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Porous Carbon Black Microsphere from Palm Oil Black Liquor Jayadi, Jayadi; Maddu, Akhiruddin; Sari, Yessie; Widayatno, Wahyu Bambang; Wismogroho, Agus Sukarto; Firdarini, Cherly; Mulya, Marga Asta Jaya
Jurnal Sains Materi Indonesia Vol. 25 No. 1 (2023): Jurnal Sains dan Materi Indonesia
Publisher : BRIN Publishing (Penerbit BRIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jsmi.2023.686

Abstract

The aim of this research is to synthesize porous carbon black microspheres from palm oil black liquor through an in-house spray pyrolysis system. The in-house spray pyrolysis (SP) system was developed using a horizontal furnace. To test the developed SP equipment, the temperature profiles within the developed spray pyrolysis chamber were examined at 3 different setting temperatures (800, 900, and 1000 °C). These temperatures were also applied for synthesizing the carbon black microspheres, with and without nitrogen as carrier gas. The morphology of carbon black produced by using SP equipment was tested by a 3D Optical Microscope and FE-SEM. The optimum temperature obtained in this study is 1000 ºC according to the characterization of carbon black microspheres produced. The FE-SEM analysis indicated the presence of spherical carbon having microstructures. This indicates that the in-house spray pyrolysis machine has been successfully developed for synthesizing carbon black microspheres.
Grid-Calibrated Patch Learning for Braille Multi-Character Recognition Widyadara, Made Ayu Dusea; Handayani, Anik Nur; Herwanto, Heru Wahyu; Yu, Tony; Mulya, Marga Asta Jaya
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.15199

Abstract

The approach presents a multi braille character (MBC) recognition system for Indonesian syllablesdesigned to address real-world imaging variations. The proposed framework formulates 105-class visual classification task, where each class represents a two-character Braille unit. This design aims to preserve inter-character spatial relationships and reduce error propagation commonly found in single-character segmentation approaches. A carefully constructed dataset undergoes spatial pre-processing stages, including rotation normalization, grid assignment, and multicell cropping, resulting in uniform 89×89 pixel image patches that ensure geometric consistency across samples. To enhance model generalization under varying illumination conditions, single-dimension photometric augmentation is applied exclusively during training, including brightness (±25%), exposure (±20%), saturation (±40%), and hue (±30%). ResNet-101 is adopted as the backbone architecture based on prior comparative studies conducted on the same dataset, demonstrating its effectiveness in capturing fine-grained Braille dot shadow patterns. The network is trained for 300 epochs with a batch size of 32 under consistent experimental settings, and performance is evaluated using a confusion-matrix-based framework with overall accuracy as the primary metric. Experimental results indicate that moderate photometric reductions significantly improve recognition performance by preserving critical micro-contrast cues. In particular, an exposure reduction of −20% achieves the best balance between accuracy (86.13%) and training efficiency (14.12 minutes), outperforming the non-augmented baseline (74.37%, 22.10 minutes). A hue reduction of −30% further improves robustness to ambient color variations, while aggressive positive adjustments degrade performance due to structural distortion. These findings confirm the effectiveness of the proposed MBC framework for practical Braille recognition in real-world environments.