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Andi Adriansyah
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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
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. 
Evaluation of the Performance of Corroded Concrete with Bottom Ash and Bacteria using Resistivity and Impact Echo Techniques Zaki, Ahmad; Azizah, Salma; P. Rosyidi, Sri Atmaja; Mahbubi, Khairil; Ibrahim, Zainah
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.003

Abstract

Concrete is a significant contributor to global emissions, necessitating the development of environmentally friendly alternatives. This study explores the use of reinforced concrete (RC) incorporating industrial by-products, specifically bottom ash (BA), as a partial sand replacement to address this issue. Additionally, the study examines the potential of Bacillus subtilis bacteria to enhance the self-repair capabilities of corroded RC with BA. Concrete mixtures with 10%, 20%, and 30% BA were prepared and subjected to accelerated corrosion for 48, 96, and 168 hours. The corroded RC specimens were then tested for compressive strength, flexural strength, corrosion rate, non-destructive testing (NDT) methods, and SEM analysis. NDT methods included impact echo (IE) and resistivity techniques. Results showed that increasing BA content led to a decrease in corrosion resistance, with current measurements of 2.07, 1.64, and 1.47 amperes for 10%, 20%, and 30% BA, respectively. After 168 hours of corrosion, the IE frequency of the Bacillus subtilis-treated specimens was 2561.04 Hz, the lowest among all samples, while the 30% BA specimen exhibited the highest frequency at 7924.81 Hz. Resistivity measurements after 168 hours showed lower resistivity in Bacillus subtilis-treated specimens (18.25 kΩ·cm) compared to the 20% BA specimen (29.27 kΩ·cm). These findings suggest that the addition of BA and Bacillus subtilis bacteria can reduce the corrosion risk in concrete, making it a viable alternative to traditional RC.
Assessment of revetment performance against wave overtopping for mitigating tidal flooding at Lebih Beach Eryani, I Gusti Agung Putu; Andin, Ni Nyoman Yulleta; Armaeni, Ni Komang; Araújo, Odilia Belija Do Carmo; Jayantari, Made Widya
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.008

Abstract

As one of the largest archipelagic nations, Indonesia faces significant coastal erosion challenges, particularly in Gianyar Regency, Bali, where coastline change rates have reached -11.12 m/year. To combat this issue, the Indonesian government has implemented revetment structures along the coastline, notably at Lebih Beach. This research systematically assesses the current performance of a coastal revetment structure on Lebih Beach, focusing on its ability to withstand modern wave conditions and prevent wave overtopping. The objective is to evaluate the structure’s physical integrity and functionality, especially as wave overtopping has impacted nearby communities and damaged infrastructure. The methodological framework incorporates detailed field surveys to document structural conditions and detect signs of erosion, material degradation, or damage. Topographic and bathymetric data are used to model the coastal and seabed profile, which is essential for simulating wave behavior. Wind, tide, and wave data from CMS-Wave in SMS 10.1 software provide insights into wave height, direction, and energy, helping predict wave impacts on each segment of the coastline. The research area is divided into six segments along the Lebih Beach coastline. Initial evaluations showed that segments 1 through 4 require further analysis due to evident vulnerabilities to wave forces. The reexamination compares the peak elevation of these segments, specifically their ability to withstand wave action at the established elevation of +5.00 m. This comparison allows for an accurate assessment of the structure’s resilience under current environmental pressures and guides recommendations for maintenance or reinforcement where needed. The evaluation results in segments 1, 2, 3, and 4 showed that the revetment still undergoes overtopping. Continuous monitoring and evaluation of coastal protection structures is needed to ensure the integrity of coastal communities and infrastructure in the face of ongoing environmental changes.
Evaluating the impact of autonomous material handling on the performance of production system: a simulation approach Purwaningsih, Ratna; Shintyastuti, Annisa Rahma; Arvianto, Ary; Hapsari, Chaterine Alvina Prima; Putri, Ade Aisyah Arifna; Dzulfikar, Rifki Daris
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.004

Abstract

In developing countries, low employment rates lead to manual material handling due to low labor costs. This article analyzes the impact of transitioning from semi-manual to fully automatic material handling systems by replacing the hand pallet system with AGV for transporting materials on the production floor. A simulation model was created to evaluate the impact of the transition on cost and quality. The model focuses on production lines with predetermined pathways and fixed working hours for workstations. The discrete event simulation was developed using ExtendSim. Queuing theory model is employed to assess the utilization of resources within the system. Three scenarios are developed: a human system, systems with 2 AGVs, and 3 AGVs.The findings suggest that systems with AGVs surpass human-operated systems in terms of system reliability, cost-effectiveness, and product excellence. The findings provide essential insights for management decision-making, specifically for deterministic production lines. The research findings emphasize the possibility of significant enhancements in production system performance by implementing autonomous material handling.
Investigation of mechanical properties and microstructural characteristics of rice husk ash-based geopolymer mortar as patch repair Astuti, Pinta; Isnaini, Muhammad Sakti; Sasmita, Devi; Purnama, Adhitya Yoga; Habirun, Asiya Nurhasanah; Zulkarnain, Anisa; Nouvaldi, Angga Jordi; Monika, Fanny
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.009

Abstract

The rapid expansion of the construction sector has escalated cement use, significantly impacting the environment due to CO2 emissions. Geopolymers are eco-friendly construction materials designed to reduce cement use and have the potential to be a patching material to rehabilitate concrete structures due to corrosion damage. Among these, pozzolanic materials like rice husk ash, rich in aluminosilicate, are abundant and suitable for geopolymer binders. This study explored the use of rice husk ash and alkali activators (NaOH/Na2SiO3), with different activator percentages (40%, 45%, and 50%), to evaluate their mechanical properties and potential applications as patch repair materials. This research involved formulating an optimal mix design through trial and error in a laboratory setting, followed by curing at 70 °C and testing at room temperature. XRF and SEM-EXD analyses were performed to determine the chemical composition and microstructure of the specimens. The activators, NaOH and Na2SiO3, were employed in a 1:3.5 ratio, with 14M molarity and 2% superplasticizer, to enhance workability. The test yielded the geopolymer mortar’s highest compressive strength of 8.14 MPa at a 40% activator variation. In comparison, the highest split tensile and flexural strengths were 2.50 MPa and 1.00 MPa, respectively, both at a 50% variation. These findings demonstrated the suitability of the mortar for patch repair on concrete substrates with compressive strengths below 8 MPa. The mechanical properties of the rice husk ash geopolymer mortar were influenced by the silica, calcium, and alkali activator content, affecting the mortar’s strength and density.
Bibliometric analysis of research trends in rigid pavement over the last decade Mudjanarko, Sri Wiwoho; Paikun, Paikun; Daniel, Basil David
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.005

Abstract

This study presents a bibliometric analysis of research trends in rigid pavement over the last decade, aiming to identify publication trends, research output distribution, main themes, citation patterns, and research gaps. The PRISMA method was employed, and statistical analysis was conducted using bibliometric software. By collecting bibliographic data from academic publications, this research reveals a significant growth in rigid pavement publications, reflecting increased global interest in this field. Major research themes include pavement design, material characterization, construction techniques, maintenance, and performance evaluation. Citation pattern analysis is used to identify influential works in this field. However, this study has limitations in data coverage and is susceptible to biases inherent in bibliometric analysis. Nevertheless, it contributes significantly to understanding the research landscape of rigid pavement, providing valuable insights for researchers, practitioners, and policymakers. Future research could deepen qualitative analysis, track the evolution of research themes, and explore interdisciplinary frameworks to enrich our understanding of rigid pavements.
High-performance sentiment classification of product reviews using GPU(parallel)-optimized ensembled methods Rao, Annaluri Sreenivasa; Reddy, Yeruva Jaipal; Navya, Guggilam; Gurrapu, Neelima; Jeevan, Jala; Sridhar, M.; Reddy, Desidi Narasimha; Pathuri, Siva Kumar; Anand, Dama
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.010

Abstract

Sentiment analysis is an important approach in natural language processing (NLP) that extracts information from text to infer underlying emotions or views. This technique entails classifying textual information into feelings like "positive," "negative," or "neutral." By evaluating data and labeling, client input may be classified on scales such as "good," "better," "best," or "bad," "worse," resulting in a sentiment classification. With the fast expansion of the World Wide Web, a massive library of user-generated data—opinions, thoughts, and reviews—has evolved, notably for diverse items. E-commerce firms use this data to gather attitudes and views from social media sites like Facebook, Twitter, Amazon, and Flipkart. The GPU-CUDA-ENSEMBLED algorithm is a GPU-accelerated method for sentiment classification, enhancing predictive performance by minimizing variances and biases. It outperforms existing algorithms like SLIQ and MMDBM, demonstrating GPU mining's efficiency. The proposed algorithm utilizes GPU-accelerated sentiment analysis to accurately predict smartphone ratings, providing valuable insights for businesses to maximize customer feedback potential.
Performance of speech enhancement models in video conferences: DeepFilterNet3 and RNNoise Maulana, Muhammad Iqbal; Raisul Akbar, Muhammad Fadhlillah; Iklima, Zendi
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.001

Abstract

As remote work and online education continue to gain prominence, the importance of clear audio communication becomes crucial. Deep Learning-based Speech Enhancement has emerged as a promising solution for processing data in noisy environments. In this study, we conducted an in-depth analysis of two speech enhancement models, RNNoise and DeepFilterNet3, selected for their respective strengths. DeepFilterNet3 leverages time-frequency masking with a Complex Mask filter, while RNNoise employs Recurrent Neural Networks with lower complexity. The performance evaluation in training revealed that RNNoise demonstrated impressive denoising capabilities, achieving low loss values, while DeepFilterNet3 showed superior generalization. Specifically, "DeepFilterNet3 (Pre-Trained)" exhibited the best overall performance, excelling in intelligibility and speech quality. RNNoise also performed well in subjective quality measures. Furthermore, we assessed the real-time processing efficiency of both models. Both RNNoise variants processed speech signals almost in real-time, whereas DeepFilterNet3, though slightly slower, remained efficient. The findings demonstrate significant improvements in speech quality, with "DeepFilterNet3 (Pre-Trained)" emerging as the top-performing model. The implications of this study have the potential to enhance video conference experiences and contribute to the improvement of remote work and online education.

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