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All Journal Jurnal Pseudocode Eduvest - Journal of Universal Studies
Francisco, Francisco
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Neuroscience Social Sciences

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Enthusiast Analysis For Development Department of Digital Business Diploma III in Instituto Profisional SĀO JOĀO Batista East Timor in 2024 Guterres, Antonio; Pinto, Manuel; Gaspar, Andre Pereira; Fernandes, Luis; Francisco, Francisco; da Silva, Olivio; Tilman, Aquelino da Costa
Eduvest - Journal of Universal Studies Vol. 5 No. 2 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i2.50845

Abstract

The study in this study aims to examine in outline the extent to which enthusiasts for the development of digital business science at a high education level, it is known that the development of the era of industrial digitalization 4.0 has explored the world of international marketing, this is seen from the advancement of digitalization technology that has developed in a modern and sustainable manner both in the digital economy, Internet economy, Web economy, digital-based economy, new economy knowledge and innovation economy. The research method used in this study is descriptive qualitative research while to obtain the results of this research, in depth and for the benefit of proving about TL community enthusiasts regarding the development plan of the digital business department at IPJB Dili, the data collection techniques used in this study, in the form of; In-depth interviews, filling out questionnaires and questionnaires online and offline, direct observation in the field, document analysis and focus group discussion.
Pengembangan model pengenalan huruf SIBI pada kondisi low-light berbasis convolutional neural network Francisco, Francisco; Aklani, Syaeful Anas
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.14-20

Abstract

Deaf and speech-impaired individuals in Indonesia face communication barriers due to limited public understanding of sign language. In real use, SIBI communication often occurs in dim lighting, yet recognition models are mainly evaluated under normal illumination, motivating robust low-light recognition. This study develops a CNN model based on MobileNetV2 to recognize SIBI (Indonesian Sign Language System) letter gestures under low-light conditions (50-100 lux). The dataset comprises 5,579 images of 26 SIBI letters, divided stratified 80:10:10. The methodology includes preprocessing with Bilateral Filter, CLAHE in LAB color space, and Adaptive Gamma Correction, plus transfer learning and fine-tuning with data augmentation. Evaluation results show 97.13% test accuracy, with most errors among similar letters. Real- time testing is stable within 50-100 lux, though accuracy decreases below 50 lux or with shadows. These findings indicate that the proposed preprocessing methods and MobileNetV2 CNN maintain reliable SIBI recognition in low-light environments.
Co-Authors da Silva, Olivio Fernandes, Luis Gaspar, Andre Pereira Guterres, Antonio Pinto, Manuel Syaeful Anas Aklani, Syaeful Tilman, Aquelino da Costa