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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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High-performance eco-friendly Ni Cu/bamboo activated carbon catalysts for oxidative desulfurization of high-concentration DBT Haerani, St.; Trisunaryanti, Wega; Triyono; Santoso, Imam; Purbonegoro, Jason; Wangsa
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.10.2.2025.1702

Abstract

This study investigated how the metal impregnation method affects the oxidative desulfurization (ODS) of dibenzothiophene (DBT) using H2O2 over Ni–Cu catalysts supported on bamboo-derived activated carbon. Catalysts with 1% and 2% Ni–Cu were prepared via simultaneous impregnation, while the effect of sequence was evaluated by comparing simultaneous and sequential impregnation (2%Ni-2%Cu/AC and 2%Cu-2%Ni/AC). The 2%Ni-2%Cu/AC catalyst was identified as the best catalyst, with a surface area of 802.36 m2/g, average pore diameter of 2.4761 nm, and total acidity of 3.1239 mmol/g. This catalyst achieved the highest DBT reduction of 90.81% under optimal conditions (0.2 g catalyst weight, 60 minutes, 40 °C, and 0.66 mL H2O2), confirming that the sequential impregnation route significantly enhances catalytic performance. In conclusion, the impregnation sequence in designing highly efficient desulfurization catalysts is important due to spray impregnation resulting in higher surface area, acidity, and catalytic activity compared to the simultaneous impregnation method.
Surface Chemistry and Adsorption Behavior of Methylene Blue on Functionalized Carbon Materials: A Comprehensive Study Ulfa, Maria; Hanif, Rizki Fauzia; Sholeha, Novia Amalia
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.10.2.2025.1711

Abstract

The increasing release of synthetic dyes, particularly methylene blue (MB), from textile effluents has become a major environmental andhealth concern, highlighting the urgent need for efficient remediation strategies. Adsorption remains one of the most effective techniques for dye removal due to its simplicity, low cost, and high efficiency. This review discusses the surface chemistry and adsorption behavior of MB on functionalized carbon-based materials, emphasizing how physicochemical characteristics, surface modifications, and functional groups influence adsorption capacity and selectivity. Recent progress in developing engineered carbonaceous adsorbents—such as activated carbon (AC), graphene derivatives, carbon nanotubes, and hybrid carbon composites—has significantly improved the removal performance of MB through enhanced structural and chemical interactions. The ACHC-KOM-1 carbon composite, for instance, exhibits remarkable photocatalytic-assisted adsorption, achieving complete MB degradation under optimized conditions. Surface functional groups and pore architecture are decisive factors governing adsorption efficiency. Oxygen-containing moieties, including carboxyl (–COOH) and hydroxyl (–OH), create active sites that facilitate electrostatic attraction and hydrogen bonding with cationic MB molecules. Nitrogen functionalities (–N), introduced via heteroatom doping, enhance electron-donating properties and π–π interactions between MB aromatic rings and the conjugated carbon framework, thereby strengthening molecular affinity. Pore dimensions further regulate accessibility and diffusion, with micropores (<2 nm) providing strong confinement and high adsorption energy, while mesopores (2–50 nm) promote rapid diffusion and prevent pore blockage. The synergistic combination of abundant surface functionalities and hierarchical porosity governs the overall adsorption capacity, kinetics, stability, and regeneration potential of carbon-based adsorbents for dye removal. The mechanistic framework presented here distinguishes biomass-derived and non-biomass carbon adsorbents, enabling rational design of high-performance materials. These findings offer practical optimization guidelines for industrial-scale methylene blue removal while supporting sustainable, circular-economy-aligned water purification technologies.
Microwave Absorption Performance of La0.7Sr0.3MnO3/AC Composite Material Based on Activated Carbon from Gnetum gnemon Seed Shell Priambodo, Danang Pamungkas; Saptari, Sitti Ahmiatri; Tjahjono, Arif; Manawan, Maykel T; Taryana, Yana; Hadiyawarman; Admi, Ratna Isnanita
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.10.2.2025.1727

Abstract

The 5G internet network has been proven to facilitate daily life for people and render electronic devices such as smartphones as an integral component of people's daily routine. However, in conjunction with the ease of use, there is an issue of electromagnetic radiation. To cope with this issue, magnetic and dielectric composite microwave absorber materials have been undertaken. To address this, we investigated the limitations of activated carbon composite material from Gnetum gnemon seed shells (AC) on the microwave absorption ability of (La0.7Sr0.3MnO3)1-y/(AC)y. The composite material (La0.7Sr0.3MnO3)1-y/(AC)y (y = 0; 0.3; 0.5; 0.7) was synthesized through a stirring process with a 96% ethanol catalyst using La0.7Sr0.3MnO3 synthesized by sol-gel method and activated carbon material from Gnetum gnemon seed shell (AC) synthesized by chemical activation method. The XRD and SEM characterizations indicated a single-phase structure, with smaller crystals and particles that were uniformly distributed throughout the composite sample. The presence of activated carbon grains from Gnetum gnemon seed shells (AC) were observed between the La0.7Sr0.3MnO3 grains in the composite sample. The EDS results confirmed the material’s purity. VNA characterization demonstrated that (La0.7Sr0.3MnO3)1-y/(AC)y was capable of producing two reflection loss troughs with the largest absorption percentages recorded at 82.99% and 85.82% respectively within the frequency range of 8 – 12 GHz. This research highlights the significance of controlled composite composition in enhancing microwave absorption capability, particularly in perovskite-based composites with biomass-activated carbon, which holds a considerable promise for applications in electromagnetic wave attenuation and absorption technologies.
Active-Reflective Learning Style Detection Using EEG and Abrupt Change Detection Primartha, Rifkie; Adji, Teguh Bharata; Setiawan, Noor Akhmad
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.10.2.2025.1737

Abstract

Recognizing the varying learning styles of students is vital to creating customized educational approaches and maximizing academic success. While commonly used, conventional evaluation methods such as self-report surveys are frequently characterized by subjective biases and inconsistent accuracy. To address this limitation, this present study proposes an EEG-driven approach for learning style classification, specifically targeting the Active and Reflective dimensions of the Felder-Silverman Learning Style Model (FSLSM). Data was acquired from 14 participants using an 8-channel OpenBCI headset, with cognitive engagement stimulated through Raven’s Advanced Progressive Matrices (RAPM). Initially, the raw EEG data underwent bandpass filtering process purposely to remove noise. Subsequently, the data was divided into consecutive 1-second segments. For feature extraction, the CUSUM algorithm was employed, with an aim to effectively capture significant signal variations. These features were then fed into an LDA classifier for style discrimination. The performance evaluation revealed impressive results—98.26% accuracy in standard Train-Test validation, and an even higher 99.29% under LOOCV testing. Notably, our approach consistently outperformed existing techniques including 1-DCNN and TSMG across all metrics. Notably, computational efficiency and reliability were improved, with the "Odd-only" subset yielding peak accuracy (99.24%). These findings demonstrate that integrating EEG signals with conventional machine learning enables real-time, high-precision learning style detection. Additionally, this work addresses the computational constraints and dataset limitations observed in recent studies, providing a robust foundation for adaptive learning systems. It is recommended that future research explore larger, more diverse datasets and additional FSLSM dimensions to enhance generalizability and practical implementation of the research.
Exploring Fluidization Dynamics and Chemical Performance in Silicon Tetrachloride (SiCl4) Hydrochlorination Processes within a Fluidized Bed Reactor: Development and Analysis of an Eulerian-Granular Model Rasheed, Ekehwanh; Saleh, Saad Nahi; Humadi, Jasim
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.10.2.2025.1741

Abstract

The present work examines the complex fluidization pattern and reactive interactions of silicon tetrachloride (SiCl4) during hydrochlorination in a fluidized-bed reactor (FBR), a system that remains difficult to model accurately. To address this gap, we develop a new Eulerian–granular CFD framework that for the first time couples the Eulerian–Eulerian fluid model with KTGF specifically for SiCl4 hydrochlorination, enabling prediction capabilities that are unavailable in previous FBR studies. The validity of the model was confirmed through comparisons with empirical bed-expansion correlations and Hsu’s gas-temperature data, that demonstrated strong agreement and the ability of the model to capture the coupled thermal–hydrodynamic behavior of the system. In addition to the conventional observations documented in previous studies, this study identifies distinct flow-regime transitions and bed-voidage evolution that are unique to SiCl4. These findings demonstrated the impact of SiCl4’s reactive transport behavior on fluidization stability. Under bubbling conditions, the model uncovered a characteristic SiCl4 distribution pattern that more significantly enhanced gas–solid mixing in comparison to previous report. Additionally, it predicts rapid heat equilibration within ~10 mm of bed height - a behavior not documented in earlier hydrochlorination studies. Chemically, the model predicted a maximum SiHCl3 concentration of 13.08% and an SiCl4 conversion of 28.97%, thereby offering new mechanistic insight into how fluidization dynamics directly govern reaction performance. Overall, this work provides the first specialized CFD framework for SiCl4 hydrochlorination, thus establishing a novel mechanistic understanding of its fluidization–reaction coupling. Furthermore, it offers a more accurate predictive basis for optimizing industrial FBR systems employed in silicon-based chemical manufacturing.
Green Polyols from Tamanu Seed Oil: Reaction Kinetics and Process Optimization Budiyati, Eni; Habibburohman, Mohammad Sofyan; Fauzi, Nur Ahmad; Wasi, Muhammad Azim; Musthofa, Malik; Ur rahmah, Anisa
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.10.2.2025.1749

Abstract

Using methanol, this study examined the hydroxylation process of epoxidized tamanu seed oil (ETSO), with an oxirane number of 3.92 to 4.04 mmol/g, under the catalyzation of sulfuric acid (H2SO4). The objectives of this study were, first, to synthesize polyol from ETSO, and, second, determine how temperature and catalyst concentration play a role in the hydroxylation process. During the experiment, a second-order reaction kinetic model was used for analysis. The hydroxylation process was conducted in a batch reactor for 4 hours under constant temperatures and stirring speed. During the experiment, the samples were taken every 30 minutes. The oxirane number of ETSO and the concentration of polyols were used to the reaction rates. The optimal conditions were found at a temperature of 65°C, with a methanol-to-epoxide mole ratio of 4:1 and a catalyst concentration of 3%. The pre-exponential factor (A) and the calculated activation energy (Ea) were found to be 59,041.74 g.mmol-1.min-1 and 44.69 kJ/mol, respectively. This research, therefore, has successfully identified the optimal conditions for the synthesis of bio-based polyols from tamanu oil.
Optimization of Electromyography (EMG) Signal Parameters for Assistive Device Control Using a Convolutional Neural Network (CNN) Ramadhan, Irfan Wahyu; Adinandra, Sisdarmanto
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.10.2.2025.1758

Abstract

Facial Electromyography (EMG) signals offer a promising modality for intuitive human-machine interfaces (HMIs). The development of robust control systems, however, remains challenging in view of the inherent complexity, noise susceptibility, and significant inter-subject variability of EMG signals in the facial region. This study addresses these technical challenges by developing and validating an optimized Deep Learning framework for facial gesture recognition. The primary objective of this study is to create a reliable classification model for five essential facial movements: 'Rest', 'Smile', 'Eyebrow Raise', 'Right Lip Movement', and 'Left Lip Movement'. The model will serve as precise control inputs for assistive devices. The proposed methodology employs a systematic workflow comprising signal preprocessing (filtering, normalization, and segmentation) followed by the automated hyperparameter optimization of a one-dimensional (1D) Convolutional Neural Network (CNN). The experimental results demonstrate that the optimized model achieved a classification accuracy of 90% on internal test data, with the learning rate identified as the most critical hyperparameter influencing performance. Furthermore, validation of the model on entirely new participants yielded an accuracy of 71%. While this result underscores the persistent challenge of generalizing across different users, it establishes a reliable baseline. Ultimately, this work provides a validated, optimization-based framework that utilizes low-cost instrumentation, thereby offering a substantial pathway towards more accessible and personalized hands-free assistive technologies to restore autonomy for individuals with severe motor impairments.
Material Properties Extraction of Mango (Mangifera indica) Leaves at Ka-Band Using a Waveguide Measurement System Arisesa, Hana; Idrus, Sevia Mahdaliza; Iqbal, Farabi; Abdullah, M.F.L; Wijayanto, Yusuf Nur; Adhi, Purwoko
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.10.2.2025.1768

Abstract

This study investigates the material properties (permittivity, dissipation factor, and conductivity) of mango leaves (Mangifera indica) over the 26–40 GHz Ka-band frequency based on a waveguide measurement system with a vector network analyzer instrument to capture the data. The data analysis employs the Nicolson-Ross-Weir method to extract material properties. The result reveals that the real part of permittivity decreases from about 11.0 to 5.0 with increasing frequency. Meanwhile, the imaginary part of permittivity remains low and stable, suggesting minimal absorption losses. The dissipation factor is consistently below 0.05 along the band. Effective conductivity ranges from 0.2 to 0.6 S/m, with a slight increase at higher frequencies. These findings suggest that at Ka‐band frequency, signal degradation through mango foliage is primarily driven by dispersion and scattering rather than strong dielectric absorption. The results provide essential information for improving foliage attenuation models and designing 5G and 6G communication systems in tropical regions. This study provides a reliable Ka-band dielectric dataset for mango leaves that improves the accuracy of tropical foliage-attenuation models and supports more robust 5G/6G link design and deployment in vegetation-dense environments.
Classification of Heart Disorders Using Deep Learning and Machine Learning Approaches Sumiati; Hendriyati, Penny; Dafa, Abdullah Hasan; Yusta, Afrasim; Sianturi, Susy Katarina
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.10.2.2025.1773

Abstract

Heart disorders persist a primary cause of mortality worldwide, underscoring the necessity for precise and effective diagnostic support systems. The objective of this study is to classify heart disorders employing a combination of deep learning and machine learning approaches based upon electrocardiogram (ECG) image data., The model’s performance was evaluated through 5-fold cross-validation per patient to ensure robust generalizability. The dataset comprised 486 ECG images from 284 patients. A total of six models were subjected to comparative analysis, including Support Vector Machine (SVM), VGG16, ResNet50, Custom CNN, Xception, and Inception-V3, by utilizing key evaluation metrics including accuracy, precision, recall, specificity, F1-score, and AUC-ROC. The experimental results demonstrated that Inception-V3 achieved the optimal overall performance, demonstrating a balance between sensitivity and precision. Furthermore, deep learning models generally outperformed traditional methods such as support vector machines (SVM). The mean performance across all models yielded an accuracy of approximately 78.6% and an AUC-ROC of 0.83, demonstrating reliable discrimination in cardiac disorder classification. Deep learning-based architectures, particularly Inception-V3 and Xception, demonstrated considerable potential in the development of automated and accurate diagnostic systems for the early detection of cardiac disorders. Future research could explore hybrid approaches and larger and more diverse datasets to enhance clinical applicability. This study provides improved accuracy and reliability in cardiac disorder classification by leveraging and comparing machine learning and deep learning approaches. The proposed model has been demonstrated to effectively capture complex patterns in medical data, thereby supporting early diagnosis and improving clinical decision-making.
Artificial Diet for the Cultivation of Eri Silkworm (Samia ricini Drury 1773) (Lepidoptera: Saturniidae) Barid, Siti Shofa Assyifa’ul Qulbi; Ramadhani, Fathur Syahrian; Purwanto, Hari; Saragih, Hendry T.S.S.G.; Aldawood, Abdulrahman Saad; Nuringtyas, Tri Rini; Sukirno
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.10.2.2025.1781

Abstract

The objective of this present study was to identify a suitable artificial diet formulation to support the development of eri silkworms, with castor leaves (Ricinus communis) constituting the primary ingredient. The quality of the artificial diet was evaluated using neonate larvae, comparing it to fresh castor leaves. The nutritional value was assessed by analyzing the protein content in the hemolymph of fifth-instar larvae using the Folin-Ciocalteu method and proximate analysis. The findings demonstrated that an artificial diet containing castor leaf powder during early instars, with fresh leaves being incorporated into the diet later instars, resulted in higher larval protein content. The weight of cocoon, empty cocoon, and pupa was 1.59 ± 0.05 g, 0.23 ± 0.02 g, and 1.37 ± 0.05 g, respectively. The shell ratio, female wingspan, and egg fertility were found to be 15.31 ± 0.11%, 2.42 ± 0.20 cm, and 79.2 ± 5.83 eggs, respectively. Formulation P2 exhibited the lowest larval mortality (4.23 ± 0.58%) and hemolymph protein content of 27.51 μg/mL These findings are of imperative for the cultivation of eri silk worm using artificial diet to avoid pathogen contamination and controllable nutrient content considering the early larval instar that is highly sensitive to microbes and nutrient deficiencies.

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