Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Antonius Filian Beato Istianto
Bina Nusantara University, Jakarta, Indonesia
Computer Science & IT Control & Systems Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Image Enhancement using Convolutional Neural Network for Low Light Face Detection Antonius Filian Beato Istianto; Gede Putra Kusuma
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 1 (2024): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i1.318

Abstract

This research aims to combine the study of face detection with improvement of image quality in low-light conditions. In this research, we introduce a method that combines Convolutional Neural Networks for image processing to enhance face detection performance in low-light conditions. The proposed method involves pre-processing the images using three image enhancement methods: Deep Lightening Network, Deep Retinex Net, and Signal-to-Noise Ratio Aware. Each of these methods is combined with the face detection method, RetinaFace. The experiment is evaluated using the DARKFACE Dataset, and the performance of each combination is assessed using Average Precision (AP). The combination that yields the best AP value will be determined as the best approach for low-light face detection. The best combination, which utilizes Signal to Noise Ratio Aware for image enhancement and RetinaFace for face detection, achieves an AP score of 52.92%. This result surpasses the face detection performance using the original images from the DARKFACE Dataset, which scored 7.12% in AP. Thus, this experiment demonstrates that image enhancement using Convolutional Neural Networks can significantly improve face detection in low-light conditions
Comparative Analysis of Binary Particle Swarm Optimization on Dynamic Value Methods for Cognitive and Social Aspects and Its Implementation in Hyper-Heuristic Safan Capri; Oscar Edward Guijaya; Antonius Filian Beato Istianto; Antoni Wibowo
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 4 (2023): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i4.264

Abstract

Particle Swarm Optimization (PSO) is a population-based optimization which include the use of cognitive and social terms. The cognitive term is represented with the variable of c1 while social term is represented with the variable of c2. Both values can be assigned between 0 and 1. The contribution of this research is to compare which role is superior in the Binary Particle Swarm Optimization (BPSO) metaheuristic with Dynamic Increase Cognitive Decrease Social (DICDS) and Dynamic Decrease Cognitive Increase Social (DDCIS) methods, as well as its implementation in the Modified Multi-Objective Agent-Based Hyper-Heuristic (MOABHH). The experiments were carried out 30 times on data set 2 from [1]. The result is that the DDCIS method is 0.4% better in objective value than the DICDS method. This is also proven with the average of number of solutions in the DDCIS method which is more 2.3 solutions than the DICDS method based on the evaluation results carried out by Modified MOABHH. In addition, Modified MOABHH which is run simultaneously with the DICDS and DDCIS methods provides better objective value results of 0.6% compared to the average of both results for each of these methods which are run separately.
Co-Authors Antoni Wibowo Gede Putra Kusuma Oscar Edward Guijaya Safan Capri