cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota adm. jakarta barat,
Dki jakarta
INDONESIA
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 6 Documents
 1 
Search results for , issue "Vol. 16 No. 2 (2025): ComTech" : 6 Documents clear
Clustering Analysis of MAMA 2024 Song of the Year Nominees Based on Musical Elements and Popularity Indicators Harahap, Libelda Aldinaduma; Sofro, A'yunin
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.v16i2.12860

Abstract

As K-pop continues to dominate global music charts, understanding the factors behind the success of songs has become increasingly essential. This study explores how musical elements and popularity indicators reveal patterns among topperforming songs. A total of 57 songs nominated for the 2024 Song of the Year category were grouped using hierarchical cluster analysis. The genre variable was consolidated into six broader categories and converted into numerical labels. All variables are normalized using the Min-Max normalization method before clustering. The data includes musical elements such as genre, tempo, danceability, energy, and happiness, as well as popularity indicators like YouTube views and Spotify streams. The analysis employs single, complete, and average linkage methods. Among these, the average linkage method yields the best results, with an agglomerative coefficient value of 0.8167. Seven distinct clusters are identified: Cluster 1 features R&B and hip-hop styles with varied energy and rhythms; Cluster 2, the largest group, includes high-energy pop, hip-hop, and dance-pop tracks that are popular on streaming platforms; Cluster 3 contains indie and experimental tracks; Cluster 4 emphasizes high-energy stage performances; Cluster 5 is an outlier with experimental traits; Cluster 6 highlights R&B and funk with global appeal; and Cluster 7 includes emotional OSTs and ballads with slower tempos. By combining musical elements and popularity indicators, this research uncovers patterns of success in K-pop songs. These findings offer actionable insights for artists, producers, and marketers, providing a datadriven reference for creating music that resonates with modern audience preferences.
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.

Page 1 of 1 | Total Record : 6

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 16 No. 2 (2025): ComTech Vol. 16 No. 1 (2025): ComTech Vol. 15 No. 2 (2024): ComTech Vol. 15 No. 1 (2024): ComTech Vol. 14 No. 2 (2023): ComTech Vol. 14 No. 1 (2023): ComTech Vol. 13 No. 2 (2022): ComTech Vol. 13 No. 1 (2022): ComTech Vol. 12 No. 2 (2021): ComTech Vol. 12 No. 1 (2021): ComTech Vol. 11 No. 2 (2020): ComTech Vol 11, No 1 (2020): ComTech (Inpress) Vol. 11 No. 1 (2020): ComTech Vol 10, No 2 (2019): ComTech Vol. 10 No. 2 (2019): ComTech Vol 10, No 1 (2019): ComTech (In Press) Vol. 10 No. 1 (2019): ComTech Vol 10, No 1 (2019): ComTech Vol. 9 No. 2 (2018): ComTech Vol 9, No 2 (2018): ComTech Vol 9, No 2 (2018): ComTech Vol 9, No 1 (2018): ComTech Vol. 9 No. 1 (2018): ComTech Vol 9, No 1 (2018): ComTech Vol 8, No 4 (2017): ComTech Vol 8, No 4 (2017): ComTech Vol. 8 No. 4 (2017): ComTech Vol 8, No 3 (2017): ComTech Vol. 8 No. 3 (2017): ComTech Vol 8, No 3 (2017): ComTech Vol. 8 No. 2 (2017): ComTech Vol 8, No 2 (2017): ComTech Vol 8, No 2 (2017): ComTech Vol 8, No 1 (2017): ComTech Vol. 8 No. 1 (2017): ComTech Vol 8, No 1 (2017): ComTech Vol 7, No 4 (2016): ComTech Vol 7, No 4 (2016): ComTech Vol. 7 No. 4 (2016): ComTech Vol 7, No 3 (2016): ComTech Vol 7, No 3 (2016): ComTech Vol. 7 No. 3 (2016): ComTech Vol 7, No 2 (2016): ComTech Vol 7, No 2 (2016): ComTech Vol. 7 No. 2 (2016): ComTech Vol 7, No 1 (2016): ComTech Vol 7, No 1 (2016): ComTech Vol. 7 No. 1 (2016): ComTech Vol 6, No 4 (2015): ComTech Vol 6, No 4 (2015): ComTech Vol. 6 No. 4 (2015): ComTech Vol. 6 No. 3 (2015): ComTech Vol 6, No 3 (2015): ComTech Vol 6, No 3 (2015): ComTech Vol. 6 No. 2 (2015): ComTech Vol 6, No 2 (2015): ComTech Vol 6, No 2 (2015): ComTech Vol. 6 No. 1 (2015): ComTech Vol 6, No 1 (2015): ComTech Vol 6, No 1 (2015): ComTech Vol. 5 No. 2 (2014): ComTech Vol 5, No 2 (2014): ComTech Vol 5, No 2 (2014): ComTech Vol. 5 No. 1 (2014): ComTech Vol 5, No 1 (2014): ComTech Vol 5, No 1 (2014): ComTech Vol. 4 No. 2 (2013): ComTech Vol 4, No 2 (2013): ComTech Vol 4, No 2 (2013): ComTech Vol 4, No 1 (2013): ComTech Vol 4, No 1 (2013): ComTech Vol. 4 No. 1 (2013): ComTech Vol 3, No 2 (2012): ComTech Vol 3, No 2 (2012): ComTech Vol. 3 No. 2 (2012): ComTech Vol 3, No 1 (2012): ComTech Vol 3, No 1 (2012): ComTech Vol. 3 No. 1 (2012): ComTech Vol. 2 No. 2 (2011): ComTech Vol 2, No 2 (2011): ComTech Vol 2, No 2 (2011): ComTech Vol. 2 No. 1 (2011): ComTech Vol 2, No 1 (2011): ComTech Vol 2, No 1 (2011): ComTech Vol. 1 No. 2 (2010): ComTech Vol 1, No 2 (2010): ComTech Vol 1, No 2 (2010): ComTech Vol 1, No 1 (2010): ComTech Vol 1, No 1 (2010): ComTech Vol. 1 No. 1 (2010): ComTech More Issue