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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 14 Documents
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Search results for , issue "Vol. 7 No. 3 (2025): EMACS" : 14 Documents clear
Enhancing Tourism Demand Forecasting Accuracy Through Clustering Time Series: A Comparison MAPE Analysis of Indonesian Provincial Domestic Tourist Flows Purnama, Mohammad Dian
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.v7i3.14112

Abstract

The post-pandemic recovery period of the Indonesian tourism sector poses new challenges for accurate tourism demand forecasting across Indonesia's diverse provincial richness. This research aims to enhance the predictive accuracy of domestic tourism demand by comparing conventional single-provincial forecasting methods with clustering-based time series techniques. The Geometric Brownian Motion (GBM) model analyzed data regarding the monthly influx of domestic tourists to 34 provinces from January 2021 to May 2025. This study utilized average linkage agglomerative nesting (AGNES) clustering to discern structural similarities among provinces. Subsequently, silhouette analysis was employed to determine the optimal number of clusters. The findings demonstrate that the cluster-based forecasting approach markedly improved accuracy relative to the non-clustered model. The Mean Absolute Percentage Error (MAPE) for the traditional provincial forecasts was 16.48%. The first cluster-based model had an MAPE of 13.38% and the second cluster-based model had an MAPE of 6.54%. These findings indicate that grouping provinces with analogous temporal patterns enhances the model's ability to identify the underlying dynamics in domestic tourism flows. The work underscores the efficacy of combining stochastic models with hierarchical clustering to enhance evidence-based tourist planning and policy development. This study improves sustainable tourism management by providing an empirical foundation for enhanced forecasting precision, particularly in post-crisis recovery periods.
Comparative Study of CNN-based Deep Learning Models for Animal, Digit, and Flower Image Classification Suri, Puti Andam; Setiono, Michael Alvin; Andrew, Andrew; Fajar, Muhammad
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.v7i3.14317

Abstract

This study explores how four convolutional neural network (CNN) models MobileNetV2, DenseNet121, EfficientNetB0, and InceptionV3 perform in classifying images from three different datasets: animals, handwritten digits (MNIST), and flowers. The main goal is to understand which model offers the best balance between accuracy and efficiency when applied to datasets with varying complexity. Each model was trained and tested using identical preprocessing steps, and its performance was evaluated based on accuracy, precision, recall, and F1-score through a confusion matrix. Training and testing times were also measured to assess computational efficiency. The results show that DenseNet121 consistently achieved the highest accuracy: 98% on animal images and 88% on flower images, while MobileNetV2 provided a close performance (97% and 82%) but with much faster processing times, between 11 and 55 minutes. EfficientNetB0, on the other hand, performed poorly on the more complex flower dataset, achieving only 5% accuracy. These findings suggest that DenseNet121 is ideal for projects where accuracy is the main concern, whereas MobileNetV2 is more suitable for real-time applications that require quick responses without a major drop in accuracy. Overall, this research highlights the importance of aligning model selection with both dataset characteristics and computational limitations in practical image classification tasks.
Optimizing Enterprise Risk Management for Decision Making Using Knowledge Graph Albone, Aan
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.v7i3.14325

Abstract

The challenge in current enterprise risk management is that hundreds of risks are eventually recorded without knowing how hazards relate to one another or cascade. The distinction between peripheral and critical hazards is unknown to decision-makers. Organizations can depict the interconnectedness of risk in a structured, adaptable, and understandable way by showing these components as nodes and their interactions as edges. This knowledge graph makes it possible to store and query risk data in ways that are not entirely supported by conventional relational models. This method's ability to execute graph queries that uncover links and patterns that would otherwise be obscured in siloed datasets is one of its main advantages. Such inquiries can reveal how a single threat can lead to many vulnerabilities across multiple assets, or how flaws in shared systems can directly and indirectly raise exposure to interconnected hazards. These revelations draw attention to structural flaws that linear or isolated investigations frequently ignore. Organizations can improve situational awareness and long-term risk governance by using such a knowledge graph to find hidden trends, pinpoint important risk spots, and more efficiently prioritize mitigation efforts. The knowledge graph also helps to optimize enterprise risk management goals like resource allocation, control prioritization, and prompt reaction planning. Enterprise risk management can effectively represent the intricate relationships between risks, vulnerabilities, threats, and assets by incorporating a knowledge graph. Businesses can concentrate mitigation efforts where they will have the biggest impact by determining which nodes and edges are the most important and highest impact. This focused strategy increases overall resilience and decreases inefficiencies.
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.

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