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ComTech: Computer, Mathematics and Engineering Applications
Published by Universitas Bina Nusantara
ISSN : 20871244     EISSN : 2476907X     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Engineering Mathematics
The journal invites professionals in the world of education, research, and entrepreneurship to participate in disseminating 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 : -
Articles 1,591 Documents
 156   157   158   159   160 
Numerical Simulation Study using the Explicit Finite Difference Method for Petroleum Reservoir Maulindani, Sri Feni; Prima, Andry; Wibowo, Jati Arie; Rusdi, Pauhesti; Widiyatni, Harin
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 2 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i2.12191

Abstract

The behavior of petroleum reservoirs is inherently complex, making it challenging to determine their performance for both single-fluid and multiphase production systems. To accurately estimate the recovery reserves of a reservoir, a comprehensive understanding of its geometry and internal flow characteristics is essential. Numerical simulation serves as a fundamental tool for reservoir engineers, offering an efficient and reliable method to predict reservoir mechanisms, evaluate pressure variations, and estimate in-place hydrocarbon yield. This study employs mathematical modeling concepts and numerical techniques to analyze the dynamic behavior of petroleum reservoir systems. A flow model based on Partial Differential Equations (PDEs), specifically the diffusivity equation for unsteady-state fluid flow in porous media, is developed and applied. The diffusivity equation is discretized and solved mathematically using the explicit finite difference method to approximate pressure distribution over time and space. The primary objective of this research is to investigate and analyze the pressure distribution that governs reservoir performance under varying conditions. Sensitivity analyses are conducted to evaluate the influence of grid spacing, time step, hydraulic diffusivity, and boundary conditions on pressure reservoir behavior within a Cartesian grid for a one-dimensional, single-phase reservoir. The findings are expected to provide insight into the relationship between reservoir properties and fluid dynamics, supporting improved prediction of reservoir behavior. Ultimately, this research contributes to the optimization of petroleum production strategies and enhances the understanding of reservoir engineering processes through quantitative simulation.
Inception-ResNet-V2 The U-Net Encoder for Road Segmentation using Sentinel 2A Yanuargi, Bayu; utami, ema
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 2 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i2.12089

Abstract

Updating road network maps is essential for transportation services, as incomplete or inaccurate maps can lead to inefficiencies and diminish service quality. The online transportation industry generates vast amounts of GPS data as drivers navigate, which is valuable for mapping road networks and improving traffic management. However, since drivers do not cover all roads, satellite imagery plays a crucial role in identifying areas that are not mapped. By combining GPS data as labels with satellite imagery, the extraction of new road networks becomes more accurate. This research employs a deep learning Convolutional Neural Network (CNN) with the U-Net architecture for road segmentation, allowing for the identification of new paths. Two different encoders are tested in this research: Inception-ResNet-V2 and a pure U-Net encoder. The Inception-ResNet-V2 encoder achieves an accuracy of 91.3%, while the pure U-Net encoder achieves 90.7%. In terms of Dice Loss, the models record values of 0.051 and 0.08, respectively. The research highlights the effectiveness of different U-Net encoders in road network segmentation. With high accuracy and low Dice Loss, this approach provides a reliable method for automatically updating road maps. It has potential applications in navigation systems, urban planning, and AI-driven intelligent transportation systems.
Temperature Forecast at Djuanda International Airport using ARIMA, ANN, and Hybrid ARIMA-ANN Elly Pusporani; Fitriana Nur Afifa; Fidela Sahda Ilona Ramadhina
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 2 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i2.13219

Abstract

This research evaluates the performance of Artificial Neural Network (ANN) models in forecasting temperature at Djuanda Airport, comparing them with the traditional Autoregressive Integrated Moving Average (ARIMA) model and a hybrid ARIMA–ANN approach. Although statistical models such as ARIMA are widely applied, their capacity to capture nonlinear dynamics in tropical climate conditions is limited, particularly when the data exhibit irregular fluctuations that linear models cannot adequately represent. Forecasting temperatures in tropical airport settings, which is crucial for flight planning, operational safety, and the reliability of aviation operations, remains relatively underexplored. This gap underscores the importance of alternative modeling techniques that can effectively address nonlinear relationships. Using one year of observed data, the models are evaluated with three accuracy metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The ANN model achieves the lowest error values (MAE 0.7630, MAPE 2.7067%, RMSE 1.0074) compared to both ARIMA and hybrid approaches. The metrics and the testing graph collectively indicate that ANN has a stronger ability to capture nonlinear temperature dynamics in tropical contexts. Nonetheless, the findings must be interpreted with caution due to the limited dataset and single case study. These limitations highlight the need for extended data and alternative architectures to improve forecasting accuracy and strengthen support for safer aviation operations.
CNN-GRU for Drowsiness Detection from Electrocardiogram Signal Hendratno, Setiawan; Surantha, Nico
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 2 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i2.12755

Abstract

Drowsiness is a problem that needs to be addressed to improve road safety. To minimize this safety issue, driving-monitoring systems have been implemented in current car models, and electrocardiography (ECG) is one of the most commonly used driving monitoring techniques. ECG data are modeled using a deep neural network, including a Bidirectional Gated Recurrent Unit (Bi-GRU). However, the accuracy for classifying Wake-Sleep is under 80% and Wake-NREM-REM reaches less than 68%. To address this issue, ECG data from the MESA and SHHS datasets are modeled using a combination of a Convolutional Neural Network (CNN) and a Bi-GRU, referred to as CNN-GRU. This model incorporated Batch Normalization and RMSProp to achieve improved accuracy in classifying drivers' conditions. It operates in two computing sectors: cloud computing (Google Colaboratory, also known as Colab) and edge computing (utilizing an AMD Ryzen 5 4600H processor laptop). Those computing sectors focused on a case where no internet connectivity occurred to process the classification. Those classifications achieved accuracy rates of 82.88% and 81.78% for Wake-Sleep classification in cloud- and edge-computing, respectively. Additionally, it achieved 71.01% (Colab) and 68.85% (edge-computing) accuracy in Wake-NREM-REM classification. This result indicates that CNN-GRU achieved better performance, surpassing the previous Bi-GRU model, which only achieved 80.42% (Colab) and 76.2% (edge-computing) for Wake-Sleep, and 68.85% (Colab) and 66.43% for Wake-NREM-REM.
Turning DIN 19682-7 Procedure of Infiltration Rate of Soils Test into the Mobile App for Cloud Storage Sulistyo, Totok; Kiptiah, Mariatul; Kusumayudha, Sari Bahagiarti; Cahyadi, Tedy Agung; Fajar, Reza Adhi
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 2 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i2.13000

Abstract

The in-situ soil infiltration test using A Double Ring Infiltrometer (DRI) apparatus can be conducted in the field according to DIN 19682-7 standards and procedures. As required by these standards, the traditional paper-based measurement form can be replaced with a new application developed to meet standard requirements. The DRI apparatus consists of two concentric rings placed in the soil, filled with water, while the outer ring maintains a constant water level. The water level drop in the inner ring is observed and recorded at regular intervals. The infiltration rate can be calculated for each interval by measuring the change in water height over time. This new application facilitates the automatic calculation of both the actual soil infiltration rate and the Horton soil infiltration model. Comparison tests between the application results and Excel calculations have yielded similar outcomes. The goal of this research is to develop a mobile web-based application for recording data and calculating soil infiltration measurements using the DRI method. The research methodology involves transforming the measurement procedure into a concept, designing the application, and then implementing that design. By replacing the paper-based process, this application will enhance the efficiency, accuracy, and flexibility of soil infiltration measurement projects in various locations. Furthermore, the data will be stored in the cloud, allowing for crowdsourced infiltration data collection and monitoring from any location, including the office.
Investigating Prospective Athletic Athletes: Classifiers, Benchmarking, and Post-Hoc XAI Analysis Ibnu Febry Kurniawan; A'yunin Sofro; Danang Ariyanto; Junaidi Budi Prihanto; Dimas Avian Maulana
ComTech: Computer, Mathematics and Engineering Applications Vol. 17 No. 1 (2026): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v17i1.13224

Abstract

Identifying highly potential athletes is a critical yet inherently challenging process that requires comprehensive analysis of diverse factors, including physiological attributes, demographic characteristics, and social influences. This multifaceted process requires meticulous evaluation of extensive datasets to ensure both accuracy and fairness in talent identification protocols. The complexity stems from the interconnected nature of the determinants of athletic performance, where physical capabilities intersect with psychological resilience, social support systems, and environmental factors. In recent years, machine learning (ML) algorithms gain prominence in decision-making processes, offering unprecedented opportunities to uncover subtle patterns and relationships within athlete data that might otherwise remain hidden. This study systematically benchmarks the performance of several state-of-the-art ML classifiers using a novel, self-collected dataset of athlete candidates. Furthermore, an explainable AI (XAI) technique, Shapley Additive Explanations (SHAP), is applied to interpret model decisions and provide meaningful insights into key predictive factors. Experimental results demonstrate that Gradient Boosting achieves superior predictive performance (F1) across the 10-fold sets, with a mean value of 0.46. SHAP analysis reveals the critical importance of anthropometric measurements and social group features in influencing prediction outcomes. These findings collectively underscore the substantial potential of ML to revolutionize talent identification in sports while emphasizing the importance of model interpretability in fostering trust and acceptance of AIdriven decision-making processes.
Explainable Machine Learning Models SHAP-based for Feature Importance Affecting Stunting Prevalence Asysta Amalia Pasaribu; Nur Fitriyani Sahamony; Khairil Anwar Notodiputro; Bagus Sartono
ComTech: Computer, Mathematics and Engineering Applications Vol. 17 No. 1 (2026): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v17i1.13732

Abstract

Stunting is a form of chronic nutritional deficiency in toddlers and remains a major public health concern due to its impact on child growth and development. Efforts to reduce its prevalence continue to be strengthened in Indonesia, particularly in Sumatra Province. This study aims to evaluate the accuracy of a logistic regression model and three machine learning models—decision tree, random forest, and Support Vector Machine (SVM)—in classifying stunting prevalence. The response variable is the prevalence of stunting among toddlers and is categorized into two classes: exceeding the national target and not exceeding it, based on the 2024 national threshold. Although classification models can provide accurate predictions, they often lack interpretability. Therefore, this study applies the Shapley Additive exPlanations (SHAP) method to the best-performing machine learning model to identify the key factors influencing stunting. The use of Shapley values is justified through the uniqueness theorem, which establishes it as the only attribution method that satisfies desirable fairness properties. SHAP values explain the model by referencing both the trained model and the underlying data. The results show that the random forest model achieves the highest accuracy (90.00%) and outperforms the other models. SHAP analysis reveals that Underweight is the most influential predictor contributing to stunting prevalence in Sumatra Province. These findings highlight the importance of machine learning interpretability in supporting policy decisions to reduce stunting.
Balinese Language Classification on Social Media using Multinomial Naive Bayes Method with TF-IDF Putu Widyantara Artanta Wibawa; Cokorda Pramartha; I Gusti Ngurah Anom Cahyadi Putra; Luh Gede Astuti
ComTech: Computer, Mathematics and Engineering Applications Vol. 17 No. 1 (2026): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v17i1.14132

Abstract

Balinese is a local language that is widely used and spoken by Balinese people, including on social media platforms. However, the nuances of its politeness levels are often lost in informal digital communication, and there is a significant lack of computational models that automatically classify these levels, particularly for low-resource languages such as Balinese. The primary objective of this study is to evaluate the performance of the Multinomial Naive Bayes method combined with Term Frequency–Inverse Document Frequency (TFIDF) feature extraction, Chi-square feature selection, and the Synthetic Minority Oversampling Technique (SMOTE) in classifying Balinese language levels. The dataset used in this study consists of 1,314 annotated social media posts and comments, primarily sourced from Instagram. A Balinese language expert performs the annotation, categorizing the texts into six levels that represent varying degrees of politeness and formality. These levels include alus singgih (polite, used for respecting others), alus sor (polite, used for self-humbling), alus mider (polite, used for both respecting others and self-humbling), alus madia (an intermediate level of politeness), basa andap (casual, commonly used in everyday life), and basa kasar (impolite, often used during arguments or toward animals). The experimental results show that the model achieves 96.53% accuracy on the training data and 61.45% accuracy on the test data. In addition, hyperparameter tuning reveals that the Multinomial Naive Bayes model with 2,720 selected features and SMOTE oversampling achieves 91.78% accuracy, significantly outperforming the baseline model without feature selection or oversampling, which achieves only 64.93% accuracy.
Forecasting Food Prices in East Java Using Stacking Ensemble Learning via K-MEANS Aviolla Terza Damaliana; Amri Muhaimin; Nabilah Selayanti; Shafira Amanda Putri; Muhammad Nasrudin
ComTech: Computer, Mathematics and Engineering Applications Vol. 17 No. 1 (2026): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v17i1.14218

Abstract

Food commodities are essential in developing countries such as Indonesia, and the government regulates food commodity prices in every province. However, price instability issues persist in certain provinces, creating challenges for effective policy control. Data science and statistical techniques play an important role in supporting the government’s efforts to monitor and manage food commodity prices. This study proposes the Stackelberg-K-Means method to predict the commodity price index in East Java. The proposed method is a collaborative framework that combines cluster analysis and stacking ensemble learning for time-series prediction. Cluster analysis is conducted first using Dynamic Time Warping as the distance measure, which is suitable for time-series data, resulting in two clusters for each commodity: rice, oil, and flour. The stacking model consists of base learners and a meta-learner. The base learner models include Ridge Regression, Random Forest, and Support Vector Regression, while the meta-learner uses Light Gradient Boosting. Parameter optimization is performed using grid search, and the proposed method is evaluated against AutoARIMA implemented in Python using both training and testing data. The results show that the proposed method outperforms the ARIMA model across all three error metrics: MAPE, MAE, and RMSE. For flour commodities, the scores are 0.042% versus 0.328%, 4.715 versus 37.57, and 6.34 versus 523.99, respectively. For rice commodities, the scores are 0.261% compared to 0.392%, 31.585 compared to 48.142, and 41.92 compared to 56.068. For oil commodities, the scores are 0.185% compared to 0.250%, 33.02 compared to 47.571, and 39.35 compared to 56.060.
Evaluating Application Integration Success Using DeLone McLean and CSF Model Al Aziizu Putra Hendriana; Tanty Oktavia
ComTech: Computer, Mathematics and Engineering Applications Vol. 17 No. 1 (2026): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v17i1.14513

Abstract

Digital transformation in the financial industry encourages organizations to adopt application integration systems to enhance operational efficiency and effectiveness. However, many information system implementation projects fail to meet expectations, particularly in guarantee institutions characterized by complex business processes. This study evaluates the success of an application integration system project in a guarantee company by applying an integrated framework that combines the DeLone and McLean (D&M) (2003) Information System Success Model with Critical Success Factors (CSF). By explicitly positioning CSF variables as antecedents of system quality, information quality, and service quality, this study extends the conventional use of the D&M model by incorporating managerial and organizational perspectives into the assessment of integration success. A quantitative approach is employed using survey data collected from 120 users of the Penjaminan Application Integration (PAI) system at PT XYZ, which are analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that CSF variables—namely top management support, internal communication, user training, and project risk management—significantly influence system quality, information quality, and service quality. Furthermore, these three quality dimensions have a significant effect on intention to use and user satisfaction, which in turn impact perceived net benefits for the organization. In conclusion, integrating managerial and system quality perspectives provides a more comprehensive understanding of application integration project success. These findings offer practical insights for improving IT project implementation strategies in the guarantee sector and in other industries with similar organizational and operational characteristics.

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