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 243 Documents
 21   22   23   24   25 
A Comparative Evaluation of XGBoost and LightGBM for Diabetes Mellitus Risk Prediction Using a Public Dataset and Web-Based Dashboard Wahyuningtyas, Sischa; Al Hafidz, Muhammad
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 7, ISSUE 1, April 2026
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol7.iss1.art11

Abstract

Diabetes mellitus is a health problem of global concern, considering that most cases are only identified when complications arise. Therefore, early detection is essential in controlling the health and financial consequences of the disease. The purpose of this study is to compare two machine learning models using gradient boosting techniques, namely Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM). This study use a technique called RandomizedSearchCV to optimize the performance of the proposed machine learning models. In evaluating the machine learning models, the study used a variety of metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. The LightGBM is a more efficient machine learning model than XGBoost based on the result. The LightGBM model had a classification accuracy of 77.3%, a precision of 71.1%, and a recall of 59.3%, which is the same value obtained by the XGBoost model. However, the LightGBM model had a higher F1 score of 64.6% and a ROC-AUC of 83.0% which indicates that the model is more balanced and can accurately classify and distinguish between the two classes. The best-performing machine learning model was integrated with a web-based system using a framework called Streamlit to create a system that is responsive, interactive, and user-friendly. The system is useful for early detection of diabetes mellitus and can be used by non-experts to determine whether a patient is at risk of developing the disease using real-time prediction and user-friendly data input. The results of the study showed that gradient boosting machine learning models can be used to diagnose and detect early cases of diabetes mellitus.
Effect of Phenol–Formaldehyde Molar Ratio on the Physicochemical Properties of Phenolic Synthetic Tanning Agent (Syntan) Winata, Wahyu Fajar; Hermawan, Prasetyo; Nurbalia, Elis
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 7, ISSUE 1, April 2026
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol7.iss1.art10

Abstract

Phenolic synthetic tanning agents (syntans) are widely used in the leather industry due to their controllable properties and consistent performance. This study aims to investigate the effect of phenol– formaldehyde molar ratio on the physicochemical properties of syntans synthesized via sulfonation. The syntans were prepared using various molar ratios (0:1, 0.5:1, 1:1, 1.5:1, 1:0.5, and 1:0) throughcondensation followed by sulfonation. The products were characterized in terms of pH, specific gravity, viscosity, degree of sulfonation (DS), and functional groups using FTIR spectroscopy. The results show that increasing phenol or formaldehyde concentration led to higher specific gravity and viscosity, indicating increased polymerization. The pH values ranged around ~4, confirming suitability for leather processing. The DS decreased with increasing phenol or formaldehyde concentration, suggesting competition between polymerization and sulfonation reactions. The optimal molar ratio (1:1) produced the most homogeneous and stable syntan system. The pH values of all syntans were consistently around ~4, indicating suitability for leather processing. Increasing phenol or formaldehyde concentration resulted in higher viscosity and specific gravity, while the degree of sulfonation decreased significantly, confirming the trade-off between polymerization and sulfonation reactions. In conclusion, the phenol–formaldehyde molar ratio significantly influences syntan properties by controlling the balance between polymerization and sulfonation. These findings provide a scientific basis for optimizing syntan formulation and improving efficiency in sustainable leather processing.
Production of Protease Enzymes and Bioactive Compounds by Lactic Acid Bacteria (LAB) From Broccoli (Brassica oleracea L.) Using Lactobacillus plantarum Starter Hidayat, Habibi; Maharani, Najlaa Ayu; Maulani, Qisti; Fajarwati, Febi Indah
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 7, ISSUE 1, April 2026
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol7.iss1.art12

Abstract

This study aimed to isolate and characterize Lactic Acid Bacteria (LAB) from broccoli (Brassica oleracea L.) with potential protease-producing activity and probiotic properties. Fermentation was carried out using Lactobacillus plantarum as a starter culture. The obtained LAB isolates were subsequently evaluated for protease activity and analyzed for bioactive compound content. The results demonstrated that LAB isolates obtained from broccoli were capable of producing proteases with significant activity, indicating their potential for biotechnological applications. In addition, the isolates produced bioactive compounds exhibiting antibacterial activity against several pathogenic bacteria, including S. epidermidis, K. pneumoniae, E. coli, S. aureus, S. typhi, and S. pyogenes, as evidenced by the formation of inhibition zones. The isolate demonstrated strong antibacterial activity against all tested pathogens with inhibition zones of 56 mm for S. epidermidis, 30 mm for S. typhi, 12 mm for both K. pneumoniae and E. coli, and 11 mm for S. pyogenes and S. aureus. Acid tolerance assays demonstrated that the LAB isolates survived at low pH, and protease activity assays showed that the isolate produced an enzyme with proteolytic activity of 23.5 mm and 0.0440 U/mL.

Page 25 of 25 | Total Record : 243

 21   22   23   24   25 

Filter by Year

2010 2026


Reset
Filter By Issues
All Issue VOLUME 7, ISSUE 1, April 2026 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