, Safira Alya Fajrian Nur Adnan Fakhrurrozi Fakhrurrozi, Fakhrurrozi Firmansyah, Rusmal Harun Al Azies Hayu Wikan Kinasih Heru Lestiawan I Ketut Eddy Purnama Ika Pantiawati Islam, Hussain Md Mehedul Junta Zeniarja Kusuma, Edi Jaya Kusumawati, Yupie L. Budi Handoko Lenci Aryani Megantara, Rama Aria Mochamad Hariadi Muhammad Huda, Alam Muhammad Naufal, Muhammad Ningrum, Amanda Prawita Nurmandhani, Ririn Paramita, Cinantya Pergiwati, Dewi Praskatama, Vincentius Pujiono Pujiono Pulung Nurtantio Andono Purwanto Purwanto Putra, Permana Langgeng Wicaksono Ellwid Rafsanjani, Muhammad Ivan Rahadian, Arief Ramadhan Rakhmat Sani Ramadhani, Irfan Wahyu Rastri Prathivi Ratmana, Danny Oka Ricardus Anggi Pramunendar Riri Damayanti Apnena Rohman, Muhammad Syaifur Saputra, Filmada Ocky Saraswati, Galuh Wilujeng Sarker, Md. Kamruzzaman Savicevic, Anamarija Jurcev Shier Nee Saw Sinaga, Daurat Sindhu Rakasiwi Soeleman, M. Arief Sri Winarno Swanny Trikajanti Widyaatmadja Vincent Suhartono Wahyu Adi Nugroho Wellia Shinta Sari Winarsih, Nurul Anisa Sri Yaacob, Noorayisahbe Mohd Yani Parti Astuti Zainal Arifin Hasibuan Zami, Farrikh Al Zul Azri bin Muhamad Noh