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Communications in Science and Technology
Published by Komunitas Ilmuwan dan Profesional Muslim Indonesia
ISSN : 25029258     EISSN : 25029266     DOI : -
Core Subject : Engineering,
Engineering
Communication in Science and Technology [p-ISSN 2502-9258 | e-ISSN 2502-9266] is an international open access journal devoted to various disciplines including social science, natural science, medicine, technology and engineering. CST publishes research articles, reviews and letters in all areas of aforementioned disciplines. The journal aims to provide comprehensive source of information on recent developments in the field. The emphasis will be on publishing quality articles rapidly and making them freely available to researchers worldwide. All articles will be indexed by Google Scholar, DOAJ, PubMed, Google Metric, Ebsco and also to be indexed by Scopus and Thomson Reuters in the near future therefore providing the maximum exposure to the articles. The journal will be important reading for scientists and researchers who wish to keep up with the latest developments in the field.
Arjuna Subject : -
Articles 234 Documents
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Insight into Aluminum Leaching with Microwave from Peat Clay: A Comparative Kinetic Study of SC and BIC Models Mirwan, Agus; Hairullah; Jelita, Rinny; Jefriadi; Putra, Meilana Dharma; Ilmanto, Bintang Hambela; Putri, Hexas Sarastiwi Handayani; Ulum, Muhammad Bahrul; Haka, Muhammad Rofi; Darmawan, Muhammad Arif
Communications in Science and Technology Vol 10 No 2 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.2.2025.1850

Abstract

The depletion of bauxite reserves has prompted the research of various types of soil as alternative sources of aluminum, such as the peat clay used in this study. The complexity of the minerals requires a more efficient leaching methods, while microwave-based leaching offers a potential approach through rapid and uniform heating. This study examines the effect of microwave power, HCl concentration, operating temperature, and particle size on the leaching efficiency of aluminum from peat clay soil. The leaching process was modeled using two approaches, namely the shrinking core (SC) model and the broken-intact cell (BIC) model under pseudo-steady state conditions. The results showed that increasing HCl concentration, microwave power, and temperature accelerated leaching, while increasing particle size decreased leaching efficiency. Optimum conditions were achieved at 4 M HCl concentration, 100 W power, 40 °C temperature, and 0.0074 cm particle size. The shrinking core (SC) model showed better fit under most conditions, while the intact-broken cell (BIC) model was more accurate at lower temperatures and particle sizes. The simulation results showed that the most suitable parameter values in the SC model were De = 0.0049 cm2/s, k = 10.5 cm/s, and kc = 2.49 cm/s, while in the BIC model De = 0.04808 cm2/s and K = 0.02689 g/cm3 were obtained. These results confirm the superiority of the SC model in representing microwave-based leaching mechanisms in general, while the BIC model provides additional insights under diffusion-limited conditions. Process Performance Index (PPI) analysis showed that optimum conditions were achieved at 4 M HCl and 40 °C, but lower acid concentrations also yielded competitive PPI. This confirms that leaching effectiveness is determined by a combination of alumina recovery and reagent consumption efficiency. These findings contribute to the development of leaching kinetics models and the optimization of more efficient and energy-saving aluminum extraction processes.
Interpretability Evaluation of Rule-Based Classifier in Myocardial Infarction Classification Based on Syntactical Features of ECG Signal Fityah, Farhatul; Setiawan, Noor Akhmad; Anggrahini, Dyah Wulan
Communications in Science and Technology Vol 10 No 2 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.2.2025.1851

Abstract

Cardiovascular diseases remain the leading cause of mortality on a global scale, with myocardial infarction (MI) representing a critical and life-threatening condition. Electrocardiography (ECG) is a widely utilized method for the detection of myocardial infarction (MI), and artificial intelligence (AI) has demonstrated a promising performance in the automated ECG-based diagnosis. However, most existing studies emphasizepredictive accuracy while failing to provide substantial evidence that model decision logic aligns with clinical reasoning, thereby limiting clinical adoption. This present study evaluates the interpretability of three rule-based machine learning classifiers—Decision Tree, RIPPER, and Rough Set—for MI detection from ECG signals, including a comparison between models with and without feature selection. Interpretability of the system is assessed through rule complexity analysis and a standardized qualitative clinical validation protocol involving three cardiologists, based on contemporary AHA/ESC ECG diagnostic guidelines. The findings indicate that the Rough Set classifier attains the optimal overall performance, with 80% of its generated rules demonstrating clinically aligned, thereby outperforming the other models regarding interpretability. The findings demonstrate the benefit of guideline-based clinical validation for advancing trustworthy ECG-based MI diagnostic systems.
YOLOv8-Based Detection of Convective Storm Clouds for Cumulonimbus Classification Rafsyam, Yenniwarti; Nurjihan, Shita Fitria; Rinaldi, Arief
Communications in Science and Technology Vol 10 No 2 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.2.2025.1854

Abstract

Cumulonimbus (CB) clouds are vertically developed convective systems that are capable of producing severe weather phenomena, including turbulence, heavy rainfall, and lightning. These phenomena pose a significant threat to aviation safety. This paper considers an automated CB cloud detection approach using the deep learning algorithm You Only Look Once version 8 on NOAA-19 satellite imagery. The images of 640 × 640 pixels each were labeled into two classes: CB and non-CB. In general, rotation, flip, and random brightening are performed to develop a more robust model. After 100 training epochs, the proposed model produced reliable detection performance, as evidenced by 1,694 TP (true positives), 438 FP (false positives), and 304 FN (false negatives) cases, with a precision of 0.79, recall of 0.84, and an F1-score of 0.81. Validation using METAR reports from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG) confirmed the consistency of the model with observed weather conditions. The results demonstrated that YOLOv8 could provide a rapid and reliable framework for real-time detection and classification of CB clouds, thereby enhancing situational awareness for aviation operations and facilitating the effectiveness of satellite-based early warning systems in convectively active tropical regions.
A High-Capacity Reversible Watermarking Technique Using Bit-Level Expansion and Pixel Shifting Arham, Aulia; Alfarozi, Syukron Abu Ishaq; Nugroho, Hanung Adi
Communications in Science and Technology Vol 10 No 2 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.2.2025.1856

Abstract

This paper proposes a high-capacity reversible watermarking method using adaptive bit-level expansion and pixel-class-guided shifting. Pixels are classified into expandable (P0) and non-expandable (P1) according to their 2-bit LSB patterns, and a lightweight reversible transformation converts P1 into P0 with minimal distortion. A shifting map enables exact recovery and avoids overflow/underflow. Secret data are embedded through a 2-bit LSB expansion rule that ensures full reversibility. Experiments on common and medical images demonstrate a consistent embedding capacity of 1.0 bpp, achieving PSNR values above 46 dB and SSIM above 0.97. In addition, the scheme exhibits low computational overhead (<0.7s per image, >380 kbps) while preserving the original histogram distribution. These results demonstrate that the proposed scheme provides an effective balance between embedding capacity, visual quality, and computational efficiency for secure medical imaging and authenticated reversible data embedding.

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