cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Is Fatimah
Contact Email
eksakta@uii.ac.id
Phone
+6282326298724
Journal Mail Official
eksakta@uii.ac.id
Editorial Address
Faculty of Mathematics and Natural Sciences Universitas Islam Indonesia Jl. Kaliurang Km 14, Ngaglik, Sleman, Yogyakarta, 55584
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
EKSAKTA: Journal of Sciences and Data Analysis
Published by Universitas Islam Indonesia
ISSN : 27160459     EISSN : 27209326     DOI : 10.20885
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Materials Science & Nanotechnology
Ekstakta is an interdisciplinary journal with the scope of mathematics and natural sciences that is published by Fakultas MIPA Universitas Islam Indonesia. All submitted papers should describe original, innovatory research, and modelling research indicating their basic idea for potential applications. The Journal particularly welcomes submissions that focus on the progress in the field of mathematics, statistics, chemistry, physics, biology and pharmaceutical sciences.
Arjuna Subject : Kimia(semua kategori) - Kimia (Lain-Lain)
Articles 9 Documents
 1 
Search results for , issue "VOLUME 6, ISSUE 2, October 2025" : 9 Documents clear
Retention of Sodium Alginate-Based Mucoadhesive Ranitidine Ifa Nurazizah; Suparmi; Kusuma, Aris Perdana
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 2, October 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss2.art7

Abstract

Ranitidine, a histamine H2-antagonist, has an oral bioavailability of 50-60% and an elimination half-life of approximately 2 to 3 hours. To enhance its therapeutic efficacy, ranitidine must remain in the stomach for an extended period. A mucoadhesive gastroretentive drug delivery system can improve its bioavailability. This study formulated ranitidine granules using sodium alginate as a polymer via wet granulation. Formulations with varying sodium alginate concentrations (7-11%) were prepared and evaluated for flow properties, tapping properties, moisture content, swelling capacity, and dissolution. The formulation with 11% sodium alginate demonstrated optimal properties. It achieved a flow rate of 12.3±0.23 g/s, an angle of repose of 27.13±0.63°, a compressibility index of 21.35±2.23%, a Hausner ratio of 1.32±0.07, a moisture content of 2.59±0.2%, a swelling index of 72.85±3.48%, and a wash-off time of 77.34±48.75 minutes. Additionally, over 80% of the drug was dissolved. In conclusion, the 11% sodium alginate formulation is the most promising for mucoadhesive ranitidine delivery.
Optimasi Hyperparameter Model Klasifikasi Citra untuk Daging Sapi dan Babi Menggunakan Convolutional Neural Networks -, Salsabila; Anwar Fitrianto; Bagus Sartono
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 2, October 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss2.art6

Abstract

Deep learning classification network in one case, has different classification capabilities than the network in another. The classification method of deep learning using CNN has specific hyperparameters that can be adjusted to have good performance. These hyperparameters include the number of convolutional layers, the number of neurons in the convolutional and fully connected layers, kernel size, and activation functions. Deep Learning uses experimental principles in finding the best hyperparameter in various cases. The model architecture can be determined by choosing a different design. This research uses pork and beef images as the data for classification using CNN. The abstract textures of beef and pork may make it difficult for the CNN classification model to distinguish between them. Hence, 32 combinations of five hyperparameters were compared. It was found that these hyperparameters affect the model's performance. The best model has obtained 98,7% accuracy that uses 20 neurons both layers of the convolution was, kernel size of 5 × 5, ReLU activation function, and two fully connected layers with dropout 0.7 as a method of overfitting prevention. A significant difference also occurs in the application of the activation function, in which ReLU has a better performance than tanh function to increase the model's prediction.
Deep Learning for Lung Disease Diagnosis: A CNN-Based Radiographic Approach: Deep Learning for Lung Disease Diagnosis Danang Bagus Wibowo; Fauzan, Achmad
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 2, October 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss2.art5

Abstract

Various types of lung diseases affect the human respiratory system, with pneumonia, tuberculosis, and Covid-19 being among the most common. Early detection plays a crucial role in improving treatment outcomes and reducing mortality rates. Chest X-ray imaging is one of the most widely used diagnostic methods; however, it typically relies on manual interpretation by medical professionals, which can be time-consuming and prone to inconsistencies. This study aims to apply the Convolutional Neural Network (CNN) method as an automated approach to classify chest X-ray images of lung conditions. The dataset consists of 460 X-ray images for each category: normal, pneumonia, tuberculosis, and Covid-19. The CNN model was trained using an input shape of 224×224 pixels, a 3×3 filter size, and 5 epochs. Evaluation results showed that the model achieved 97% accuracy on the validation and 93% on the testing data. These findings highlight the potential of CNN in supporting automated diagnosis of lung diseases. In the future, this technology is expected to assist healthcare professionals in delivering faster and more accurate diagnoses, particularly in areas with limited access to radiology experts. Moreover, this innovation aligns with Sustainable Development Goal (SDGs) 3: Good Health and Well-being, by promoting early detection, timely treatment, and more equitable access to quality healthcare services.
Bayesian Inference and Logistic Regression Based Modelling for Earthquake Probability Estimation in East Java Aisyah Tur Rif’atin Nurdini; Amiroch, Siti; Siti Alfiatur Rohmaniah
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 2, October 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss2.art4

Abstract

East Java is one of the seismically active regions in Indonesia, yet predictive studies that integrate spatial data and event parameters remain limited. This study develops a two-stage approach to model earthquake risk more comprehensively by combining Bayesian inference and logistic regression. The first stage employs a Bayesian model to estimate the daily probability of earthquake occurrence based on historical data from 2014 to 2024. The results show an average daily probability of 13.5%, with a 95% credible interval indicating a high level of confidence. Spatially, Region 1 (covering southern East Java) is identified as the area with the highest probability, followed by Region 3 and Region 2. In the second stage, logistic regression is used to identify combinations of event parameters—particularly magnitude and depth—that significantly influence the likelihood of moderate-to-major earthquakes (magnitude ≥ 5.0). The prediction results indicate that most high-risk events occur at shallow depths in Region 1 and Region 3, while Region 2 appears less frequently but still presents underlying geological hazards. These findings demonstrate that integrating probabilistic modeling with parameter-based classification offers a more refined understanding of earthquake risk. As an initial framework, this study also opens avenues for developing future early warning systems based on dynamic data and machine learning methods.
Itu Analisis faktor kejadian batu empedu menggunakan model regresi logistik biner. Amri, Ihsan Fathoni; Rohim, Febrian Hikmah Nur; Nurul Azka, M. Ilham; Rakhmawati, Muji Silvi
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 2, October 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss2.art3

Abstract

Gallstone disease (cholelithiasis) is a digestive system disorder with a globally increasing prevalence. This study aims to identify risk factors contributing to the occurrence of gallstones using a logistic regression model. The data were obtained from the UC Irvine Machine Learning Repository, comprising a total of 319 outpatients from Ankara VM Medical Park Hospital, Turkey. The analysis was conducted on 23 independent variables, including demographic characteristics, body composition, medical history, and laboratory results. The Chi-Square test identified four significant variables, while the Wald test revealed six statistically significant predictors of gallstone occurrence: age, comorbidities, diabetes mellitus, visceral fat rating, visceral fat area, and vitamin D levels. Diabetes mellitus emerged as the most dominant risk factor (OR = 11.5), whereas higher levels of vitamin D showed a protective effect. The logistic regression model demonstrated a classification accuracy of 77%, indicating good predictive performance. These findings are expected to support early detection, clinical decision-making, and preventive interventions for more effective gallstone prevention.
Aktivitas Penghambatan enzim Dipeptidyl Peptidase 4 (DPP-4) dari ekstrak dan seskuiterpen lakton yang Diisolasi dari Daun Yacon (Smallanthus sonchifolius) Tamhid, Hady Anshory; Hertiani, Triana; Murti, Yosi Bayu; Murwanti, Retno
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 2, October 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss2.art2

Abstract

The Sesquiterpene lactones, enhydrin and uvedalin, are the chemotype and subtype compounds of the yacon leaf. They are known to have anti-diabetic activity, but their mechanisms are not clear. One of the mechanisms of anti-diabetic agents is to inhibit the activity of the dipeptidyl peptidase 4 enzyme (DPP-4). The research aimed to determine the inhibition of the extract, enhydrin, and uvedalin isolated from the yacon leaf against the DPP-4 enzyme activity. Yakon leaves were extracted using 70% ethanol by the maceration method. Enhydrin and uvedalin were obtained from previous research. The inhibitory activity of the DPP-4 enzyme was then determined by the fluorescence assay method using a multi-well plate reader. Sitagliptin was used as standard inhibitor of DPP-4 enzyme. The ethanol extract of yacon leaves inhibits the DPP-4 enzyme with an IC50 of 856.9 ppm. The percentage inhibition of the DPP-4 enzyme by the enhydrin and uvedalin at a concentration of 250 ppm was 2.37 and 35.16%, respectively. The inhibitory potency of the pure isolated compounds was not substantially greater than that of the crude extract itself. This led us to the conclusion that the overall contribution of enhydrin and uvedalin to the extract's DPP-4 inhibitory activity is actually modest, suggesting the presence of other active compounds within the extract. However, it may provide a new approach to the treatment of type 2 diabetes.
Ni-Biochar dari limbah daun kelapa sawit sebagai fotokatalis berbiaya rendah untuk degradasi zat warna metil jingga Dwiki Ramanda, Galih; Is Fatimah; Suresh Sagadevan; Mohd Rafie Bin Johan
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 2, October 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss2.art1

Abstract

Photocatalytic oxidation is one of the methods in the advanced oxidation process. The method depends on several factors to be effective in application, one of these is the cost-effectivity and sustainability. The intent of this research work is to synthesize low-cost photocatalyst by using waste: palm leaves ash. Lignocellulosic as a structural compenent found in palm leaves has the potential for various applications, including as a catalyst for wastewater treatment. The Ni-biochar (Ni-BC) photocatalyst based on nickel nanoparticles was synthesized by using palm leaves under pyrolysis method on two varied Ni content; 10 and 30 % wt to get Ni10-BC and Ni30-BC, respectively. Various characterization techniques consist of XRD, SEM, and VSM were conducted, meanwhile the photocatalytic activity for methyl orange photodegradation was employed as activity testing. Results showed that single nickel nanoparticles dispersed on biochar structure are appeared by XRD measurement. The porous structure of materials is derived, with the magnetism of 9.64 emu/g and 3.87 emu/g for Ni content of 10 and 30 % wt., respectively. The fabricated Ni-BC samples showed excellent photoactivity represented by 74.73 and 50.01% of the degradation efficency towards methyl orange by Ni10-BC and Ni30-BC, respectively. A persuasive mechanism and kinetics are well presented. The kinetics study expressed the fitness of the degradation that follow pseudo-first order kinetics.
Performance Evaluation of 119.88 kWp IoTBased On-Grid Solar System at Admin Building Grissik Putra, Asri Eka; Hasan, Abu; Bow, Yohandri
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 2, October 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss2.art8

Abstract

The global rise in carbon emissions has intensified the urgency of transitioning toward renewable, environmentally friendly and sustainable energy systems, particularly in industrial sectors with high fossil fuel dependency such as oil and gas. Solar panels represent a clean and reliable alternative for electricity generation. This research evaluates the performance of a 119.88 kWp monocrystalline solar panel system integrated with an Internet of Things (IoT)-based on-grid monitoring system at the Grissik Administration Building. Over a 30-day observation period, the solar panels supplied an average of 432 kWh/day, approximately 72.07% of the installed capacity, reducing fuel gas consumption by 0.19 MMSCFD and lowering CO₂ emissions by 10.38 tons. System efficiency exceeded 80% under optimal irradiation conditions. The IoT-based monitoring platform facilitated real-time data and system control, improving operational decision-making and reliability. This research provides novel empirical evidence of field-scale performance of IoT-integrated photovoltaic systems within Indonesia’s oil and gas facilities, demonstrating their significant role in enhancing industrial energy efficiency and supporting the national clean energy transition.
Characterization and Application of Greener-Activated Coal Fly Ash for Phosphate Adsorption in Donan River Water Samples Rohmah, Dini Novi; Nur Mas'udah, Aldina Khoerunnisa; Sudariasri, Vida Zenitha; Boikh, Mesakh Trywira Wibowo; Moneteringtyas, Prisca Caesa
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 2, October 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss2.art9

Abstract

The Karangkandri PLTU (Steam Power Plant) Industry in Cilacap is one of the large industries in the Cilacap industrial area, which has abundant coal-burning waste in the form of fly ash. The excess fly ash from PLTU Karangkandri can be used for the adsorption of polluted phosphate ions in the Donan River. The aim of this research is to determine the characterization of fly ash as an adsorbent using FTIR (Fourier Transform Infra-Red), XRD (X-Ray Diffraction), and XRF (X-Ray Fluorescence) instruments and its ability to adsorb phosphate ions in a polluted water sample. The first stage of this research was to take water samples from the Donan River at the 3 most polluted points. The second stage was to activate fly ash using wet grinding and hydrothermal methods as well as characterization of fly ash using FTIR, XRD, and XRF instruments. Then the effectiveness of the fly ash adsorbent in adsorbing phosphate ion was tested by using the spectrometric method. The analysis was carried out with the influence of mass variations in the adsorbent of 50, 100, 150, and 200 grams per 500 ml of water sample. This research found that activated fly ash by greener activation can removed 82,8% phosphate ion with 0,5g fly ash, remove 72.4% phosphate ion with 1 gram fly ash, removed 77.6% phosphate ion with 2 grams of fly ash. And the best one is 5 grams of fly ash can remove 93.2% phosphate ion.

Page 1 of 1 | Total Record : 9

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue VOLUME 6, ISSUE 2, October 2025 VOLUME 6, ISSUE 1, April 2025 VOLUME 5, ISSUE 2, October 2024 VOLUME 5, ISSUE 1, April 2024 VOLUME 4, ISSUE 2, August 2023 VOLUME 4, ISSUE 1, February 2023 VOLUME 3, ISSUE 2, August 2022 VOLUME 3, ISSUE 1, February 2022 VOLUME 2, ISSUE 2, August 2021 VOLUME 2, ISSUE 1, February 2021 VOLUME 1, ISSUE 2, August 2020 VOLUME 1, ISSUE 1, February 2020 VOLUME 19, ISSUE 2, August 2019 VOLUME 19, ISSUE 1, February 2019 VOLUME 18, ISSUE 2, August 2018 VOLUME 18, ISSUE 1, February 2018 VOLUME 17, ISSUE 2, August 2017 VOLUME 17, ISSUE 1, February 2017 VOLUME 16, ISSUE 2, August 2016 VOLUME 16, ISSUE 1, February 2016 VOLUME 15, ISSUE 1-2, 2015 VOLUME 14, ISSUE 2, August 2014 VOLUME 14, ISSUE 1, February 2014 VOLUME 13, ISSUE 1-2, August 2013 VOLUME 13, ISSUE 1, February 2012 VOLUME 12, ISSUE 2, August 2011 VOLUME 12, ISSUE 1, February 2011 VOLUME 11, ISSUE 2, August 2010 VOLUME 11, ISSUE 1, February 2010 More Issue