cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Christian Harito
Contact Email
christian.harito@binus.edu
Phone
+6221-5350660
Journal Mail Official
aagung@binus.edu
Editorial Address
Universitas Bina Nusantara Jl. Kebon Jeruk Raya No.27 Kebon Jeruk, Jakarta Barat 11530
Location
Kota adm. jakarta barat,
Dki jakarta
INDONESIA
Engineering, Mathematics and Computer Science Journal (EMACS)
Published by Universitas Bina Nusantara
ISSN : -     EISSN : 26862573     DOI : https://doi.org/10.21512/emacs
Core Subject : Science, Education, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics
Engineering, MAthematics and Computer Science (EMACS) Journal invites academicians and professionals to write their ideas, concepts, new theories, or science development in the field of Information Systems, Architecture, Civil Engineering, Computer Engineering, Industrial Engineering, Food Technology, Computer Science, Mathematics, and Statistics through this scientific journal.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 174 Documents
 14   15   16   17   18 
Binary Classification of Asthma for the CAPS Pediatric Dataset in Malawi Using Machine Learning Sodiq, Jaffarus; Syarifah Diana Permai
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.14108

Abstract

Childhood asthma poses a significant public health challenge, especially in low- and middle-income countries. An early intervention is essential for effective management and improved prevention of Childhood asthma. This study aims to develop a predictive model for childhood asthma by applying machine learning (ML) techniques. The dataset includes self-reported information on respiratory symptoms, anthropometric measurements, spirometry data, and personal carbon monoxide (CO) exposure among children aged 6–8 years in rural Malawi. We employed a supervised ML approach, focusing on classification algorithms and handling imbalanced outcomes, including Random Forest, Logistic Regression, and XGBoost. Additionally, this study applied the Synthetic Minority Over-sampling Technique (SMOTE), creating synthetic samples of the minority class to balance the distribution of the outcome variable in the training data. Data preprocessing involved handling missing values, feature selection, and normalization to ensure data quality and model performance. Model evaluation was conducted using cross-validation and performance metrics, including precision, recall, and F1-score. Among the evaluated models, Logistic Regression emerged as the most balanced approach, offering strong precision and the highest F1-score while maintaining a reasonable recall rate. This balance reduces the likelihood of overdiagnosis while still capturing a significant proportion of true positives, making it suitable for early screening applications. Moreover, Logistic regression, with its simple mathematical structure, provides more transparency and explainability, which are vital for clinical adoption and gaining practitioner trust.
Web-Based Quality Control Dashboard Design for Data Validation and Monitoring: A Case Study of BMKG Instruments Purwandari, Kartika; Aufauzan, Brian Tirafi; Sigalingging, Join Wan Chanlyn
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 1 (2026): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i1.13532

Abstract

Accurate meteorological data are vital for the operational activities of the Agency for Meteorology, Climatology, and Geophysics (BMKG), specifically for weather forecasting and disaster mitigation. However, Automatic Weather Station (AWS) instruments frequently encounter sensor degradation and technical malfunctions, which compromise data validity. Traditional manual validation is inefficient and prone to human error. This study addresses these gaps by designing a web-based Quality Control (QC) dashboard for real-time AWS data monitoring. Developed using the Laravel framework and PostgreSQL, the system integrates Leaflet.js and Chart.js for interactive spatial and analytical visualization. Using the Agile Scrum methodology, the development process was iteratively refined across eight sprints. Implementation results show a significant improvement in data validation accuracy and a reduction in potential human error. User Acceptance Testing (UAT) with fifteen BMKG specialists confirms high usability, with the system receiving "Strongly Agree" ratings for its efficiency in real-time monitoring and reporting. The practical implications include enhanced data credibility for national climate modeling. This paper concludes that while the dashboard streamlines workflows, future iterations should incorporate automated anomaly detection algorithms. Limitations include a current reliance on static validation thresholds, suggesting a need for machine learning integration in future research.
Measuring Student Satisfaction with Academic Applications at BINUS University Through the Customer Satisfaction Score (CSAT) Framework Fransisco, Mario
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 1 (2026): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i1.14540

Abstract

This study examines student satisfaction with four key academic applications used at BINUS University: BinusMaya, Neksus Semester Plan, Thesis App, and the Library App which play a central role in supporting learning activities, academic administration, and access to academic resources. The study applies the Customer Satisfaction Score (CSAT) framework, a widely used approach for evaluating user satisfaction with system quality, usability, and performance. The CSAT evaluation is adapted to the functional characteristics of each application. Thesis App, BinusMaya, and Neksus Semester Plan, which directly support core academic processes, are assessed using four dimensions: fulfilment, efficiency, system availability, and accuracy. Meanwhile, the Library App, which functions as a supporting academic resource platform, is evaluated using ease of use, features and functionality, and system performance. A quantitative descriptive approach was employed, with data collected through an online survey conducted between July and September 2025. A four-point Likert scale was used to encourage clear evaluative responses, and the sample size was determined using Slovin’s formula. The findings indicate that students generally report positive satisfaction across all applications. Thesis App performs strongly in terms of efficiency and accuracy, while the BinusMaya shows high satisfaction in fulfilment. Neksus Semester Plan receives favourable evaluations in efficiency but faces responsiveness challenges during peak usage periods. Library app is positively rated for its features, although improvements are needed in system performance and interface consistency. Overall, the results suggest that BINUS University’s academic applications effectively support student activities, while also highlighting the importance of continuous system improvement.
Machine Learning Approach: A Comparative Analysis of Classifiers in Predicting Obesity Type Tedjasulaksana, Jeffrey; Dinata, Ferry Jaya; Krisnadi, Rafael; Reksosamudro, Matthew S.W.; Wen, Wilbert; Hidayat, Muhammad Fadlan
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 1 (2026): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i1.15268

Abstract

Obesity is a growing global public health concern that increases the risk of chronic diseases and significantly affects quality of life. Traditional diagnostic methods such as Body Mass Index (BMI) have limitations in accurately representing body fat distribution and individual health conditions. This study aims to comparatively evaluate the performance of various machine learning and neural network models in predicting obesity levels using a multiclass classification approach. The dataset consists of 2,111 observations with 12 predictor variables and seven obesity categories, obtained from a publicly available source. Data preprocessing included duplicate removal, outlier handling using the interquartile range method, feature scaling, and categorical encoding, followed by a 60:20:20 train–validation–test split. Several classifiers were implemented, including Logistic Regression, Support Vector Classifier, Random Forest, Extra Trees, Gradient Boosting-based models (XGBoost and LightGBM), Multilayer Perceptron, K-Nearest Neighbors, and TabNet. Model performance was evaluated using macro-average F1-score and confusion matrix analysis. The results indicate that LightGBM achieved the highest predictive performance with an F1-score of 0.96, demonstrating strong generalization across obesity categories. XGBoost and Random Forest also showed strong performance, while Support Vector Classifier exhibited consistent results across training, validation, and cross-validation. These findings suggest that ensemble-based models are highly effective for obesity classification, while model selection should consider accuracy, interpretability, and computational constraints.

Page 18 of 18 | Total Record : 174

 14   15   16   17   18 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol. 8 No. 1 (2026): EMACS (In Press) Vol. 7 No. 3 (2025): EMACS Vol. 7 No. 2 (2025): EMACS Vol. 7 No. 1 (2025): EMACS Vol. 6 No. 3 (2024): EMACS Vol. 6 No. 2 (2024): EMACS Vol. 6 No. 1 (2024): EMACS Vol. 5 No. 3 (2023): EMACS Vol. 5 No. 2 (2023): EMACS Vol. 5 No. 1 (2023): EMACS Vol. 4 No. 3 (2022): EMACS Vol. 4 No. 2 (2022): EMACS Vol. 4 No. 1 (2022): EMACS Vol. 3 No. 3 (2021): EMACS Vol. 3 No. 2 (2021): EMACS Vol. 3 No. 1 (2021): EMACS Vol. 2 No. 3 (2020): EMACS Vol. 2 No. 2 (2020): EMACS Vol. 2 No. 1 (2020): EMACS Vol. 1 No. 1 (2019): EMACS More Issue