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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 231 Documents
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Temporal and Spatial Analysis of Vegetation and Non-Vegetation Using Landsat 8 Imagery with a Support Vector Machine Approach Syifa Fauziyah; Fauzan, Achmad
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 1, April 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss1.art5

Abstract

Kertajati International Airport is one of the newest airports located in Majalengka regency, West Java province. The establishment of this airport has sparked interest as a case study, particularly regarding land-use changes around the Kertajati area and, more broadly, in Majalengka Regency. This study aims to measure the extent of changes in vegetated and non-vegetated land around Kertajati International Airport, Majalengka Regency, West Java Province. The methodology employed involves the Support Vector Machine (SVM) classification method. Various kernels, including (1) linear, (2) polynomial, (3) radial basis function (RBF), and (4) sigmoid, were simulated in the analysis. The data used in this study comprise Landsat 8 satellite imagery obtained from Google Earth Engine, utilizing bands such as red, green, blue, near-infrared (NIR), and shortwave infrared (SWIR) for the years 2013 and 2023. The dataset was split using the hold-out method into four scenarios, with varying training and testing data proportions: 75%-25%, 80%-20%, 85%-15%, and 90%-10%. Each scenario was repeated 40 times to ensure robust results. The best results were achieved using the SVM model with an RBF kernel at a data split ratio of 75%-25%, as indicated by the highest accuracy scores. Consistent with the accuracy, the evaluation metrics also fell into a high-performance category. Land area predictions for the vicinity of Kertajati International Airport were analyzed based on the optimal data split proportion. The results of this study reveal a significant reduction in vegetated land from 2013 to 2023, accompanied by a notable increase in non-vegetated land over the same period.
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.
bioplastik PENGARUH PEMLASTIS GLISEROL DAN SORBITOL TERHADAP KARAKTERISTIK BIOPLASTIK BERBAHAN PATI UBI MBOTE Hamzah, Imron; N, Lailatin; S, Gancang
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 5, ISSUE 1, April 2024
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol5.iss1.art6

Abstract

Bioplastics are plastic biopolymers made from starch that are easily degraded by soil microbes and may be an alternative to traditional plastics. Mbote tuber starch may be used as the main mass for the production of bioplastics by adding glycerin and sorbitol plasticizers. This research aims to determine the influence of plasticizers with different composition. Starch making, mixing the ingredients with plasticizer, heating, printing and drying are the sequence steps of making bio-plastic. In this study, plasticizers used glycerol and sorbitol with variations in the composition of the plasticizers 0, 0.6, 0.9, 1.2, 1.5 ml. The results showed the maximum tensile strength of glycerol bioplastic (56.12 out of 0.06) MPa with elongation (5.23 to 1.43)%, for sorbitol bioplastic the maximum tensile strength was (70, 66 then 0.09) MPa and it has an elongation value (7.85 then 0.08)%. The maximum degradability test of glycerol emollient showed that the emollient volume was 46.93% and the resolution was 3.28 mg per day and it took 14 days and 9 hours to completely reduce, while the sorbitol emollient obtained meant 1, It was 2, 4%. 5 mg is required for decomposition for 11 days and 4 hours for / day and overall reduction. The maximum area test of gliserol plasticizer is volume 0,9 plastisizer was 20,00±0,39 %, for sorbitol plastisizer is volume 1,5 plastisizer wes 14,00±0,51 %. Key words : Plasticizer Glycerol and Sorbitol, Mbote tuber starch, Bio-plastics

Page 22 of 24 | Total Record : 231

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