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JRST (Jurnal Riset Sains dan Teknologi)
Published by Universitas Muhammadiyah Purwokerto
ISSN : 25799118     EISSN : 25499750     DOI : http://dx.doi.org/10.30595/jrst
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering
JRST (Jurnal Riset Sains dan Teknologi) adalah jurnal peer reviewed dan Open-Acces. JRST merupakan jurnal yang diterbitkan oleh Lembaga Publikasi Ilmiah dan Penerbitan (LPIP) Universitas Muhammadiyah Purwokerto. JRST mengundang para peneliti, dosen, dan praktisi di seluruh dunia untuk bertukar dan memajukan keilmuan di bidang sains dan teknologi yang meliputi bidang Matematika, Kimia, Biologi, Teknologi Rekayasa dan Keteknikan, Farmasi, Geografi, Komputer dan Teknologi Informasi. Dokumen yang dikirim harus dalam format Ms. Word dan ditulis sesuai dengan panduan penulisan. JRST terbit 2 kali dalam setahun pada bulan Maret dan September.
Arjuna Subject : Energi (semua kategori) - Energi (Lain-Lain)
Articles 23 Documents
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Search results for , issue "Volume 9 No. 2 September 2025: JRST" : 23 Documents clear
Characteristics of Lokodidi Formation Sandstone in Iloheluma Village, Gorontalo Regency Aang Panji Permana; Alfi Ilda Sasmida; Ronal Hutagalung
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.22456

Abstract

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.
The Role of Green-Synthesized Fe3O4/Ag Magnetic Nanoparticle Masses on the Specific Absorption Rate toward Magnetic Hyperthermia Applications Dhel Prisca Daro Kowe; Mahardika Yoga Darmawan; Nurul Imani Istiqomah; Edi Suharyadi; Nur Aji Wibowo
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.22884

Abstract

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.
Leveraging Linear Discriminant Analysis for Early Mental Health Disorder Identification Deden Iwan Setiawan; Marselina Endah Hiswati; Sriwidodo Sriwidodo; Mohammad Diqi; Luh Putu Erikawati; Rahayu Cahya Ariani
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.23053

Abstract

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.
Optimization of AI Usage in Learning Materials Application of Prompt Engineering Techniques for Learning Management Systems (LMS) Muhammad Fauzan Gustafi
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.23393

Abstract

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.
Utilization of Sentinel-2A Imagery to Analyze Vegetation Density Using the MSARVI Method in Cigugurgirang Village, Parongpong Sub-district, West Bandung Regency Arrafi Malika Ardy; Amelia Rosmayanti; Riki Ridwana; Lili Somantri
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.23470

Abstract

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.
Decision Support System for New Employee Recruitment Using Profile Matching and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) Method Elsi Titasari Br Bangun; Retantyo Wardoyo; Yudha Islami Sulistya; Bayu Anugerah Putra; Fauzan Azim
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.23640

Abstract

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.
Prediction of Vaccine Inventory in Infants with Holt-Winter's Exponential Smoothing Method (Case Study: East Java Province) Dian Puspita Sari; Aris Fanani; Susilo Ari Wardani; Wika Dianita Utami
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.23659

Abstract

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.
Network Analysis Reveals Tea Catechins as Multi-Target Inhibitors of Colon Adenocarcinoma Anwar Rovik; Dyah Fitri Kusharyati
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.23742

Abstract

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.
Circular Economy for Sustainable Steel Structure Dian Laras Wati; Oei Fuk Jin
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.24151

Abstract

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.
Identification and Characterization of Plant Vegetation in the Very Acid Environment of Kawah Putih Bandung Anik Nafisah Maulida; Muhamad Jalil
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.24186

Abstract

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.

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