Article Per Year (5 Year)
p-Index From 2020 - 2025
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This Author published in this journals
All Journal Jurnal ICT : Information Communication & Technology International Journal of Multilingual Education and Applied Linguistics International Journal of Computer Technology and Science
Arya Dimas Wicaksana
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Arts Computer Science & IT Education Languange, Linguistic, Communication & Media Public Health Social Sciences

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Enhancing Low-Resolution Facial Images for Forensic Identification Using ESRGAN Helena Dewi Hapsari; Arya Dimas Wicaksana; Hafiz Fadli Faylasuf; Asa Yuaziva; Rivanka Marsha Adzani; Endang Purnama Giri; Gema Parasti Mindara
International Journal of Multilingual Education and Applied Linguistics Vol. 1 No. 4 (2024): November : International Journal of Multilingual Education and Applied Linguist
Publisher : Asosiasi Periset Bahasa Sastra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmeal.v1i4.156

Abstract

This research is motivated by the challenges in facial identification for forensic investigations due to poor image quality, especially from low-resolution CCTV recordings. Images with noise, low lighting, and suboptimal angles often hinder accurate facial recognition. This study aims to examine the effectiveness of the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) method in enhancing the quality of forensic facial images. The methodology consists of three main stages: data preparation of low-resolution facial images, applying the ESRGAN model to enhance image resolution, and evaluating the results using metrics such as PSNR and SSIM. The findings reveal that ESRGAN significantly improves the visual details of facial images, thereby supporting better facial identification processes. These results have important implications for leveraging deep learning technology to facilitate image analysis in forensic contexts. However, challenges such as extreme noise presence require further development of methods to achieve more optimal outcomes.
Design and Development of LeleQue : A Web-Based Financial Management Application for Catfish Farming MSME Jonser Steven; Arya Dimas Wicaksana; Helena Dewi Hapsari; Wien Kuntari
International Journal of Computer Technology and Science Vol. 2 No. 1 (2025): International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v2i1.131

Abstract

The rapid development of information technology has significantly impacted the business world, including Micro, Small, and Medium Enterprises (MSMEs). However, business actors, particularly in the catfish farming sector, often face challenges in accessing broader markets and improving operational efficiency. This study aims to develop LeleQue, a web-based information system designed to efficiently support the management of catfish farming businesses. By implementing the SDLC (Software Development Life Cycle) method, this research outlines the system development process, from needs analysis to system evaluation The LeleQue application is equipped with key features such as financial bookkeeping, inventory management, debt tracking, financial reports, and discussion forums. The findings indicate that LeleQue provides an integrated platform to meet the operational management needs of catfish farming businesses. Additionally, the application facilitates collaboration among farmers through its community forum. This study contributes to supporting MSME digitalization, particularly in the catfish farming sector, to enhance efficiency, productivity, and business competitiveness.
E-Commerce FreezeMart untuk Penjualan Frozen Food dengan Sistem Rekomendasi Berbasis Content-Based Filtering Aditya Wicaksono; Setiady Ibrahim Anwar; Muhammad Al Amin; Rizki Juliansyah; Arya Dimas Wicaksana; Gema Parasti Mindara
Jurnal ICT: Information Communication & Technology Vol. 25 No. 1 (2025): JICT-IKMI, July, 2025
Publisher : LPPM STMIK IKMI Cirebon

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Abstract

Kemajuan teknologi digital mendorong perkembangan platform e-commerce yang memudahkan transaksi secara cepat dan fleksibel. Penelitian ini membahas FreezeMart, situs e-commerce untuk penjualan makanan beku yang dilengkapi sistem rekomendasi terpersonalisasi. Sistem memanfaatkan content-based filtering dan analisis histori pembelian untuk menyarankan produk yang relevan. Masukan pengguna diproses menggunakan teknik Natural Language Processing seperti TF-IDF untuk mencocokkan preferensi dengan atribut produk. Pengembangan dilakukan dengan metode Agile Scrum dan mencakup tiga peran utama: guest, customer, dan admin. Pengguna dapat mengelola pesanan dan memperoleh rekomendasi sesuai kebutuhan, sementara admin bertanggung jawab atas pengelolaan sistem. Sistem rekomendasi yang diimplementasikan terbukti mampu meningkatkan pengalaman belanja pengguna. Pada tahap selanjutnya, pengembangan sistem dapat difokuskan pada penerapan collaborative filtering serta peningkatan kinerja platform.
Co-Authors Aditya Wicaksono Asa Yuaziva Endang Purnama Giri Gema Parasti Mindara Hafiz Fadli Faylasuf Helena Dewi Hapsari Jonser Steven Kuntari, Wien Muhammad Al Amin Rivanka Marsha Adzani Rizki Juliansyah Setiady Ibrahim Anwar