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Romahadi, Dedik
Department Of Mechanical Engineering, Faculty Of Engineering, Universitas Mercu Buana
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Multiclass gas pipeline leak detection using multi-domain signals and genetic algorithm-optimized classification models Suprihatiningsih, Wiwit; Romahadi, Dedik; Pranoto, Hadi; Youlia, Rikko Putra; Anggara, Fajar; Rahmatullah, Rizky
Teknomekanik Vol. 9 No. 1 (2026): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/teknomekanik.v9i1.38372

Abstract

Pipeline networks are critical infrastructure for oil and gas transport because the occurrence of leaks can rapidly escalate into safety, economic, and environmental crises. Operators are practically required to identify the presence and type of leaks; however, applying multiclass recognition is challenging when labeled data and computing power are limited. Therefore, this study proposes a three-stage pipeline which consists of: (1) adopting the GPLA-12 dataset of acoustic or vibration signals spanning 12 leak types; (2) extracting multi-domain features by combining time-domain descriptors with Power Spectral Density (PSD)-based spectral features; and (3) applying a genetic algorithm (GA) as a wrapper for feature selection to enhance discriminability and reduce dimensionality, which was followed by benchmarking seven conventional classifiers and GA-based refinement of the top model with a focus on the feature subset and hyperparameters. A maximum accuracy of 96.35% was achieved on the GPLA-12 dataset with low computation time and a simple model architecture. The proposed pipeline also attained similar or better accuracy at substantially lower complexity and data requirements compared with prior deep CNN approaches. These results support timely multiclass decision-making in resource-constrained industrial settings. A key observation was that the focus was on supervised leak-type classification from acoustic or vibration signals, while localization, severity estimation, and multi-sensor fusion were beyond the scope of this study.
Optimization of CNC Turning Parameters for Surface Roughness of Brass 36000 Using the Taguchi Method Noviana, Agus; Fitri, Muhamad; Romahadi, Dedik
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol 7, No 3 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v7i3.37302

Abstract

Brass is widely used in industrial applications due to its excellent machinability and durability, making it well suited for CNC turning operations. Although numerous studies have investigated the optimization of turning parameters, variations in machine tools and cutting conditions often lead to differing conclusions. This study aims to optimize surface roughness in the CNC turning of Brass 36000 using the Taguchi method. An L9 orthogonal array was employed to evaluate the effects of spindle speed, feed rate, depth of cut, and coolant type. Experimental data were analyzed using signal-to-noise (S/N) ratio analysis and analysis of variance (ANOVA) to identify the most influential parameters and optimal cutting conditions. The results indicate that feed rate is the dominant factor affecting surface roughness, contributing 95.54% of the total variation, followed by spindle speed (1.88%), depth of cut (0.33%), and coolant type (0.18%). The optimal machining parameters were determined as a spindle speed of 1700 rpm, feed rate of 0.1 mm/rev, depth of cut of 1.0 mm, and the use of synthetic coolant (GT41), resulting in a minimum surface roughness of 0.67 µm. These findings demonstrate that precise control of feed rate is critical for achieving improved surface quality in CNC turning of brass.
Compound development as a protective layer on fecral substrate by a combination of γ-Al2O3 ultrasonic and NiO electroplating techniques to improve thermal stability Hidayat, Imam; Feriyanto, Dafit; Zakaria, Supaat; Abdulmalik, SS.; Nurato, Nurato; Romahadi, Dedik
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.003

Abstract

One of the most technologically advanced methods for developing and adhering catalysts to the FeCrAl substrate is electrophoretic deposition. However, it faces a problem: low thermal stability at high temperatures of 10000 °C, caused by a lack of a protective oxide layer. The goal of this study is to investigate the protective oxide layers formed by Al2O3 and NiO coatings on FeCrAl metallic material for catalytic converters (CATCO). The electrolyte was prepared with distilled water at a constant temperature of 40±50 °C. The pH was adjusted to 5 with HCl and NaOH reagents. The electrolyte was prepared at 40 ± 50 °C and stirred for 1 minute using a magnetic stirrer. A 50mm x 10mm Ni plate substrate served as the anode, while a 40mm x 20mm FeCrAl cathode was used. The spacing between the anode and cathode was set at 25mm. The electroplating was conducted for several variation times of 15, 30, 45, 60 and 75 minutes, current density of 8 A/dm2, 3g γ-Al2O3 was inserted into the beaker for each sample and the total surface area was 1600mm2 on both sides. Drying was performed after electroplating at 600 °C for 12 hours.  Raman spectroscopy revealed that several compounds observed during the experimental stages, such as FeCrAl, γ-Al2O3, NiO, NaO2, NiAl2O4, NiCr2O4, and FeCr2O3, were also present in the coated FeCrAl CATCO, with distinct peaks. Therefore, it can be concluded that the UB+EL 30 min successfully deposited the γ-Al2O3 and NiO on the FeCrAl substrate after CATCO fabrication.
Towards enhanced acoustic fan booster damage detection: a comparative study of feature-based and machine learning approaches Youlia, Rikko Putra; Romahadi, Dedik; Feleke, Aberham Genetu; Nugroho, Irfan Evi; Alina, Alina
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Machine failure detection frequently uses non-destructive monitoring techniques such as vibration analysis. Although vibration analysis can identify machine degradation, the apparatus is often costly and necessitates specialist knowledge. Additionally, many existing methods in audio classification rely on characteristics represented as pictures or vectors, which increases computational complexity. In contrast, this research introduces a novel method that substitutes vibration data with a singular numerical feature derived from audio signals, addressing both cost and complexity issues. Our objective is to develop a rapid and precise audio-based method for detecting machine damage. The acoustic signals from the machine apparatus were classified into three categories: normal, belt damage, and combined belt and bearing defect. The data processing technique involved lowering the sample rate and segmenting the data to improve computational efficiency and classification performance. We use the Welch method and appropriate statistical techniques to analyze Power Spectral Density (PSD). The performance of seven classifier models, KNN, LDA, SVM, NB, ANN, RF, and DT, was evaluated using accuracy, precision, sensitivity, specificity, and F-score. LDA achieved the highest accuracy at 92.83%, followed by ANN (92.75%), NB (92.74%), and DT (92.34%). These models outperformed KNN (89.90%) and RF (89.40%), with SVM recording the lowest accuracy at 85.40%. LDA was highly effective, achieving the highest accuracy with a single average PSD-type feature, showcasing its robustness in machine defect diagnosis. Compared to previous methods, this approach simplifies feature extraction, reduces computational demands, and maintains high diagnostic performance, providing notable benefits in terms of effectiveness and precision. 
Co-Authors Abda Abda Abdul Hamid Abdulmalik, SS. Adinarto, Tri Wahyu Aditya, Bima Agung Wahyudi Biantoro Alief Avicenna Luthfie Alief Avicenna Luthfie Alief Avicenna Luthfie, Alief Avicenna Alief Luthfie Avicenna Alina, Alina Andi Firdaus Sudarma Anggara, Fajar Arvisyah, Diar Arwati, I Gusti Ayu Auf, Abdurrahman Chaeroni, Amat Dafit Feriyanto Desti Dorion, L. B. Diah Utami Diah Utami, Diah Enggar Pur nama , Rama Fajar Anggara Fajar Anggara Fajar Anggara Feleke, Aberham Genetu Genetu Feleke, Aberham Ghufron, Hanif Golwa, Gian Villany Hadi Pranoto Hadi Pranoto Hanif Ghufron Hifdzul Luthfan Habibullah Hui Xiong Hui Xiong Ilhamullah, Ilhamullah Jalaluddin, Mai Nursherida Karmiadji, Djoko Wahyu Kurniawan, Rizki Nur Afami L. B. Desti Dorion Mahendra, Tito Syahril Sobarudin Izha Maris, Iman Maulana Yusuf Muhamad Fitri Muhammad Imran Murtyas, Solli Dwi Nanang Ruhyat Noviana, Agus Nugroho, Irfan Evi Nurato Nurato, Nurato Pramana, Putratama Aziz Rachmanu, Fatkur Rahmatullah, Rizky Rikko Putra Youlia Saputra, Gofar Julio Sisliana, Azara Vigha Solli Dwi Murtyas Sri Anah Supaat Zakaria Susilo, R. Dwi Pudji Turmudi, Agung Wang Dong Wermasaubun, Hendrikus Wibowo, Agus Setiawan Wijaya, Fathoni Putra Winnugroho Wiratman, Manfaluthy Hakim, Tiara Aninditha, Aru W. Sudoyo, Joedo Prihartono Wiwit Suprihatiningsih Xiong, Hui Yafiq, Muhammad Sulthan Yang Xiawei Yishuang, Tang Yosua Heru Irawan Zakaria Zakaria Zakaria, Supaat