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Sinergi
Published by Universitas Mercu Buana
ISSN : 14102331     EISSN : 24601217     DOI : https://dx.doi.org/10.22441/sinergi
Core Subject : Engineering,
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
SINERGI is a peer-reviewed international journal published three times a year in February, June, and October. The journal is published by Faculty of Engineering, Universitas Mercu Buana. Each publication contains articles comprising high quality theoretical and empirical original research papers, review papers, and literature reviews that are closely related to the fields of Engineering (Mechanical, Electrical, Industrial, Civil, and Architecture). The theme of the paper is focused on new industrial applications and energy development that synergize with global, green and sustainable technologies. The journal registered in the CrossRef system with Digital Object Identifier (DOI). The journal has been indexed by Google Scholar, DOAJ, BASE, and EBSCO.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 25 Documents
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Search results for , issue "Vol 29, No 2 (2025)" : 25 Documents clear
Real-time dental caries segmentation with an efficient Deformable U-Net (DU-Net) for teledentistry system Iklima, Zendi; Kadarina, Trie Maya; Salamah, Ketty Siti; Sentosa, Arrival Dwi
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.015

Abstract

Digital technology has greatly improved teledentistry by facilitating telediagnostics and teleconsultations, particularly benefiting those in remote areas. Additionally, AI advancements enhance diagnostic accuracy and streamline clinical decision-making, reducing costs and resource disparities in dental care. This study presents an improved U-Net architecture, Deformable U-Net (DU-Net), for semantic dental caries segmentation, leveraging deformable convolutions to dynamically adjust sampling points for improved feature extraction and reduced computational redundancy. By connecting encoder-decoder blocks via skip-connections, the DU-Net architecture enables efficient real-time segmentation and balance accuracy while reducing computational demands. The deformable block in DU-Net and DDR U-Net shows a balanced performance and efficiency while maintaining accuracy despite reduced FLOPs. The proposed architecture was implemented in real-time dental caries segmentation on a Dual Core Cortex A72 system and web server. It shows a significant improvement in Dice score, reducing CPU and memory usage compared to conventional U-Net models. Moreover, the DU-Net and its half variants achieved competitive performance with much lower computational demands makes suitable for web servers and embedded applications. The result highlights the DU-Net capability to optimize both computational efficiency and segmentation accuracy, offering a promising solution for real-world applications where speed and resource management are critical, particularly in the medical imaging field.
Cikakembang River Restoration from the Perspective of Numerical Modelling Kent, Steven; Yudianto, Doddi; Gao, Cheng; Fitriana, Finna; Wang, Qian
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.020

Abstract

The poor condition of the Citarum River demands more significant pollution control. One alternative for controlling pollution can be limiting the amount of wastewater entering one of the tributaries of the Citarum River, namely the Cikakembang River. This study is a follow-up study that will model heavy metal parameters in the Cikakembang River. Data collection was carried out six times, where the heavy metal parameter detected was copper. Numerical modelling for copper parameters was carried out using MATLAB software with the Runge Kutte-4 discretisation scheme. The study location covers 2.36 km upstream of the Cikakembang River, with 12 textile industry wastewater disposal points. Numerical modelling results for copper parameters show a settling rate of heavy metal particles of 40 day-1, with a maximum RRMSE value of 9.97%. Combining the water quality models for organic and heavy metal parameters created, pollution control simulations can be run in both seasons. The pollution control scenario aims to find the maximum amount that enters the Cikakembang River without passing the class four river water quality standards. The selection of the standard is based on the use of Cikakembang River water, namely for irrigation purposes. Based on the results of pollution control simulations, the pollutant carrying capacity for BOD, COD and copper parameters in the Cikakembang River is 199.43 kg/day, 1103.80 kg/day and 4.06 kg/day, respectively.
Optimizing PSO for classification: comparison of Naïve Bayes and C4.5 for osteoporosis prediction Anugerahwati, Zulfi; Lestari, Sri
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.011

Abstract

Osteoporosis is a medical disease marked by a reduction in bone density, which significantly increases the risk of fractures. Osteoporosis patients do not always exhibit symptoms and because current diagnostic techniques have limitations, early detection is frequently needed. The osteoporosis dataset consists of 1.958 records each containing 15 regular attributes and 1 special attribute as the label.  The attribute represented as “1” for the presence of osteoporosis and “0” for its absence. The primary objective is to predict an individual’s risk of developing osteoporosis, including age, gender, bone density, lifestyle factor, medical history, and nutritional intake of calcium and vitamin D. To achieve this, Naïve Bayes and C4.5 has been employed. PSO is employed to identify the most relevant features, thereby optimizing the efficiency and accuracy of the classification models. The initial step in data preprocessing involved handling missing values to ensure data integrity. After implementing PSO, Naïve bayes improved from 82,65% to 83,67%, while C4.5 exhibited an even greater increase, rising from 91,07% to 96,17%. PSO significantly optimizes model, with the most improvement in C4.5. PSO proves to be a valuable tool for feature selection. Age and Hormonal Change emerged as important for both models. Furthermore, Physical Activity and Calcium Intake, which despite having varying levels of influence, were consistently considered relevant.  By focusing on these significant attributes, enables us more effectively monitor and recognize early signs of osteoporosis. Identifying individuals at high risk, more effective early detection and intervention, improving the potential for timely management and prevention.
Instrumented model slope to investigate the influence of rainfall and slope gradient on matric suction Jelani, Jestin; Ahmad Ishak, Aina Syahirah; Ahmad, Nordila; Suif, Zuliziana; Wan Suhaili, Wan Mohamad Adham Hanis; Ahmad Mazuki, Ahmad Loqman; Supian, Latifah Sarah
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.025

Abstract

Prior researchers indicated that prolonged and heavy rainfalls primarily trigger major landslides in Malaysia. This study was carried out to investigate the influence of rainfall on the matric suction of silty sand slopes through a small-scale model. A 35° and 45° slope (namely EXP1 and EXP2) models were built using soil samples from the former landslide site at Kemensah Heights, Selangor, Malaysia. Two types of sensors were used to measure matric suction and rainfall intensities using Watermarks 200SS Soil Moisture Sensor and Hydreon rain gauge RG-15, respectively. The elapsed time since the beginning of the rainfall was recorded using two cameras placed at the front and side of the slope model to observe progressive failure. The results showed that the initial matric suction with a value of 250 kPa is significantly reduced and approached 0 kPa when the range of cumulative rainfall intensity is between 30 and 36.75 mm/min and 5.25 and 6.75 mm/min recorded by PP1 and PP2 in EXP1 and EXP2, respectively. The results indicate that the reduction in matric suction induced by rainwater infiltration is the triggering mechanism of slope failure. It has also been noticed that rainfall infiltration increases with decreasing slope gradients. However, a small gradient slope requires longer rainfall prior to failure. A slope with a high gradient has a longer time before failure occurs after loss of matric suction than a low slope gradient.
Design of path planning robot simulator by applying sampling based method Suwoyo, Heru; Andika, Julpri; Adriansyah, Andi
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.016

Abstract

This research aims to create a simulator for solving the global path planning of mobile robots. Various sampling-based methods such as Rapidly-exploring Random Tree (RRT), RRT*, and Fast-RRT, along with other derivative algorithms, have been widely used to solve path-planning problems in mobile robots. The level of computational efficiency, path optimality, and the ability to adapt to variant environments are some of the issues that still arise, although these techniques have shown good results in many cases. Although the existing solutions are innovative, comparison between the existing methods is still difficult due to significant differences in convergence speed, implementation complexity, and quality of the resulting paths. This makes choosing the most suitable method for a particular application difficult. The simulator uses sampling-based path planning algorithms such as RRT*, Fast RRT*, RRT*-Smart, informed-RRT*, and Honey Bee Mating Optimization-based Fast-RRT*. With this simulator, users can easily compare the performance of each algorithm and see the characteristics and efficiency of each algorithm in various situations. By running all methods through this simulator, the user can easily compare the methods based on convergence speed and optimality. Therefore, it will effectively help users understand robot navigation, improve the quality of learning, and promote the development of path-planning technology for mobile robots.

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