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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah EDUMATIC: Jurnal Pendidikan Informatika Indonesian Journal of Data and Science Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Buletin Sistem Informasi dan Teknologi Islam Jurnal Informatika: Jurnal Pengembangan IT International Journal of Artificial Intelligence in Medical Issues The Indonesian Journal of Computer Science
Darwis, Herdianti
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Medicine & Pharmacology Public Health

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Journal : knowledge engineering and data science

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Feature Engineering and Anchor Optimization for Enhancing Faster R-CNN Detection of Low-Contrast Steel Surface Defects Darwis, Herdianti; Nurhalimah, Sitti; Azis, Huzain
Knowledge Engineering and Data Science
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Detection of defects on low-contrast steel surfaces, especially crazing and rolled-in-scale, remains a major challenge due to their visual similarity to background patterns. Although state-of-the-art methods have achieved high accuracy through complex architectural adjustments, the contribution of preprocessing techniques has not been thoroughly investigated. This study investigates pre-processing-based improvements to Faster R-CNN by combining Bilateral Filtering to reduce noise, CLAHE to enhance local contrast, CIoU Loss for more effective bounding box regression, and customized anchor settings for irregular defect configurations. Evaluated using the NEU-DET dataset, our BF-CIoU Faster R-CNN model achieved a mAP@50 score of 72.32%, with an AP of 43.74% for crazing and 53.04% for rolled-in-scale. Although these results fall short of the performance of state-of-the-art architectures that utilize feature fusion and attention mechanisms (80.2% mAP), our approach demonstrates that preprocessing improvements alone can yield competitive baseline performance without additional architectural complexity. This study confirms the effectiveness of Bilateral Filtering and CLAHE in removing defective signals, while highlighting the need for more advanced feature-extraction modules to achieve higher accuracy. Further research will examine hybrid approaches that combine preprocessing with attention-based architectures for steel inspection systems in industry.
Backpropagation Neural Network with Combination of Activation Functions for Inbound Traffic Prediction Purnawansyah, Purnawansyah; Haviluddin, Haviluddin; Darwis, Herdianti; Azis, Huzain; Salim, Yulita
Knowledge Engineering and Data Science
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Predicting network traffic is crucial for preventing congestion and gaining superior quality of network services. This research aims to use backpropagation to predict the inbound level to understand and determine internet usage. The architecture consists of one input layer, two hidden layers, and one output layer. The study compares three activation functions: sigmoid, rectified linear unit (ReLU), and hyperbolic Tangent (tanh). Three learning rates: 0.1, 0.5, and 0.9 represent low, moderate, and high rates, respectively. Based on the result, in terms of a single form of activation function, although sigmoid provides the least RMSE and MSE values, the ReLu function is more superior in learning the high traffic pattern with a learning rate of 0.9. In addition, Re-LU is more powerful to be used in the first order in terms of combination. Hence, combining a high learning rate and pure ReLU, ReLu-sigmoid, or ReLu-Tanh is more suitable and recommended to predict upper traffic utilization.
Co-Authors Adela Regita Azzahra Adnan, Adam Aji Prasetya Wibawa Alfitriana Riska Amaliah, Tazkirah Annisa Febrianti Arfah, Jumrayanti Asfar, A. Muh. Fitrah Astuti, Wistiani Atmajaya, Dedy Atussaliha, Nur Almar' Azwan Triyadi Backar, Sunarti Passura Basri, Haerunnisa Basri, Nur Anisa Dian Fajar AR, Muh. Aqil Fitrah Yusti Randana Harlinda, Harlinda Hasanuddin, Tasrif Hasihi, Cholisah Erman Haviluddin Haviluddin Irawati Irawati Lilis Nur Hayati lilis nurhayati Lokapitasari Belluano, Poetri Lestari Magfirah, Magfirah Mardiyyah Hasnawi Ming Foey Teng, Ming Foey Moleo, Alif Safa Munaf, Adryan Dwiprawira Nafalski, Andrew Nia Kurniati Nur wahidah Nurhalimah, Sitti Purnawansyah Purnawansyah Purnawansyah Puspitasari, Rahma Rahmasari, Putri Aulia Raja, Roesman Ridwan Salim, Yulita Samir, Mutmainnah Sastra, Ramdan Siska Anraeni Syahrir, Firdaus Abrazawaiz Triyanna Widiyaningtyas Triyanna Widyaningtyas, Triyanna Umalekhoa, Alfi Syahrin Umar, Fitriyani Veithzal Rivai Zainal Wanaspati, Nugraha Wati, Silmi Nur Zaskia Widyawati, Dewi Zahirah, Dinna