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All Journal Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah EXPLORE Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen EXPLORE Jurnal Rekayasa Sistem Informasi dan Teknologi Journal of Data Analytics, Information, and Computer Science (JDAICS) Jurnal Pengabdian Masyarakat Ilmu Komputer Jurnal Computer and Technology
Karim, Muh Nasirudin
Universitas Teknologi Mataram
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Mathematics Social Sciences Other

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FINE-TUNING RESNET50V2 WITH ADAMW AND ADAPTIVE TRANSFER LEARNING FOR SONGKET CLASSIFICATION IN LOMBOK Wahyudi, Erfan; Imran, Bahtiar; Zaeniah; Erniwati, Surni; Karim, Muh Nasirudin; Muahidin, Zumratul
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6485

Abstract

This study aims to develop a classification system for traditional Lombok songket fabric patterns using the ResNet50V2 architecture, optimized through fine-tuning and the AdamW optimizer. The data were collected directly from songket artisans in Lombok and categorized into three groups based on the origin of the patterns: Sade, Sukarara, and Pringgasela. The model was trained with data augmentation techniques, including rotation, shifting, and zooming, to increase data diversity. During the training process, fine-tuning was applied to the last layer of ResNet50V2, and optimization was performed using AdamW with a learning rate of 0.0001. The model was evaluated using a confusion matrix, classification report, and analysis of accuracy and loss. The experimental results showed that the model achieved 100% accuracy at the 15th epoch. Furthermore, experiments with different parameters (epochs, batch size, and learning rate) demonstrated that the 15th epoch provided the best results with 100% accuracy, while using higher epochs (30 and 40) did not necessarily yield better outcomes. This model is effective in identifying songket fabric patterns with good classification results for each class. Although the results are excellent, increasing the dataset size and exploring more complex model architectures could further enhance performance. Overall, this study demonstrates the significant potential of deep learning technology in classifying songket patterns with reliable accuracy in real-world applications.
PENERAPAN ARTIFICIAL NEURAL NETWORK DAN SUPPORT VECTOR MACHINE UNTUK KLASIFIKASI KUALITAS MUTIARA KHAS LOMBOK BERDASARKAN CIRI VISUAL Karim, Muh Nasirudin; Efendi, Muhammad Masjun; Muahidin, Zumratul
Journal Computer and Technology Vol. 3 No. 1 (2025): Juli 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v3i1.336

Abstract

Penelitian ini bertujuan untuk mengklasifikasikan citra mutiara Lombok berdasarkan bentuk, ukuran, dan kecacatan menggunakan metode pengolahan citra dan kecerdasan buatan. Proses segmentasi citra dilakukan menggunakan metode thresholding untuk memisahkan objek mutiara dari latar belakang, kemudian dilanjutkan dengan deteksi tepi menggunakan metode Canny guna mempermudah ekstraksi fitur. Fitur morfologis seperti area, perimeter, roundness, diameter, serta cacat bentuk dan warna diekstraksi menggunakan metode regionprops. Hasil ekstraksi ini kemudian digunakan sebagai variabel dalam proses klasifikasi menggunakan Jaringan Syaraf Tiruan (JST) dan dibandingkan dengan metode Support Vector Machine (SVM). Dataset yang digunakan terdiri dari 360 citra mutiara yang terbagi dalam tiga kelas: A, AA, dan AAA. Hasil klasifikasi menunjukkan bahwa metode JST menghasilkan akurasi tertinggi sebesar 98%, mengungguli SVM yang memperoleh akurasi 96%. Temuan ini menunjukkan bahwa kombinasi metode regionprops dan JST efektif dalam klasifikasi multiview citra mutiara Lombok.
Implementation of Conditional WGAN-GP, ResNet50V2, and HDBSCAN for Generating and Recommending Traditional Lombok Songket Motifs Akbar, Ardiyallah; Karim, Muh Nasirudin; Imran, Bahtiar
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10894

Abstract

Songket is a traditional Indonesian woven textile with profound cultural and aesthetic value, particularly in Lombok, where artisans continue to preserve its distinctive motifs. However, the creation of new designs is still carried out manually, requiring considerable time and relying heavily on the artisans’ creativity. This study proposes an integrated system that combines Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP), ResNet50V2, and HDBSCAN to automatically generate and recommend Lombok’s traditional songket motifs. The dataset consists of primary data collected directly from local artisans and secondary data from the BatikNitik public repository, thereby providing authentic yet diverse motif samples for training. CWGAN-GP is employed to synthesize motifs with stable and realistic structures across multiple epochs. Subsequently, ResNet50V2 is utilized for deep visual feature extraction, HDBSCAN for density-based clustering, and UMAP for two-dimensional visualization of motif distribution. The system successfully groups motifs into meaningful clusters, with the largest cluster containing consistent patterns of high aesthetic value. A recommendation mechanism is also developed to suggest up to five similar motifs from the original dataset within the same cluster, ensuring cultural relevance while fostering design innovation. Despite these promising outcomes, several limitations remain, such as the relatively small number of songket motif samples, variations in motif quality, and challenges during data collection including inconsistent lighting and non-uniform patterns. These factors affect both dataset consistency and generative performance. Nevertheless, this approach demonstrates the potential of artificial intelligence to support the preservation and innovation of cultural heritage by assisting artisans in creating and exploring new motifs more efficiently without losing their traditional identity.
Anomaly-Based DDoS Detection Using Improved Deep Support Vector Data Description (Deep SVDD) and Multi-Model Ensemble Approach Imran, Bahtiar; Samsumar , Lalu Delsi; Subki, Ahmad; Wahyuni, Wenti Ayu; Muahidin, Zumratul; Karim, Muh Nasirudin; Yani, Ahmad; M. Zulpahmi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11863

Abstract

Distributed Denial-of-Service (DDoS) attacks remain a critical threat to network infrastructure, demanding robust and efficient detection mechanisms. This study proposes an enhanced Deep Support Vector Data Description (Deep SVDD) model for unsupervised DDoS detection using the UNSW-NB15 dataset. The approach leverages a deep encoder architecture with batch normalization and dropout to learn compact latent representations of normal traffic, minimizing the hypersphere volume enclosing benign flows. Only normal samples are used during training, adhering to the unsupervised anomaly detection paradigm. The model is evaluated against five established baselines—Isolation Forest, Local Outlier Factor (LOF), One-Class SVM, Autoencoder, and a simple ensemble—using AUC, F1-score, and recall as primary metrics. Experimental results demonstrate that Deep SVDD significantly outperforms all baselines, achieving superior class separation, high detection sensitivity, and computational efficiency (0.0004 GFLOPs). Notably, while LOF exhibited a deceptively high F1-score, its AUC near 0.5 revealed poor discriminative capability, highlighting the risk of relying on single metrics. The ensemble approach failed to improve performance, underscoring the limitation of naive score averaging when weak detectors are included. Visualization of score distributions and ROC curves further confirms Deep SVDD’s ability to effectively distinguish DDoS from benign traffic. These findings affirm that representation learning in latent space offers a more reliable foundation for anomaly detection than traditional distance-, density-, or reconstruction-based methods. The proposed model presents a promising solution for real-time, low-overhead intrusion detection systems in modern network environments. Future work will explore adaptive ensembles, self-supervised pretraining, and deployment on edge devices.
Co-Authors Ahmad Khairul Anam ahmad yani Akbar, Ardiyallah Ardiyallah Akbar Astutik Zaerani Efendi, Muhamad Masjun Efendi, Muhammad Masjun erniwati, surni Fira Ria Sutardi Imran, Bahtiar Lalu Delsi Samsumar, M.Eng. Lihana M. Zulpahmi Moch Arief Soeleman Muahidin, Zumratul Purwanto Purwanto Ricardus Anggi Pramunendar Subki, Ahmad Suryadi, Emi wahyuni, wenti ayu Zaeniah Zaenudin Zaenudin