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All Journal PROtek : Jurnal Ilmiah Teknik Elektro Journal of System and Computer Engineering Jurnal Abdi Masyarakat Indonesia Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Jurnal Medika: Medika JBIMA: Jurnal Pengabdian kepada Masyarakat
Bayu Adrian Ashad
Universitas Muslim Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Medicine & Pharmacology Nursing Public Health Other

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Enhancing Polyp Segmentation Using Attention U-Net with CLAHE Ramdaniah, Ramdaniah; Ashad, Bayu Adrian
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2370

Abstract

Colorectal cancer remains one of the leading causes of death worldwide, where early detection of polyps through colonoscopy plays a vital role in prevention. This study aims to enhance polyp segmentation performance by integrating Attention U-Net with Contrast Limited Adaptive Histogram Equalization (CLAHE) as a preprocessing technique. The proposed method was evaluated using two benchmark datasets, CVC-ClinicDB as the primary dataset and Kvasir-SEG for cross-domain testing. The model was trained using a combination of Binary Cross-Entropy and Dice losses, with a 70–15–15 split for training, validation, and testing. Experimental results show that applying CLAHE improves segmentation accuracy, achieving Dice and IoU scores of 0.84 and 0.76 on CVC-ClinicDB, and 0.62 and 0.50 on Kvasir-SEG, respectively. Statistical analysis using the Wilcoxon signed-rank test confirmed a significant difference between the baseline and enhanced models. These findings demonstrate that the integration of CLAHE with Attention U-Net effectively improves boundary detection and robustness against illumination variations across datasets, contributing to more accurate and reliable computer-aided diagnosis in colorectal cancer screening.
Peramalan Beban Listrik Harian di Kota Ternate Menggunakan ELM Ilyas, Andi Muhammad; Rahman, Muhammad Natsir; Aswat, Aldi; Syamsuddin, Faris; Suparman, Suparman; Ashad, Bayu Adrian; Siswanto, Agus
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2465

Abstract

The continuously increasing growth of electricity demand necessitates accurate and systematic planning of electric power systems to ensure power flow quality and system reliability. Ternate City, as one of the major activity centers in North Maluku Province, has experienced a substantial rise in electricity consumption, thereby requiring an effective and reliable load forecasting approach. This study aims to predict the daily electricity load in Ternate City using the Extreme Learning Machine (ELM) method. The analysis is conducted using historical electricity load data, which are processed through data preprocessing stages, dataset partitioning into training and testing sets, and ELM-based modeling. The performance of the proposed model is evaluated using the Mean Absolute Percentage Error (MAPE). The results indicate that the MAPE values for the training dataset range from 5.84% to 13.63%, corresponding to very good to good performance categories. Meanwhile, the testing dataset yields MAPE values ranging from 13.45% to 33.09%, which fall within the good to sufficient performance categories. Furthermore, the prediction results are able to accurately capture daily electricity load fluctuation patterns from Monday to Sunday, including peak load periods. Based on these findings, the ELM method demonstrates strong potential as a reliable approach to support electric power system planning and to enhance the quality and reliability of electricity supply in Ternate City.
Co-Authors Agus Siswanto Andi Syarifuddin Arif Jaya Aswat, Aldi Mansur Rahman, Muhammad Natsir Ramdaniah Ramdaniah Ramdaniah Saidah Suyuti Sriwijanaka Sriwijanaka Sriwijanaka Sriwijanaka Sriwijanaka Yudi Hartono St. Hajrah Mansyur Sugianto Sugianto Suparman Suparman Syamsuddin, Faris