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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Rekayasa Mesin Sinergi World Chemical Engineering Journal Jurnal Teknik Mesin Scientific Journal of Mechanical Engineering Kinematika Teknomekanik Jurnal Abdimas ADPI Sains dan Teknologi International Journal of Innovation in Mechanical Engineering and Advanced Materials Ramatekno Jurnal Polimesin Journal of Engineering and Technological Sciences
Dedik Romahadi
Universitas Mercu Buana
Aerospace Engineering Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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ANALISIS DISTRIBUSI TEMPERATUR PADA SPESIMEN UNTUK ALAT ULTRA VIOLET (UV) WEATHERING CHAMBER MENGGUNAKAN APLIKASI ANSYS Yusuf, Maulana; Romahadi, Dedik; Fitri, Muhamad
Jurnal Teknik Mesin (Journal Of Mechanical Engineering) Vol 12, No 2 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jtm.v12i2.16548

Abstract

Sinar UV telah memainkan peran penting dalam menurunkan kekuatan material. Radiasi UV telah dikaitkan dengan degradasi material komposit dengan tingkat degradasi bergantung pada beberapa parameter utama, seperti: panjang gelombang UV, waktu pemaparan dan intensitas UV. Alat UV Weathering Chamber adalah alat yang dapat digunakan untuk mensimulasikan berbagai kondisi cuaca untuk pengujian material spesimen komposit.. Metode yang digunakan pada penelitian ini yaitu metode elemen hingga dengan menggunakan simulasi pada aplikasi ansys untuk mengetahui distribusi termal pada specimen untuk alat uv weathering chamber. Hasil dari penelitian ini yaitu berupa nilai paparan termal atau distribusi temperature yang diterima oleh specimen dengan variasi temperatur 50°C, 60˚C dan 70°C. Simulasi pada variasi temperatur 50°C distribusi temperatur antara 28,071˚C s/d 30,01˚C. Variasi temperatur 60°C distribusi temperatur antara 28,009˚C s/d 30,93˚C. Variasi temperature 70°C distribusi simulasi 28,142˚C s/d 31,9˚C.
Implementasi Metode Elemen Hingga di Solidworks Guna Mengoptimalkan Desain Velg Depan Cast Wheel Sepeda Motor Kurniawan, Rizki Nur Afami; Romahadi, Dedik; Fitri, Muhamad
Jurnal Teknik Mesin (Journal Of Mechanical Engineering) Vol 12, No 2 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jtm.v12i2.16676

Abstract

Velg cast wheel sering mengalami kerusakan yang menyebabkan rusaknya pada bagian bibir velg atau pecahnya spoke jika menopang beban berlebih. Aspek keselamatan sangat penting diperhatikan dalam industri otomotif karena menyangkut nyawa penumpang. Optimalisasi struktural berbagai komponen kendaraan telah menunjukkan bahwa kinerja kendaraan sangat dipengaruhi oleh berat komponen. Berdasarkan permasalahan tersebut maka tujuan dibuatnya penelitian ini untuk merancang model desain velg cast wheel yang ringan namun mampu menahan beban sebesar 503 N. Sehingga perlu dibuat analisis menggunakan perbandingan model desain dan variasi material, serta dilakukan simulasi statis menggunakan software Solidworks 2018. Hasil yang dicari adalah von mises, displacement, strain, factor of safety, dan menghasilkan desain yang ringan. Hasil simulasi pada ketiga model masih aman dalam menahan beban 503 N, karena nilai factor of safety tidak kurang dari 1. Untuk massa desain dengan variasi material, mendapatkan hasil yang lebih ringan dari velg aslinya.
Analisis Komparasi Kekuatan Geometri Desain Rusuk Penguat pada Kursi Plastik menggunakan Computer Aided Engineering Saputra, Gofar Julio; Romahadi, Dedik
Jurnal Teknik Mesin (Journal Of Mechanical Engineering) Vol 12, No 1 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jtm.v12i1.16503

Abstract

Kursi plastik merupakan furnitur yang sering dijumpai di masyarakat umum karena mudah digunakan untuk dibawa ke mana-mana dan juga menghemat penyimpanan karena dapat ditumpuk. Dalam penggunaan sehari-hari, kursi plastik sering mengalami kerusakan. Untuk kinerja konstan yang ideal, kursi plastik harus memiliki kapasitas beban maksimum yang jauh lebih besar dari beban operasi, sehingga tegangan tidak merusak kursi plastik dari waktu ke waktu. Penelitian ini mengacu pada Analisis Elemen Hingga, analisis tegangan Von-Mises, dan kegagalan desain kursi plastik. Analisis dilakukan pada struktur pembebanan statis di Solidworks 2021, dengan beban sebesar 1200N yang bekerja ke bawah (sumbu Y) di sepanjang empat kaki kursi plastik yang diposisikan tetap. Analisis dilakukan pada tiga alternatif desain, yaitu desain kursi plastik tanpa rusuk penguat, kursi plastik dengan rusuk penguat model X, dan kursi plastik dengan rusuk penguat model kotak. Analisis dilakukan dengan menggunakan bahan plastik ABS. Setelah dilakukan simulasi didapatkan nilai tegangan von-mises dari ketiga alternatif desain sebesar 38,30 MPa; 30,81 MPa; dan 8,86 MPa dengan batas tegangan yang diizinkan oleh material ABS adalah 28,00 MPa. Nilai faktor keamanan dari ketiga alternatif desain adalah 0,73; 0,91; dan 3.16. Batas aman minimum untuk beban statis adalah 1,25. Dengan demikian alternatif desain yang memenuhi persyaratan adalah kursi plastik dengan rusuk penguat model kotak dengan nilai faktor keamanan 3,16. Adapun dua desain lainnya tidak aman. Lokasi tidak aman terjadi di sudut pangkal kaki kursi plastik.
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. 
Electroencephalogram-Based Multi-Class Driver Fatigue Detection using Power Spectral Density and Lightweight Convolutional Neural Networks Suprihatiningsih, Wiwit; Romahadi, Dedik; Feleke, Aberham Genetu
Journal of Engineering and Technological Sciences Vol. 57 No. 4 (2025): Vol. 57 No. 4 (2025): August
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.4.2

Abstract

Driver fatigue is the primary factor contributing to traffic accidents globally. To address this challenge, the electroencephalogram (EEG) has been proven reliable for assessing sleepiness, fatigue, and performance levels. Although alertness monitoring through EEG analysis has shown progress, its use is affected by complicated methods of collecting data and labelling more than two classes. Based on previous research, the original form of EEG signals or power spectral density (PSD) has been extensively applied to detect driver fatigue. This method needs a large, deep neural network to produce valuable features, requiring significant computational training resources. More observations regarding feature extraction and classification models are needed to reduce computational cost and optimize accuracy values. Therefore, this research aimed to propose a PSD-based feature optimization on a lightweight convolutional neural network (CNN) model. Five types of statistical functions and four types of signal power ratios were applied, and the best features were selected based on ranking algorithms. The results showed that feature optimization using the Relief Feature (ReliefF) algorithm had the highest accuracy. The proposed lightweight CNN model obtained an average intra-subject accuracy of 71.01%, while the cross-subject accuracy was 69.07%.
Designing an intelligent system for vibration diagnosis of centrifugal water-cooling pumps using Bayesian networks Suprihatiningsih, Wiwit; Romahadi, Dedik; Genetu Feleke, Aberham
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4390-4402

Abstract

Implementing monitoring methods is a viable method to reduce substantial damage to cooling water centrifugal pumps. Engaging in manual vibration analysis requires considerable time and a requisite level of competence. Small datasets pose challenges when applying classification systems that utilize linear classification models and deep learning. Given these issues, our proposal entails developing a system capable of autonomously, precisely, and accurately diagnosing vibrations using a limited dataset. The system is anticipated to possess the capability to detect multiple categories of mechanical defects, such as static imbalance, dynamic imbalance, misalignment, cavitation, looseness, and bearing corrosion. The Bayesian network (BN) structure was constructed using the MATLAB software. The input data parameters comprise vibration signals measured in the frequency domain and values representing phase differences. The constructed intelligent system was subsequently assessed using a dataset including 120 samples. The smart system can rapidly anticipate and precisely identify every form of harm with exceptional accuracy and sensitivity, relying on test outcomes. The test data analysis reveals that the intelligent system attained an average accuracy of 94.74%, precision of 95.32%, sensitivity (recall) of 93.67%, and F-score of 94.36%. 
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.
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 | DOI: 10.22441/sinergi.2026.1.016

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. 
STUDI NUMERIK KARAKTERISTIK VORTEX GENERATOR PADA MODIFIKASI AIRFOIL JOUKOWKSI DAN PADA SILINDER Re = 100,000 Fajar Anggara; Dedik Romahadi; Subekti; Alief Avicenna Luthfie
Scientific Journal of Mechanical Engineering Kinematika Vol 10 No 2 (2025): SJME Kinematika Desember 2025
Publisher : Mechanical Engineering Department, Faculty of Engineering, Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/sjmekinematika.v10i2.797

Abstract

Utilization of ocean wave energy is one of the renewable energy sources with high potential in Indonesia. The use of Vortex Induced Vibration (VIV) has been widely developed by researchers, where the vortex will self-excite to create vibrations. Of course, its efficiency will increase when installed simultaneously to form a row. Wake Induced Vortex (WIV) has greater potential because it adds vibration through a vortex generator in a row of VIVs in the wake area. This study studied the characteristics of the vortex generator that will be used in WIV. The research method used CFD simulation Fluent 2025 with 3-dimensional geometry. The mesh used is 700 thousand in the form of a hexahedral. Independent mesh studies have been conducted so that the number of meshes 700 thousand is the most optimal and does not affect the simulation results. The Y+ value used is 1, so the mesh thickness close to the wall for Re 100,000 is 1 mm. The location of the flow separation greatly affects the vortex structure and shedding frequency of each geometry. Whereas airfoil produces bigger power but it has less frequency shedding than cylinder.
Co-Authors A. M. Leman Abda Abda Abdul Hamid Abdul Hamid Abdul Hamid Abdulmalik, SS. Abdurrahman Auf Aberham Genetu Feleke Adinarto, Tri Wahyu Agung Wahyudi Biantoro Agus Noviana Alfian Noviyanto Alief Avicenna Luthfie Alina Alina Alina, Alina amat chaeroni Ana Nur Oktaviani Andi Firdaus Sudarma Anggara, Fajar Auf, Abdurrahman Azara Vigha Sisliana Chaeroni, Amat Dafit Feriyanto Desti Dorion, L. B. Diah Utami Diah Utami, Diah Fajar Anggara Feleke, Aberham Genetu Genetu Feleke, Aberham Ghufron, Hanif Gian Villany Golwa Hadi Pranoto Hadi Pranoto Hadi Pranoto Haftirman, Haftirman Hanif Ghufron Hendrikus Wermasaubun Hifdzul Luthfan Habibullah Himawan S. Wibisono Hui Xiong Hui Xiong Hui Xiong I Gusti Ayu Arwati Ilhamullah, Ilhamullah Imam Hidayat Irfan Evi Nugroho Jalaluddin, Mai Nursherida Karmiadji, Djoko Wahyu Kurniawan, Rizki Nur Afami L. B. Desti Dorion Mahendra, Tito Syahril Sobarudin Izha Mahesh Kumar Maris, Iman Maulana Yusuf Md Radwanul Karim Muhamad Fitri Muhammad Imran Muhammad Imran Muhammad Imran Murtyas, Solli Dwi Nanang Ruhyat Noviana, Agus Nugroho, Irfan Evi Nurato Pramana, Putratama Aziz Putratama Aziz Pramana Rachmanu, Fatkur Rahmatullah, Rizky Rikko Putra Youlia Rizki Nur Afami Kurniawan Samir Sani Abdulmalik Saputra, Gofar Julio Sisliana, Azara Vigha Solli Dwi Murtyas SS Abdulmalik SS. Abdulmalik Subekti Supaat Zakaria Supaat Zakaria Supaat Zakaria Susilo, R. Dwi Pudji Tang Yishuang Turmudi, Agung Wang Dong Wermasaubun, Hendrikus Wibowo, Agus Setiawan Wijaya, Fathoni Putra Wiwit Suprihatiningsih Xiong, Hui Yafiq, Muhammad Sulthan Yang Xiawei Yishuang, Tang Yosua Heru Irawan Yudha Aji Pramono Zakaria Zakaria Zakaria, Supaat