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All Journal Jurnal Simetris Jurnal Transformatika JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Innovation Studies Jurnal Algoritma Moneter : Jurnal Keuangan dan Perbankan Scientific Journal of Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) INOVTEK Polbeng - Seri Informatika
Chaerul Umam
Jurusan Teknik Kimia, Universitas Sultan Ageng Tirtayasa, Cilegon-Banten, Indonesia
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 Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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IMPLEMENTATION OF LSTM (LONG SHORT TERM MEMORY) ALGORITHM TO PREDICT WEATHER IN CENTRAL JAVA Irwan, Rhedy; Andono, Pulung Nurtantio; Al Zami, Farrikh; Ocky Saputra, Filmada; Megantara, Rama Aria; Handoko, L. Budi; Umam, Chaerul
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1118

Abstract

Agro-indutrial agricultural production such as red onions in Indonesia has a very important share in driving Indonesia's economic growth, especially in Central Java province which contributed 28.15% of the total national red onion production in 2021. Weather conditions have a major influence on the red onion planting process until the red onions are ready to be harvested. In this study, the objective is to predict various types of weather such as rainfall, air temperature, and air humidity in seven districts in Central Java, namely Brebes, Temanggung, Demak, Boyolali, Kendal, Pati, and Tegal. To do this, the use of the LSTM (Long Short Term Memory) algorithm with its ability to store memory longer than RNN will be reliable for predicting various types of weather in the future. This research was developed with the CRISP-DM (Cross Industry Process Model for Data Mining) method which has a goal-oriented approach, this method is a mature and widely accepted method in Data Mining with various applications in Machine Learning. With the final results from 39 models by using the evaluation of the average value of train MSE 0.013, test RMSE 0.11, test MSE of 0.02, test RMSE 0.12 and succeed to predict 5 days or months ahead from the last data that is provided.
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.
Co-Authors Abdussalam Abdussalam, Abdussalam Al zami, Farrikh Alzami, Farrikh ARIANTO, EKO De Rosal Ignatius Moses Setiadi Eko Hari Rachmawanto Ervikhan, Muhammad Ibnu Firman Wahyudi, Firman Guruh Fajar Shidik Heru Lestiawan Irwan, Rhedy Isinkaye, Folasade Olubusola Islam, Hussain Md Mehedul L. Budi Handoko Megantara, Rama Aria Nugroho, Agung Nur Hariyanto, Mohammad Malik Ocky Saputra, Filmada Pulung Nurtantio Andono Reza Pahlevi, Mohammad Rizky Saputra, Filmada Ocky Saputra, Maulana Bagus Saputri, Pungky Nabella Sinaga, Daurat Sinaga, Daurat Sya'bani, Hafiidh Akbar Wellia Shinta Sari Wicaksana, Fajar Adji Widyanto Utomo, Arya Wildanil Ghozi