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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Journal of Computing Theories and Applications Scientific Journal of Informatics Journal of Future Artificial Intelligence and Technologies
Islam, Hussain Md Mehedul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : Jurnal Teknik Informatika (JUTIF)

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Comparative Analysis of Supervised Learning Algorithms for Delivery Status Prediction in Big Data Supply Chain Management Apnena, Riri Damayanti; Ginting, Gerinata; Sudrajat, Ari; Islam, Hussain Md Mehedul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4689

Abstract

This study addresses the problem of predicting delivery status in supply chain data, a critical task for optimizing logistics and operations. The dataset, which includes multiple features like order details, product specifications, and customer information, was pre-processed using oversampling to address class imbalance, ensuring that the model could handle rare cases of late or canceled deliveries. The data cleaning process involved handling missing values, removing irrelevant columns, and transforming categorical variables into numerical formats. After pre-processing and cleaning, five machine learning models were applied: Logistic Regression, Random Forest, SVM, K-Nearest Neighbors (KNN), and XGBoost. Each model was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results showed that XGBoost outperformed the other models, achieving the highest accuracy and providing the most reliable predictions for the delivery status. This makes XGBoost the best choice for supply chain data analysis in this context. This study contributes to the growing application of machine learning in supply chain optimization by identifying XGBoost as a robust model for delivery status prediction in large datasets. For future research, exploring hybrid models and advanced feature engineering techniques could further improve prediction accuracy and address additional challenges in supply chain optimization, especially in the context of real-time data processing and dynamic supply chain environments.  
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 Adityawan, Harish Trio Ajib Susanto Akbar, Ismail Apnena, Riri Damayanti Ari Sudrajat Chaerul Umam Christy Atika Sari De Rosal Ignatius Moses Setiadi Dian Kristiawan Nugroho Doheir, Mohamed Eko Hari Rachmawanto Farroq, Omar Gerinata Ginting Guruh Fajar Shidik Herowati, Wise Heru Lestiawan Kusumawati, Yupie Mulyono, Ibnu Utomo Wahyu Muslikh, Ahmad Rofiqul Nugroho, Sandy Pulung Nurtantio Andono Rachman, Rahadian Kristiyanto Sarker, Md Kamruzzaman Stefanus Santosa Trisnapradika, Gustina Alfa Wellia Shinta Sari