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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Jurnal Informatika dan Teknik Elektro Terapan I-ECONOMICS: A Research Journal on Islamic Economics Register: Jurnal Ilmiah Teknologi Sistem Informasi INTEGER: Journal of Information Technology Applied Technology and Computing Science Journal Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Edunomika (JIE) Jifosi Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Akuntansi, Manajemen Dan Ekonomi Islam (JAM-EKIS) Jurnal Material dan Energi Indonesia Sharia Economic and Management Business Journal (SEMBJ) Islamic Banking & Economic Law Studies Al-Intaj : Jurnal Ekonomi dan Perbankan Syariah Journal of Visionary Sharia Economy (JOVISHE) Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Muttaqin, Faisal
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Social Sciences Other

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

 1 
Pengaruh preprocessing citra retina pada klasifikasi diabetic retinopathy berbasis prototypical network Wulyono, Abi Eka Putra; Muttaqin, Faisal; Mulyo, Budi Mukhamad
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11126

Abstract

Diabetic retinopathy is a diabetes complication that can lead to progressive retinal damage and permanent blindness. Early detection through automated fundus image classification is essential but challenged by varying image quality, background noise, and color dominance that reduces lesion visibility. Prototypical networks have demonstrated good performance in few-shot learning settings, yet specialized preprocessing is rarely explored. This study proposes a prototypical network enhanced with modified circle crop to remove irrelevant regions and enhanced green channel to improve microvascular lesion contrast. Experiments were conducted on the APTOS 2019 dataset consisting of 3,662 images, split into 2,929 training and 733 testing samples, using a 5-way 5-shot configuration. The proposed preprocessing increases accuracy from 64.53 percent to 71.35 percent and improves quadratic weighted kappa from 0.5712 to 0.6990. These results indicate that preprocessing enhances feature representation and classification performance under limited data conditions.
Analisis Efisiensi Arsitektur U-Net dengan Encoder MobileNetV2 pada Segmentasi Karat Daun Kopi Adeva, Muhammad; Muttaqin, Faisal; Mulyo, Budi Mukhamad
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11221

Abstract

Coffee Leaf Rust (Hemileia vastatrix) poses a serious threat to Robusta coffee productivity. Manual identification is often slow and subjective, while standard Deep Learning segmentation methods like U-Net with VGG16 encoder bear heavy computational loads (~24.89 million parameters), hindering deployment on resource-constrained devices. This study aims to optimize computational efficiency by proposing a Lightweight U-Net architecture based on the MobileNetV2 encoder. The model's performance was comparatively evaluated against the VGG16 baseline using the PlantSeg public dataset. Experimental results show that MobileNetV2 integration successfully reduced model size massively by 96% (to ~0.95 million parameters) and accelerated inference time by ~20% (76.28 ms). Although there was a slight F1-Score decrease of 0.3% compared to the baseline, the proposed architecture offers the best trade-off between efficiency and accuracy, making it a viable solution for mobile implementation
Klasifikasi kendaraan bermotor berdasarkan jumlah gandar menggunakan adaptive minimal ensemble Al Hakim, Abdurrahman; Muttaqin, Faisal; Hendra Maulana
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11239

Abstract

The increasing volume of motor vehicles requires automated monitoring for the classification of heavy vehicle categories (Category I–V) based on the number of axles using side-view cameras. This process represents a complex fine-grained visual classification challenge due to the similar body shapes of trucks. To address the dilemma between the need for high accuracy and computational efficiency, this study implements an Adaptive Minimal Ensemble (AME) architecture that adaptively combines small-scale models.  The model is evaluated using a confusion matrix along with accuracy, precision, recall, and F1-score metrics. The testing results demonstrate that a single EfficientNetV2-S model is only able to achieve a maximum accuracy of 83% and exhibits significant limitations in extracting crucial distinguishing features, leading to misclassification of Category 4 and 5 vehicles. In contrast, the AME architecture, which utilizes the two best-performing EfficientNetV2-S base models, successfully achieves a substantial performance improvement with 95% accuracy, 95.21% precision, 95% recall, and a 94.99% F1-score.  In conclusion, the adaptive layer mechanism in AME is proven to be highly effective in compensating for the individual prediction weaknesses of its base models, resulting in a significantly more precise vehicle classification monitoring system.
Co-Authors Abda Abda Adeva, Muhammad Al Hakim, Abdurrahman Alfioni, Sovita Ani Dijah Rahajoe Aprina, Nana Arishandy, Zalfa Ibtisamah Asnaini Asnaini, Asnaini B, Idwal Basuki Rahmat Masdi Siduppa Fatimatuz Zuhro, Fatimatuz Fina Amru Millati Fitri, Marsellina Gusti Eka Yuliastuti Harniati, Resi Haryanto, Yosafat Donni Herry Novrianda idwal Idwal, Idwal Isnaini, Desi Juwairiah Juwairiah Khairul Anwar Made Hanindia Prami Swari Maulana, Hendra Mulyo, Budi Mukhamad Oktavian, Jaguar Deva Nanggalasakti pramitha sari, nova Pratama Wirya Atmaja Putra, Chrystia Aji Rachmania, Rachmania Rahmadsyach, Mochammad Taufiq Rahmat Hidayat RIZKI ZULIANDRY, MUHAMMAD IVAN Shar, Aan Subagio, Arif Widiasan Sugeng Santoso Sugiarto S Suryantari, Putu Anggi Wulyono, Abi Eka Putra Zakita, Asyer Pradana Putra Zelvia Liska Afriani