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All Journal Syntax: Journal of Software Engineering, Computer Science and Information Technology Intechno Journal : Information Technology Journal
Amelia Putri
universitas majalengka
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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ANALISIS LITERATUR SISTEMATIS TERHADAP METODE IMAGE DENOISING BERBASIS DEEP LEARNING UNTUK COMPUTER VISION Amelia Putri; Iin Karmila Septiani
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 7, No 1 (2026): Juni 2026
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v7i1.8865

Abstract

ABSTRAKPenggunaan citra digital dalam berbagai aplikasi Computer Vision seringkali terkendala oleh kehadiran noise yang menurunkan kualitas informasi visual. Makalah ini menyajikan Analisis Literatur Sistematis (SLR) terhadap perkembangan metode image denoising berbasis deep learning. Proses pencarian artikel dilakukan secara terstruktur melalui database yang terindeks Scopus dengan mengadaptasi protokol PRISMA. Melalui analisis terhadap 40 literatur kunci yang sepenuhnya bersumber dari database Scopus, ditemukan pergeseran signifikan dari metode yang membutuhkan data bersih (supervised) menuju pendekatan yang lebih fleksibel seperti Noise2Noise dan blind denoising untuk menangani noise pada dunia nyata. Hasil tinjauan ini memberikan gambaran komprehensif mengenai tren arsitektur, dataset benchmark, serta tantangan dalam mencapai efisiensi komputasi untuk restorasi citra resolusi tinggi.Kata kunci— Image Denoising, Deep Learning, Systematic Literature Review, Computer Vision, Scopus, PRISMA.ABSTRACT The use of digital imagery in various Computer Vision applications is often hindered by the presence of noise, which degrades visual information quality. This paper presents a Systematic Literature Review (SLR) on the development of deep learning-based image denoising methods. The article search process was structured through Scopus-indexed databases using the PRISMA protocol. Through an analysis of 40 key literatures completely sourced from the Scopus database, a significant shift was identified from supervised methods requiring clean data toward more flexible approaches, such as Noise2Noise and blind denoising, to handle real-world noise. The results of this review provide a comprehensive overview of architectural trends, benchmark datasets, and the challenges in achieving computational efficiency for high-resolution image restoration.Keyword— Image Denoising, Deep Learning, Systematic Literature Review, Computer Vision, Scopus, PRISMA.
A Hybrid Rule-Based and Multinomial Naïve Bayes System for Sentiment and Intent Classification of Indonesian Public Reports with Sarcasm Detection Suhendri Suhendri; Sahal Ubaidillah Gunardo; Kartika Dwi Mulyana; Ruli Susanti; Dika Alfaizal Akbar; Amelia Putri
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2765

Abstract

Public service reporting systems in Indonesia face significant challenges in processing large volumes of unstructured citizen feedback efficiently. This study proposes Aspiralytica, a mobile-based citizen report classification system that integrates TF-IDF feature extraction with a Multinomial Naive Bayes (MNB) classifier within a hybrid rule-based and machine learning architecture. The system simultaneously performs three-class sentiment classification (positive, negative, neutral) and five-class intent classification (complaint, appreciation, request, emergency, suggestion), with automated priority level determination and a rule-based sarcasm detection module achieving F1 of 0.8980. Evaluated on an augmented dataset of 1,137 sentiment-labeled and 2,187 intent-labeled Indonesian-language citizen report texts using Stratified 10-Fold Cross-Validation, the proposed MNB model achieved sentiment classification accuracy of 96.59% (F1: 96.59%) and intent classification accuracy of 96.97% (F1: 96.96%). An ablation study confirmed TF-IDF with MNB as the dominant performance driver, and a computational efficiency benchmark empirically justified MNB selection with mean inference latency of 0.3955 ms and throughput of 52,312 requests per second. The system is deployed as a FastAPI backend integrated with a React Native mobile frontend, delivering real-time classification through a citizen-facing interface.
Co-Authors Dika Alfaizal Akbar Iin Karmila Septiani Kartika Dwi Mulyana Ruli Susanti Sahal Ubaidillah Gunardo Suhendri Suhendri