JRST (Journal of Science and Technology Research) is a peer-reviewed and Open-Access journal. JRST is published by the Institute for Research and Community Service (Lembaga Penelitian dan Pengabdian Masyarakat) of Universitas Muhammadiyah Purwokerto. JRST invites researchers, lecturers, students, and practitioners from around the world to exchange and advance knowledge in the fields of science and technology, covering: Research in Computer Science and Informatics Engineering Research in Engineering Science and Technology (Mathematics, Physics, Chemistry, Engineering Technology, and Engineering) Research in Health and Life Science (Biology, Pharmacy, and Health Sciences) Submitted articles must be in Microsoft Word format and written in accordance with the journal's author guidelines panduan penulisan. Journal title : JRST (Jurnal Riset Sains dan Teknologi) Abbreviation : JRST ISSN : 2549-9750 (online) 2579-9118 (print) DOI Prefix : 10.30595/jrst by Type of peer-review : Single-blind Indexing : Sinta 3 and view more Frequency : 2 issues/year (Mar, Sept) Journal History : See Journal history Editors : See Editorial Team Citation analysis : | Google Scholar | Sinta | Dimensions Journal cover: get here
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Identification and Characterization of Plant Vegetation in the Very Acid Environment of Kawah Putih Bandung
Kawah Putih in Bandung Regency is an extreme environment with volcanic characteristics such as fluctuating temperatures, low soil pH and high sulfur levels. This research aims to identify and characterize plants in the region, as well as assess their morphological and physiological adaptations to extreme conditions. The field survey method in June 2024 recorded plant species and their characteristics. Some of the identified species include Vaccinium varingifolium (Cantigi), Polypodium feei (Tangkur Fern), Histiopteris incisa (Batwing Fern), and Litsea cubeba (Lemo). These plants show special adaptations: Vaccinium varingifolium has the ability to adapt to drought and high temperatures, Polypodium feei and Histiopteris incisa are able to overcome nutritional deficiencies and exposure to sulfur, while Litsea cubeba with its deep root adaptations can detoxify the environment. This research provides insight into plant adaptation strategies in extreme environments and the importance of conservation to protect local biodiversity and the economic potential of these plants.
Copyright (c) 2025 Anik Nafisah Maulida, Muhamad Jalil https://creativecommons.org/licenses/by-sa/4.0
Diversity of Bird Species in the Samarendah Park Area Samarinda City East Kalimantan Province
Every city has parks or green areas which have an important function in creating a healthy and comfortable environment. Samarendah Park, located in the city of Samarinda, is a city park area filled with various types of vegetation such as trees, grass and others. The presence of that vegetation allows various types of birds to come and search for food or build nests. The diversity of bird species in Samarendah Park is not yet known, making it very important to conduct research. The aim of this study is to determine the diversity index of bird species found in Samarendah Park, Samarinda. The method used in this research is Point Count. Each bird species observed at the observation points is noted for its morphology, counted, documented, and identified. The obtained data were analyzed using the Diversity Index (H’), Relative Abundance Index (RAI), Evenness Index, Richness Index, Feeding Guild, and conservation status based on the International Union for Conservation of Nature. (IUCN). The research results obtained 7 types of birds with a diversity index value of 1.36, which falls into the moderate category. The highest IKR value is Passer montanus at 56.03%, the Species Evenness Index value is 0.7, and the Species Richness Index is 1.26. The results of the Feeding Guild analysis revealed 3 types of omnivores, 2 types of insectivores, and 2 types of granivores. As for the conservation status of all the bird species found, they are classified as Least Concern. (LC).
Copyright (c) 2025 Muhamad Agil, Raihan, Syifa Aulia Putri, Dina Firda Safitri, Intan Nur Aini, Melati, Amul Lihan, Cindi Amelia Putri, Firda Latifah Ashari, Titi Windiani, Anisa Tatia Putri https://creativecommons.org/licenses/by-sa/4.0
Soil-Derived Endospore-Forming Bacillus Bacteria Producing Protease
Proteases are widely utilized in many industries, including waste treatment, textiles, detergents, food processing, and medicines. This encourages researchers to discover novel sources of these enzymes. Exploration and isolation of putative proteolytic bacteria from the soil is one promising approach. The purpose of this study is expected providing the bacterial isolates that will produce superior proteases. Proteolytic bacteria were isolated from soil samples using the spread plate technique and selective skim milk agar media. The formation of clear zones and the determination of the proteolytic index were the starting point for a qualitative analysis of protease activity. Protease activity was determined quantitatively. The substrate is 1% casein dissolved in a 0.2 M tris-HCl buffer at pH 7.5, while the standard is tyrosine. The quantitative measurement of protease activity was carried out concurrently with the bacterial growth curve determination. The proteolytic bacteria P02 was successfully isolated in this research, having a protease enzyme activity of 0.867 U/mL and a proteolytic index of 0.8 ± 0.1. Protease activity peaked when the bacterial growth was in the logarithmic phase. Proteolytic bacterial P02 was Gram-positive and possesses the ability to form endospores and has a rod-shaped cell morphology. According to the outcomes of biochemical physiological testing, the bacterial P02 is considered to be the genus of Bacillus sp. The proteolytic bacteria P02 has potential to provide a sustainable and renewable source of protease, which is widely employed in industry and biotechnology.
Copyright (c) 2025 Mazidah Noer Inayah, Suci Indah Budiarti, Rizal Khoirun Alfisah https://creativecommons.org/licenses/by-sa/4.0
Formula Optimization of Roselle Flower Extract as Clay Facial Soap
Roselle flower is known rich in anthocyanin compounds that has activities as antipollutants, so it has potential to be developed as clay facial soap. Soap is formed by the saponification reaction between fatty acids and strong bases which generally has high pH properties. The anthocyanin compound in roselle extract is unstable at base pH. In addition, there are crucial parameters such as pH, viscosity and foam stability that must be eligible to be accepted as cleanser product for facial skin. This study aimed to obtain an optimal formula for clay facial soap containing roselle flower extract by optimizing concentration of surfactant SLES and KOH as soap-forming components. Soap basis was made by mixing the base with fatty acid phase. Design Expert 13 software, one-way ANOVA, and T-Test SPSS were used to design, analyze the effect and predict optimum mixed components in the formula so the final product met the ideal parameters. The data of study showed that the optimal formula of roselle flower clay facial soap should have 9.5% of SLES and 5.25% KOH with a desirability value of 1. The optimum formula had organoleptic, homogeneity, pH, viscosity and foam stability parameters that met the requirements.
Copyright (c) 2025 Hidayah Anisa Fitri, Ghaliyah Kusuma Wardani, Ika Yuni Astuti, Indri Hapsari https://creativecommons.org/licenses/by-sa/4.0
Analysis of the Implementation of Drug Control Category A with MMSL in Hospital Pharmacy Installation on Drug Values, ITOR and Stock Out
Effective drug management is crucial for enhancing both economic performance and patient health outcomes in healthcare settings. This study aims to evaluate the implementation of drug inventory control using the Minimum Maximum Stock Level (MMSL) method at Hospital “X” in Purwakarta. Employing an experimental research design, this study conducted a comparative analysis of drug consumption before and after the application of the MMSL method, focusing on key metrics such as inventory value, inventory turnover (ITOR), and stock-out rates. The study's population comprised Category A medications, totaling 106 items, with an investment value of constituting 70.6% based on ABC analysis. Data were analyzed using the Wilcoxon signed-rank test to assess the significance of the MMSL method's impact. Our findings revealed notable differences in inventory value, ITOR, and stock-out rates between the baseline consumption (Rp. 830,213,174; ITOR 2.47; stock-out Rp. 6,967,778) and the MMSL implementation period (Rp. 627,919,174; ITOR 3.15; stock-out Rp. 3,776,316), with statistical significance levels of 0.045, 0.000, and 0.300, respectively. The results indicate that the MMSL method significantly enhances drug inventory management in the hospital setting, underscoring its effectiveness in optimizing pharmaceutical resource allocation.
Copyright (c) 2025 Herayanti Hermawan, Yusi Anggriani, Hesty Utami Ramadaniati, Cipto Darmodjati Kumolo https://creativecommons.org/licenses/by-sa/4.0
Leveraging Linear Discriminant Analysis for Early Mental Health Disorder Identification
Mental health disorders pose a significant global challenge, with early identification playing a crucial role in effective intervention and treatment. However, existing diagnostic methods often rely on subjective assessments, leading to potential misdiagnosis and delayed treatment. This study aims to address these limitations by exploring the application of Linear Discriminant Analysis (LDA) for early identification of mental health disorders, specifically focusing on Bipolar Type-1, Bipolar Type-2, Depression, and Normal conditions. Utilizing a publicly available dataset from Kaggle comprising 120 records and 17 attributes, this study applies LDA to classify mental health conditions. The preprocessing steps included handling missing values, encoding categorical data, and normalizing the dataset to enhance model performance. The classification performance was evaluated using a confusion matrix and classification report metrics, demonstrating high accuracy, precision, recall, and F1-scores, particularly for Bipolar Type-1 and Depression, while slightly lower for Bipolar Type-2 and Normal conditions. The novelty of this research lies in the application of LDA to a nuanced mental health dataset, emphasizing its potential as a computational diagnostic tool to complement traditional assessment methods. However, findings suggest that larger, more diverse datasets and the incorporation of objective clinical assessments are necessary to further improve classification accuracy. This study underscores the potential of LDA as a practical and interpretable approach for early mental health diagnosis, providing a foundation for future research to enhance its robustness and clinical applicability.
Copyright (c) 2025 Deden Iwan Setiawan, Marselina Endah Hiswati, Sriwidodo, Mohammad Diqi, Luh Putu Erikawati, Rahayu Cahya Ariani https://creativecommons.org/licenses/by-sa/4.0
Analysis of Factors Affecting Air Pollution Levels Using the Three-Way Analysis of Variance Method
Nowadays, air pollution is a major cause of worsening environmental quality around the world, impacting human health. In Jakarta, once the world's most polluted city, motor vehicle emissions are a major contributor to the increase in PM2.5 concentrations, which are particulate matter harmful to health. This study aims to analyze the factors that affect PM2.5 concentration, analyze the interaction between these factors, and analyze the most influential factor on PM2.5 concentration using the Three-Way ANOVA method. The factors analyzed include weather (rainy and sunny), wind speed (weak wind and moderate wind), and data collection time (morning, afternoon, evening). The results of the analysis showed that the weather variable and data collection time had a significant influence on PM2.5 concentrations in the air, while the wind speed variable did not show a significant influence. In addition, there was no interaction between the independent variables. The data collection time variable has the most dominant influence with an F-count value of 145.80, indicating that PM2.5 concentrations vary more depending on the time of data collection.
Copyright (c) 2025 Anggina Sandy Sundari, Amalia Kusumawati, Dino Rimantho https://creativecommons.org/licenses/by-sa/4.0
Optimization of AI Usage in Learning Materials Application of Prompt Engineering Techniques for Learning Management Systems (LMS)
The application of artificial intelligence (AI) in education opens new opportunities to enhance the quality and effectiveness of learning materials. This study integrates the capabilities of ChatGPT-4o with Prompt Engineering techniques, including persona, context, task, format, exemplar, and tone, to develop AI-based adaptive and interactive instructional content within Learning Management Systems (LMS). By employing these techniques, AI can generate more relevant, personalized, and academically aligned learning materials. The evaluation results using Mean Absolute Error (MAE) indicate that the error rate in generating learning outcomes is 17, syllabus design 14, quiz generation 12, and retrieval of CPL & CPMK 9. These values demonstrate that while ChatGPT-4o is reasonably accurate in generating instructional materials, deviations still exist, particularly in completeness of information and material specification. Furthermore, manual validation remains necessary to ensure alignment with academic standards. Thus, this study confirms that AI-based Prompt Engineering can be an effective and efficient tool for supporting digital learning, yet human supervision is essential to maintain accuracy, credibility, and the quality of educational content.
Copyright (c) 2025 Muhammad Fauzan Gustafi https://creativecommons.org/licenses/by-sa/4.0
Implementation of Smart Contracts in Electronic Voting (Case Study: Election of the President and Vice President POLNES Student Executive Board)
The election of the candidates for President and Vice President of the Samarinda State Polytechnic Student Executive Board (BEM) still relies on conventional paper-based voting, which is susceptible to manipulation, ballot damage, and time-consuming vote counting. This research seeks to develop design and test for blockchain-based e-voting system prototype utilizing smart contracts to enhance efficiency, security, and transparency. Employing the Waterfall methodology, the research includes requirement analysis, system design using flowcharts and UML use case diagrams, smart contract implementation in Solidity on a local Ethereum network (Ganache), and testing via unit testing for smart contracts using Truffle and BlackBox testing for the user interface. Results demonstrate the system’s ability to automate the election process, including candidate and voter registration, identity verification via student ID, prevention of double voting, and real-time vote counting. Blockchain technology ensures immutability, transparency and guarantees data integrity. The results of this research can be an alternative solution that is more efficient, secure and transparent, by minimizing the risk of data manipulation, saving logistics costs, potential damage to voting media and accelerating the vote counting process which is automatically calculated by smart contracts. Overall, this system proves the potential of blockchain technology and smart contracts as a modern alternative to electronic voting systems.
Copyright (c) 2025 Rahmat Wahyudi, Muhammad Farman Andrijasa, Noor Alam Hadiwijaya https://creativecommons.org/licenses/by-sa/4.0
Decision Support System for New Employee Recruitment Using Profile Matching and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) Method
This research focuses on the challenges of recruiting new employees at Perumdam Tirta Siak Pekanbaru, which often involves time-consuming procedures and is prone to subjective bias. The main objective is to develop a decision support system using the profile matching and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) methods to enhance the efficiency and effectiveness of HRD in selecting the best candidates. Profile matching is used to assess applicant competency with company criteria, while PROMETHEE determines the priority order of candidates. The data collected and analyzed includes assessment criteria and user feedback. The conclusion confirms the effectiveness of the system in setting assessment parameters accurately and minimizing subjective bias. The system shows high flexibility in adapting to changing assessment parameters and candidates. Test results show the system is capable of producing consistent and reliable employee ratings, making it a valuable tool in recruitment at Perumdam Tirta Siak. Its implementation is expected to bring the company closer to its long-term strategic goals and strengthen the decision-making process for recruiting new employees.
Copyright (c) 2025 Elsi Titasari Br Bangun, Retantyo Wardoyo, Yudha Islami Sulistya, Bayu Anugerah Putra, Fauzan Azim https://creativecommons.org/licenses/by-sa/4.0
Prediction of Vaccine Inventory in Infants with Holt-Winter's Exponential Smoothing Method (Case Study: East Java Province)
Infant vaccination is important in supporting growth and strengthening the immune system. One of the challenges faced is the imbalance between vaccine supply and demand in various regions, which can lead to distribution shortages. This study aims to predict the supply of infant vaccines to reduce distribution gaps using the Holt-Winters Exponential Smoothing method. This method is applied using two approaches: an additive and a multiplicative model based on monthly data from 2021 to 2024. The results show that the multiplicative model is more accurate for the bivalent oral polio vaccine (BOPV), hepatitis B (HBO), and measles-rubella (MR) vaccines because demand exhibits significant fluctuations. The additive model is more accurate for Bacillus Calmette-Guérin (BCG), diphtheria-pertussis-tetanus (DPT), and inactivated poliovirus vaccine (IPV) because demand tends to be stable around a constant average value. The BOPV vaccine yields perfect accuracy (MAPE< 10%) and reasonably good accuracy for the HBO vaccine (MAPE< 20%). The BCG and MR vaccines have low accuracy levels (MAPE< 50%). The DPT and IPV vaccines have bad accuracy levels (MAPE> 50%). Accuracy levels can be influenced by demand fluctuations, uneven distribution, and adjustments to the α, β, and ꝩ parameters. The results of this study indicate that the Holt-Winters Exponential Smoothing method can help predict vaccine supply fluctuations more accurately, thereby supporting more even distribution across all regions.
Copyright (c) 2025 Dian Puspita Sari, Aris Fanani, Susilo Ari Wardani, Wika Dianita Utami https://creativecommons.org/licenses/by-sa/4.0
Network Analysis Reveals Tea Catechins as Multi-Target Inhibitors of Colon Adenocarcinoma
Colon cancer is the second most common cause of cancer death worldwide. It drives the need for effective prevention and treatment strategies, including identifying new and inventive targets for treating colon cancer. This present study evaluated the potential of tea catechins against colon adenocarcinoma in silico. The study method includes a literature review, an analysis of protein targets and protein interactions, and network analysis. The prediction analysis showed that only epicatechin-3-gallate, epigallocatechin-3-gallate, and epigallocatechin potentially target colon cancer. These compounds target six common proteins in human cancer cells, such as AURKA, CA9, SERPINE1, MET, SQLE, and VEGFA. The analysis showed that the protein targets interact strongly with various proteins in human cancer cells. Therefore, tea catechin, especially epigallocatechin-3-gallate, can be explored as a colon cancer chemopreventive agent.
Copyright (c) 2025 Anwar Rovik, Dyah Fitri Kusharyati https://creativecommons.org/licenses/by-sa/4.0
Reconstruction of Low-Resolution Facial Images Using Improvements to the FSRCNN Model with Parallel Feature Extraction Layers and Residual Connections
Enhancing the quality of reconstructing low-resolution facial images poses a significant challenge in various applications, particularly in CCTV-based surveillance. This study develops a Fast Super-Resolution Convolutional Neural Network (FSRCNN) model with the addition of parallel feature extraction layers and residual connections from these layers to the expanding layer to improve the reconstruction quality of faces from low resolution. The proposed model is tested using two datasets: CelebA and CCTV footage. The tests are conducted for two reconstruction scales, namely from LR to HR and from LR to intermediate. The results demonstrate that the proposed model consistently outperforms the standard FSRCNN. In the LR to HR reconstruction, the proposed model achieves an average PSNR improvement of 1.24 dB and an SSIM increase of 0.0584 on the CelebA dataset. For the CCTV dataset, an average PSNR enhancement of 0.52 dB and an SSIM improvement of 0.025 are attained. While this model significantly enhances image quality, several limitations were identified regarding performance on more varied CCTV images and the higher complexity of the model.
Copyright (c) 2025 Tommy, Rosyidah Siregar, Edy Rahman Syahputra https://creativecommons.org/licenses/by-sa/4.0
Chlorophyll-a Analysis in the Coastal Waters of Nusa Lembongan using Sentinel-2 Image Data
Nusa Lembongan is a potential area for seaweed cultivation and is the largest commodity producing the best seaweed on the island of Bali. This shows that the waters in Nusa Lembongan are included in fertile waters. One indicator in determining the level of fertility in a water area is by analyzing the chlorophyll-a value. One method for analyzing the chlorophyll-a value is using remote sensing methods. The image data used in this study were Sentinel-2A and Sentinel-2B level 1C image data. The calculation of chlorophyll-a analysis used the Case 2 Regional Coast Color (C2RCC) processor. The C2RCC processor used in the analysis is included in the Sentinel Application Platform (SNAP) application. The results of the analysis showed that the chlorophyll-a value in the dry season was higher than in the rainy season. The maximum value of chlorophyll-a analysis in the dry season was 40.92 mg/m3 while the maximum value of chlorophyll-a analysis in the rainy season was 40.89 mg/m3. The locations with the highest chlorophyll-a values ​​in the coastal waters of Nusa Lembongan are in the mangrove forest ecosystem in Jungutbatu Village, seaweed cultivation in Lembongan Village and coral reef ecosystem in Tanjung Ental.
Copyright (c) 2025 Sagung Putri Chandra Astiti, I Gusti Ngurah Kerta Arsana, I Putu Gustave Suryantara Pariartha https://creativecommons.org/licenses/by-sa/4.0
Optimization of CNN Architectures for Accurate Brain Tumor Classification: A Comparative Study
Automatic classification of brain tumors from MRI images is crucial for supporting early diagnosis and improving treatment planning. However, manual diagnostic processes remain limited by subjectivity and resource constraints. This study aims to optimize brain tumor classification by conducting a comparative analysis of six Convolutional Neural Network (CNN) architectures: VGG16, VGG19, MobileNet, InceptionV3, AlexNet, and Xception. The MRI datasets were sourced from open repositories and processed through normalization, noise reduction, segmentation, and data augmentation. All CNN models were implemented using transfer learning and trained under consistent parameters. Model performance was evaluated based on accuracy, sensitivity, specificity, and F1-score. The results revealed that the Xception and InceptionV3 architectures achieved the highest classification performance, with validation accuracies of 97.9% and 96.1%, respectively. MobileNet also performed competitively at 95.6%, offering notable computational efficiency. In contrast, VGG19 and AlexNet yielded lower validation accuracies and exhibited signs of overfitting. These findings highlight the effectiveness of modern CNN architectures that incorporate depthwise separable convolutions and residual connections in extracting complex features from brain MRI images. Therefore, models such as Xception and MobileNet are strong candidates for implementation in computer-aided diagnosis systems in resource-constrained clinical environments.
Copyright (c) 2025 Nurul Huda, Herman Yuliansyah, Maulany Citra Pandini https://creativecommons.org/licenses/by-sa/4.0
Utilization of Sentinel-2A Imagery to Analyze Vegetation Density Using the MSARVI Method in Cigugurgirang Village, Parongpong Sub-district, West Bandung Regency
Vegetation monitoring using vegetation indices is widely applied in satellite image analysis to understand vegetation distribution and density. However, its accuracy is often affected by atmospheric disturbances and soil background noise. MSARVI (Modified Soil and Atmospheric Resistance Vegetation Index) was developed to address these issues, yet its application remains limited. This study aims to analyze vegetation density and evaluate the accuracy of the MSARVI index using Sentinel-2A imagery in Cigugurgirang Village. The methodology includes processing Sentinel-2A imagery to calculate MSARVI values, classifying vegetation density levels, and validating results using field reference data. MSARVI is computed by integrating atmospheric and soil background corrections to enhance vegetation detection accuracy. The results indicate that Cigugurgirang Village is predominantly covered by high-density vegetation, spanning an area of 147.71 ha. The accuracy assessment yielded an overall accuracy of 87.50% and a kappa accuracy of 83.34%, demonstrating high reliability. These findings confirm that MSARVI is an effective method for vegetation density mapping with high accuracy.
Copyright (c) 2025 Arrafi Malika Ardy, Amelia Rosmayanti, Riki Ridwana, Lili Somantri https://creativecommons.org/licenses/by-sa/4.0
Characteristics of Lokodidi Formation Sandstone in Iloheluma Village, Gorontalo Regency
This research is motivated by the absence of a detailed study of the characteristics of the Lokodidi Formation sandstone in the Keramat area of ​​Boalemo Regency, although the distribution of sandstone in this area has been identified in previous studies. The main problem raised is the lack of data on the geological setting and mineralogical and geochemical characteristics of the Lokodidi Formation sandstone, which are important for understanding the geological processes and resource potential in the area. The purpose of this study was to determine the geological setting of the research area and to analyze the characteristics of the Lokodidi Formation sandstone based on petrographic and geochemical data. The methods used include field surveys for sampling, geomorphological observations, and laboratory analysis in the form of thin section preparation for petrography and X-Ray Fluorescence (XRF) analysis to determine the content of the main and trace chemical elements in the sandstone. The classification of sandstone is carried out based on the Pettijohn scheme and the parameters of the SiO₂/Al₂O₃ and Fe₂O₃/K₂O ratios. The results of the study indicate that the geomorphology of the study area consists of three main units: denudational hills, volcanic hills, and alluvial plains. The stratigraphy of the study area consists of five rock units, namely fine sandstone, medium sandstone, coarse sandstone, breccia, and andesite. Petrographic analysis shows that the sandstone samples are dominated by the Lithic Arenite type, which is characterized by the composition of quartz, feldspar, and lithic fragments. XRF geochemical analysis identified the presence of two main types of sandstone, namely Fe-shale and Quartz Arenite, based on the different Fe and quartz contents in each sample. These findings enrich the understanding of the characteristics of the Lokodidi Formation sandstone and provide a basis for further research related to the geological potential and mineral resources in the area.
Copyright (c) 2025 Alfi Ilda Sasmida, Aang Panji Permana, Ronal Hutagalung https://creativecommons.org/licenses/by-sa/4.0
Comparison of Anode Types on Electrical Power and Organic Removal using Microbial Fuel Cells Integrated with Anaerobic Baffled Reactor
The tofu production process produces liquid waste that has a high organic content and has the potential to pollute water bodies if not treated. The combination of Microbial Fuel Cells (MFCs) and Anaerobic Baffled Reactors (ABR) presents an effective solution to treat this liquid waste while generating electricity. The purpose of this study was to analyze the effect of the type of anode in the MFCs-ABR system on electrical power (mW) and COD reduction in tofu liquid waste. The methodology used includes processing tofu liquid waste with variations in the types of zinc and stainless steel anodes. The parameters observed were electrical power and COD reduction efficiency at retention times of 0, 24, 48, 72, and 96 hours. The results showed that the zinc anode produced electrical power reaching 0.38 mW and showed a COD reduction efficiency of 55.85% at 96 hours. The stainless steel anode produced electrical power of 0.18 mW and a COD reduction efficiency of 52.80% at 96 hours. This study shows that the type of anode affects the electrical power generated in the MFCs system.
Syadzadhiya Qothrunada Zakiyayasin Nisa'
Copyright (c) 2025 Syadzadhiya Qothrunada Zakiyayasin Nisa’, Restu H. A. Murti, Nurvita Cundaningsih, M. Ardiansyah Dwi Tama, Nur Laili A. Mukharomah https://creativecommons.org/licenses/by-sa/4.0
Optimization of Mechanical Properties of Rotary Friction Welding (RFW) Joints in AISI 1008 Steel with Friction Time Variation
One method than can be used to joint solid cylinders is Rotary Friction Welding (RFW). RFW is a Type solid-state welding that has the advantage of not requiring filler, shielding gas and produces good mechanical properties because the joining occurs below the base metal temperature. This study aims was to determine the effect of friction time on the physical and mechanical properties of Rotary Friction Welding (RFW) joints in AISI 1008 steel. RFW welding is carried out with a rotation speed of 1,170 RPM and friction plunge of 3 mm and forging depth 2 mm with three kinds of welding time, 3 minutes, 4 minutes and 5 minutes were investigated. The average temperature of RFW welding with variations of welding time (3-minute, 4-minute and 5-minute) are 1,022.3°C, 1034.5°C and 1,062.7°C. The physical properties of the weld joint were obtained from photomacro using an optical microscope and corrosion testing using the weight loss method. The mechanical properties of the RFW weld joint were determined from tensile and bending tests using a Universal Testing Machine as well as hardness testing using the Vickers Microhardness method. Photomacro observations show that the longer the welding time used in RFW welding, the wider the welded area. The test results show that optimal physical and mechanical properties were obtained on RFW weld joints with a welding time variation of 4 minutes. At the 4-minute variation, the highest tensile and bending strength values were obtained compared to other time variations. As for the hardness testing of the welding zone with a friction time of 4 minutes, it has the smallest value compared to other variations in welding time (3 minutes and 5 minutes). For the base metal and the HAZ, the hardness values for all variations are relatively the same because the materials used are the same and the welding heat is not high enough to change the microstructure in the area. Corrosion testing conducted for 50 days showed that all RFW welds with welding time variations had excellent corrosion resistance values.
Copyright (c) 2025 Yustiasih Purwaningrum, Rachnaldy Putra, Excel Rifky Fachreza https://creativecommons.org/licenses/by-sa/4.0
Implementation of Energy Harvesting Technology for Sustainable Infrastructure Projects
Digital construction has become an important strategy in promoting the Sustainable Development Goals (SDGs) set by the United Nations, especially in the context of infrastructure development. The purpose of this research is to discuss the development of Energy Harvesting (EH) technologies that have been investigated in infrastructure to date. By utilizing energy in the surrounding environment, EH technology offers great potential in reducing carbon emissions generated by infrastructure systems. This study employs the Systematic Literature Review (SLR) method, involving a structured process of literature search in the Scopus database, selection based on abstracts and content relevance, and in-depth analysis of 27 qualified articles. This research discusses five of the most common EH technologies, namely photovoltaic, piezoelectric, thermoelectric, Wind Energy Conversion System (WECS), and Wind Energy Converter (WEC) technologies. Each technology was evaluated based on its application in infrastructure with a focus on the efficiency and effectiveness of the application of the technology. The results indicate that Photovoltaic and Piezoelectric technologies are the most ready for short-term implementation, while Thermoelectric and WEC technologies show strong long-term potential despite facing technical and economic challenges. Hybrid combinations of multiple technologies have also been proven to enhance efficiency compared to single systems. The harvested energy can be utilized for various infrastructure needs, such as street lighting and Structural Health Monitoring (SHM) systems. This study recommends the application of hybrid technologies as a more efficient solution, tailored to the characteristics of the location and type of infrastructure. In addition, policy support and incentives are needed to accelerate the adoption of these technologies. By implementing these strategies, the construction industry can significantly contribute to achieving the UN's 2030 agenda for sustainable development.
Copyright (c) 2025 Timotius Alfanov, Oei Fuk Jin https://creativecommons.org/licenses/by-sa/4.0
Development of a Small-Capacity Tensile Testing Machine for Educational Demonstration
The tensile testing machine is a device used to characterize the mechanical properties of materials. Most tensile testing machines currently available are designed for high-capacity applications, such as testing metals and concrete, making them less suitable for testing composite and plastic materials. Therefore, a small-capacity tensile testing machine that is more flexible and efficient is needed for testing and educational purposes. This study aims to develop and improve the performance of a small-capacity tensile testing machine by enhancing its control system and human interface. Improvements include increasing the displacement sensor sensitivity from 200 mm to 50 mm, using a load cell with a more appropriate capacity (reduced from 2 tons to 1 ton), and adding a data logging feature via USB in text format. The test results indicate that the developed system is capable of performing tensile tests in accordance with ASTM D638 standards, with higher accuracy compared to the previous version. The implementation of the new control system also enables better data recording, thereby enhancing the machine's reliability as a tool for education and research.
Copyright (c) 2025 Paryana Puspaputra, Hendra Setiawan, Arif Rahman Hakim https://creativecommons.org/licenses/by-sa/4.0
Circular Economy for Sustainable Steel Structure
Steel construction contributes significantly to global carbon emissions and energy consumption. In response to sustainability concerns, circular economy principles offer a promising approach to reducing waste, maximizing material reuse, and minimizing environmental impact. This study presents a systematic literature review of 317 Scopus-indexed articles published between 2010 and 2024. The selection focused on reputable journals (Q1–Q4), environmentally friendly steel structures, reuse, and life cycle assessment (LCA). Bibliometric and keyword mapping analysis using VOSviewer revealed that the topic of steel structure reuse and sustainability is still fragmented. Design for Deconstruction (DfD) practices were found to reduce emissions by up to 70% compared to new structures but face practical limitations due to technical and economic challenges. Moreover, the reuse of industrial by-products such as steel slag and blast furnace gas shows high potential for energy savings and CO₂ reduction, although adoption remains limited. These findings highlight the need for innovative joint design, technological integration, and policy incentives to advance circular economy implementation in steel construction.
Copyright (c) 2025 Dian Laras Wati, Oei Fuk Jin https://creativecommons.org/licenses/by-sa/4.0
Effect of Time Variation and Mole Ratio on Biodiesel Production from Coconut Oil Using CaO Catalyst via Transesterification Method
The use of fossil fuels by the world resulted in an impulse for the use of renewable energy sources including biodiesel. In the present investigation, the objective is to find out the best conditions of coconut oil transesterification into biodiesel for transesterification source of the homogeneous basic catalyst of CaO. The experiments were conducted by varying the reaction time (100, 120 and 150 min), the molar ratio of coconut oil to methanol (1:12, 1:15 and 1:18), and temperature (60°C) as a fixed level in the experimental plan The optimum condition of reaction was at 1:15 molar ratio and a time of 120 min producing biodiesel that meets the (SNI 7182:2015) Indonesian National Standard with properties; density was 858.20±26.95 kg/m3, viscosity was 2.64±0.22 cSt, methyl ester content was 96.14±0.73%. Furthermore, the utilization of CaO catalyst not only improved the quality of biodiesel, but also possessed economic and environmental benefits compared with traditional homogeneous catalysts. These results confirm the feasibility of using coconut oil as a cheap and sustainable raw materials for biodiesel production as well as their effective use in larger scale production of renewable energy.
Copyright (c) 2025 Grace Trinita, Dessy Agustina Sari, Muhammad Fahmi Hakim https://creativecommons.org/licenses/by-sa/4.0
The Role of Green-Synthesized Fe3O4/Ag Magnetic Nanoparticle Masses on the Specific Absorption Rate toward Magnetic Hyperthermia Applications
With chemotherapy and radiation therapy being the most common cancer treatments today, there are questions about their effectiveness and risks.Therefore, an alternative method that does not have significant side effects is needed, such as magnetic hyperthermia therapy (HM).The mass of magnetic nanoparticles (MNP) and the coating of MNP as anti-cancer agents play a very important role in optimizing the HM method, characterized by the Specific Absorption Rate (SAR) value.This study examines the effect of the mass of iron oxide magnetic nanoparticles with a silver coating (Fe3O4/Ag) on the Specific Absorption Rate (SAR) value. MNP Fe3O4/Ag was synthesized using the green synthesis (GS) method with Moringa oleifera (MO) plant extract.The results of this study show that GS-NPM Fe3O4/Ag has a crystal size between 42.69 - 43.58 nm with a bandgap energy of 2.50 eV, and contains functional groups O-H, C-H, N-Ag, Fe-O, and Fe-O-Si.There was a temperature change in the NPM Fe3O4/Ag for all mass variations, ranging from 0°C to 14.2°C over a period of 600 seconds, using a frequency of 15 kHz and an alternating magnetic field amplitude of 150 Oe.This temperature change indicates that the greater the mass of the NPM sample, the higher the NPM temperature. The lowest and highest SAR values obtained, consecutively, were 0.43 W/g at a mass of Fe3O4/Ag 0.125 grams and 1.11 W/g at a mass of 0.025 grams.
Copyright (c) 2025 Dhel Prisca Daro Kowe, Mahardika Yoga Darmawan, Nurul Imani Istiqomah, Edi Suharyadi, Nur Aji Wibowo https://creativecommons.org/licenses/by-sa/4.0