cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Andi Adriansyah
Contact Email
andi@mercubuana.ac.id
Phone
+628111884220
Journal Mail Official
sinergi@mercubuana.ac.id
Editorial Address
Fakultas Teknik Universitas Mercu Buana Jl. Raya Meruya Selatan, Kembangan, Jakarta 11650 Tlp./Fax: +62215871335
Location
Kota adm. jakarta barat,
Dki jakarta
INDONESIA
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 531 Documents
 44   45   46   47   48 
Support Vector Machine (SVM) based Detection for Volumetric Bandwidth Distributed Denial of Service (DVB-DDOS) attack within gigabit Passive Optical Network Bibi, Sumayya; Zulkifli, Nadiatulhuda; Safdar, Ghazanfar Ali; Iqbal, Sajid
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.017

Abstract

The dynamic bandwidth allocation (DBA) algorithm is highly impactful in improving the network performance of gigabit passive optical networks (GPON). Network security is an important component of today’s networks to combat security attacks, including GPON. However, the literature contains reports highlighting its vulnerability to specific attacks, thereby raising concerns. In this work, we argue that the impact of a volumetric bandwidth distributed denial of service (DVB-DDOS) attack can be mitigated by improving the dynamic bandwidth assignment (DBA) scheme, which is used in PON to manage the US bandwidth at the optical line terminal (OLT). Thus, this study uses a support vector machine (SVM), a machine learning approach, to learn the optical network unit (ONU) traffic demand patterns and presents a hybrid security-aware DBA (HSA-DBA) scheme that is capable of distinguishing malicious ONUs from normal ONUs. In this article, we consider the deployment of the HSA-DBA scheme in OMNET++ to acquire the monitoring data samples used to train the ML technique for the effective classification of ONUs. The simulation findings revealed a mean upstream delay improvement of up to 63% due to the security feature offered by the mechanism. Besides, significant reductions for the upstream delay performance recorded at 63% TCONT2, 65% TCONT3, and 95% TCONT4 and for frame loss rate reduction for normal ONU traffic, respectively, were observed in comparison to the non-secure DBA mechanism. This research provides a significant stride towards secure GPONs, ensuring reliable defense mechanisms are in place, which paves the way for more resilient future broadband network infrastructures.
Broadband HMSIW antenna using a demi hexagonal ring slot for X-band application Astuti, Dian Widi; Muslim, Muslim; Umaisaroh, Umaisaroh; A Majid, Huda; Alam, Syah
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.008

Abstract

Microstrip antennas offer several advantages, including small size, easy fabrication, controllable polarity and radiation patterns, and easy integration with other components. These qualities make microstrip antennas more reliable than other antenna types. However, they also have limitations, such as lower radiation efficiency and narrow bandwidth, primarily due to the thin substrate thickness. Substrate integrated waveguide (SIW) is a type of microstrip antenna. SIW antennas come in two forms: one with a rectangular shape, typically designed as a slot, and the other in the form of a horn. However, SIW slot antennas face challenges with narrow impedance bandwidth due to the thin substrate, unlike conventional bulky hollow waveguides. The half-mode substrate integrated waveguide (HMSIW) slot antenna, which is a 50% miniaturized version of the SIW slot antenna, also suffers from reduced fractional bandwidth, resulting from the miniaturization and the thin substrate. This paper focuses on enhancing the bandwidth of HMSIW antennas by incorporating a demi-hexagonal ring slot. The broadband impedance bandwidth simulation (27.36%) is achieved through triple resonance frequencies to address the issue of narrow impedance bandwidth. Both the simulation results and measurements show consistency, with the measured impedance bandwidth ranging from 8.91 to 12.62 GHz (34.46%), demonstrating at least triple resonance frequencies.
Indonesia rupiah currency detection for visually impaired people using transfer learning VGG-19 Alfatikarani, Raissa; Suciningtyas, Laras; Bimasakti, Genta Garuda; Mardhatillah, Faqisna Putra; Paragas, Jessie R.; Tjahyaningtijas, Hapsari Peni Agustin
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.022

Abstract

People with visual impairments often face difficulties in determining the authenticity of paper money, which is a crucial skill to avoid fraud. The limitations of traditional methods, like blind codes for visually impaired people, require a more advanced and efficient solution. Previous methods of currency detection using Convolutional Neural Network (CNN) techniques, including the VGG-19 architecture, have often encountered challenges, particularly the long training times required. Therefore, we propose using transfer learning techniques and modifying the top layers of the VGG-19 model, known as fully connected layers, within a mobile application with audio feedback built using Android Studio. These modifications involve substituting the three fully connected layers with dense and flattened layers. We also implemented hyperparameter tuning, including adjusting the batch sizes and setting the number of epochs. The datasets used Indonesian Rupiah paper currency from the 2022 emission year, specifically Rp 50,000 and Rp 100,000 denominations. The best transfer learning VGG-19 model achieved a batch size of 32 and an epoch of 50, resulting in a high accuracy of 88%. Response speed testing with performance profiling on Android Studio showed an overall average response time of 458 ms. The main advantage of using transfer learning with the VGG-19 model is that it significantly reduces training time while still achieving high accuracy, differentiating this work from previous studies that relied on training from scratch, which is more time-consuming and resource-intensive. Therefore, this mobile app can be categorized as having a fast response time.
Inventory optimization model using Artificial Neural Network method and Continuous Review (s,Q) Setyaningrum, Hanny; Sriwana, Iphov Kumala; Mufidah, Ilma
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.013

Abstract

The medical device industry company experienced the problem of prolonged accumulation of finished goods in the warehouse, causing one of the safety box items to be defective and damaged. Therefore, this study aims to plan demand forecasting and design inventory policies that consider repair items caused during the buildup of finished goods in the warehouse to minimize total inventory costs using ANN and Continuous Review (s,Q) methods. Demand forecasting is carried out for the next 20 months, from May 2023 to December 2024, using the ANN model with a total forecasting of 17936 units of inner items and 3370 units of outer items. After that, the inventory policy calculation uses the continuous review (s,Q) method. The calculation results show a decrease in the total inventory cost on inner items by 83% and outer items by 79%. After demand forecasting, there was also a decrease in the total initial inventory cost of inner items by 81% and outer items by 80%. This research develops an inventory optimization model that considers repair items due to the accumulation of goods in the warehouse by integrating holding cost, ordering cost, and repair cost variables to develop inventory policies to be more effective and efficient and to utilize damaged products for repair and resale. The limitation of this research is that it only gets demand forecasting results for the next 20 months because the company only started operating in September 2021 and limited data access. It is hoped that future researchers can plan and design an inventory policy strategy with demand forecasting for the next 10 years, focusing on repair items caused by the accumulation of finished goods in the warehouse.
Development of face image recognition algorithm using CNN in airport security checkpoints for terrorist early detection Anggraini, Eca Indah; Nurdin, Fachdy; Restianto, Mohammad Obie; Dahsan, Sudarti; Ardhana, Andini Aprilia; Supriyadi, Asep Adang; Darmawan, Yahya; Arief, Syachrul; Ikhsanudin, Agus Haryanto
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.004

Abstract

Ensuring airport security is of paramount importance to safeguard the lives of passengers and prevent acts of terrorism. In this context, developing advanced technology for early terrorist detection is crucial. This paper presents a novel approach to enhancing security measures at airport checkpoints by applying Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) algorithms in face image recognition. Our system utilizes state-of-the-art artificial intelligence techniques to analyze facial features. Our research uses VGG architecture and pre-trained with face data as a CNN model. This model is used to extract face embedding features from the dataset. These embedding features are then compressed with Principal Component Analysis (PCA) to obtain the meaningful feature as training data for the ANN algorithm. We trained our system using data from 500 identities data with 60 data for each identity.  This training enables our system to recognize known terrorists and individuals on watchlists by comparing the facial features of individuals passing through security checkpoints with those in the database. The proposed CNN-ANN-based face recognition system not only enhances airport security but also significantly reduces the processing time for security checks. It can quickly identify potential threats, allowing security personnel to take appropriate actions in real time ensuring a rapid response to security concerns. We present the architecture, training methodology, and evaluation of the CNN-ANN model, achieving a high accuracy of 91.16% and precision of 91.36%. Through this research, we aim to increase airport security and strengthen efforts to combat terrorism, making air travel safer and more secure for all passengers. 
Performance evaluation of hyper-parameter tuning automation in YOLOV8 and YOLO-NAS for corn leaf disease detection Saputra, Huzair; Muchtar, Kahlil; Chitraningrum, Nidya; Andria, Agus; Febriana, Alifya
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.018

Abstract

Corn cultivation was crucial in Southeast Asia, significantly contributing to regional food security and economies. However, leaf diseases posed a significant threat, causing substantial losses in production and quality. This research utilized artificial intelligence (AI) technology to address this issue by automating the hyper-parameter tuning process in YOLO (You Only Look Once) object detection models for early corn leaf disease detection. High-resolution images of corn leaves were captured and preprocessed for consistency. The preprocessing stage involved creating new dataset folders for images and labels, resizing images while preserving their aspect ratio, and rotating them if necessary. The images, containing 11,596 labeled instances, were analyzed using YOLOv8 and YOLO-NAS models. Each image's detected disease regions were converted into YOLO-format text files with x, y, width, and height coordinates, describing the presence and severity of infections. The models' performances were evaluated using precision, recall, mAP50, and mAP50-95 metrics. YOLOv8m achieved a mAP50 of 98.5% and mAP50-95 of 67.8%, while YOLO-NAS-L demonstrated superior detection capabilities with a mAP50 of 70.3% and mAP50-95 of 38.9%. This automated system facilitated early disease identification and enabled prompt preventive measures, thereby enhancing crop yields and mitigating losses. The findings highlighted the potential of advanced AI-driven detection systems in revolutionizing crop management and supporting global food security. 
Five questions on the multisensory perception in historic urban areas Septianto, Eggi; Firmansyah, Firmansyah; Poerbo, Heru Wibowo; Martokusumo, Widjaja
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.009

Abstract

Multisensory perception is essential for individuals or communities to fully appreciate urban historic areas. Recognizing non-physical aspects such as sound, smell, and tactile sensations based on human senses is crucial for enhancing the relationship between individuals and their environment. Discussions on heritage conservation extend beyond physical aspects evaluated visually to encompass non-physical elements. This article presents five questions covering the definition, influencing factors, types and categories, and methods for multisensory perception research. Furthermore, this article aims to explain gaps in multisensory research in urban historic areas to determine what and how future heritage conservation studies should be conducted. The research employs content analysis methods to analyze discussions from selected literature regarding the benefits of multisensory research in urban historic areas. Multisensory perception significantly influences the diverse sensory experience of intangible aspects in an environment, enriching urban heritage conservation approaches.
Forecast of sugar demand in retail using SARIMA and decomposition models case study: a retail store in Indonesia Sari, Titi; Sakti, Sekar
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.006

Abstract

This study discusses forecasting demand in a retail store, focusing on sugar, which is a staple food in Indonesia, as the research object. Despite its importance and forecast challenge, there is no research has been done on sugar at the retail level. This study aims to find the most suitable forecast model that can capture data patterns well to give a good prediction of sugar sales in a retail store in Indonesia by comparing SARIMA and decomposition models. This study uses a stationary test and ACF pattern analyses to prepare the data, a residual test to avoid forecast bias, cross-validation to check the forecast model performance, and MAPE as the performance indicator. SARIMA (0,0,0)(0,1,1)8 and multiplicative decomposition with 3 periods of double-moving average models are chosen. Both models have similar patterns but different slopes because the decomposition model is more sensitive to data patterns, resulting in different MAPEs, which are 15.22% and 13.64%.  Despite the popularity of SARIMA, decomposition can be an interesting alternative to use since it can capture trend data patterns better. However, the short forecast period is preferable for the decomposition model to avoid high trend slope prediction in the long run, leading to more frequent forecast activity and higher resources compared to SARIMA.
Characterization of Eichhornia crassipes bio-adsorbent activated by H3PO4 for the removal of lead ion (Pb2+) from wastewater of battery industry Erlangga, Reza; Sari, Dessy Agustina; Wahyuningtyas, Aulia
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.002

Abstract

Lead ion (Pb2+) contamination from battery industry wastewater affects significant environmental and health risks. This study explored the use of H3PO4-activated water hyacinth (WH) bio-adsorbent as an effective solution for removing Pb2+. The WH bio-adsorbent was prepared by activating dried water hyacinth stems with 1.2 M H3PO4, enhancing adsorption properties. SEM-EDX analysis revealed significant morphological changes, with increased porosity and oxygen-containing functional groups (O-H, C-O-P), which improved adsorption capacity. Adsorption kinetics followed a pseudo-second-order model (R2 = 0.99981), indicating that chemisorption dominated the Pb2+ removal process. Adsorption isotherms firmly fit the Langmuir model (R2 = 0.96), confirming monolayer adsorption on a homogeneous surface. The effect of pH was also investigated, with maximum adsorption efficiency (96.928%) observed at pH 7. FTIR analysis showed changes in functional groups before and after adsorption, confirming the ion exchange mechanism between Pb2+ and the activated bio-adsorbent. The findings suggest that H3PO4 activation increases the surface area and raises the chemical activity of WH, providing new insights into the dual mechanism of physical and chemical modifications for lead removal. This study addresses a critical gap in optimizing adsorbents for heavy metal removal, demonstrating the potential of H3PO4-activated WH for industrial wastewater treatment.
Evaluation of FIR bandpass filter and Welch method implementation for centrifugal pump fault detection Romahadi, Dedik; Feleke, Aberham Genetu; Adinarto, Tri Wahyu; Feriyanto, Dafit; Biantoro, Agung Wahyudi; Rachmanu, Fatkur
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.007

Abstract

The motivation for this research is the high vibration observed during the operation of the centrifugal cooling water pump. Our study aims to assess the pump's state and check the vibrations to ensure the factors underlying the fault of the centrifugal pump in the alkaline chlorine factory. While previous studies have primarily used spectral amplitude results from the Fast Fourier Transform to analyze engine vibrations, we propose a different approach in this study. We employ the Finite Impulse Response (FIR) Bandpass Filter and the Welch Method, a practical analytic approach. The ISO 10816-3 standard is a benchmark of the RMS value to determine the pump's condition. The FIR Bandpass Filter and Welch Method prove to be highly effective in describing and modifying the vibrational signals of the centrifugal pump. The approach is particularly beneficial as it is consistent across sample rate settings, reduces the vibration of amplitude low, produces a smoother spectrum with only the primary frequency component, and segments the vibration signal into the frequency band-aids to identify the primary vibration source. The diagnostic results reveal increased vibrations at 1x, 2x, and ball pass frequency (BPF), indicating impeller damage and disappearance. Post-repair, the vibration value experiences a significant drop, as per the fault analysis results, further confirming the high effectiveness of our approach. These findings have practical implications for the maintenance and fault diagnosis of centrifugal pumps, providing a reliable and effective method for identifying and addressing issues. 

Page 46 of 54 | Total Record : 531

 44   45   46   47   48 

Filter by Year

2013 2026


Reset
Filter By Issues
All Issue Vol 30, No 1 (2026) Vol 29, No 3 (2025) Vol 29, No 2 (2025) Vol 29, No 1 (2025) Vol 28, No 3 (2024) Vol 28, No 2 (2024) Vol 28, No 1 (2024) Vol 27, No 3 (2023) Vol 27, No 2 (2023) Vol 27, No 1 (2023) Vol 26, No 3 (2022) Vol 26, No 2 (2022) Vol 26, No 1 (2022) Vol 25, No 3 (2021) Vol 25, No 2 (2021) Vol 25, No 1 (2021) Vol 24, No 3 (2020) Vol 24, No 2 (2020) Vol 24, No 1 (2020) Vol 23, No 3 (2019) Vol 23, No 2 (2019) Vol 23, No 1 (2019) Vol 22, No 3 (2018) Vol 22, No 2 (2018) Vol 22, No 1 (2018) Vol 21, No 3 (2017) Vol 21, No 2 (2017) Vol 21, No 1 (2017) Vol 20, No 3 (2016) Vol 20, No 2 (2016) Vol 20, No 1 (2016) Vol 19, No 3 (2015) Vol 19, No 2 (2015) Vol 19, No 1 (2015) Vol 18, No 3 (2014) Vol 18, No 2 (2014) Vol 18, No 1 (2014) Vol 17, No 3 (2013) More Issue