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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Rekayasa Mesin Sinergi Jurnal Teknik Mesin International Journal of Advanced Technology in Mechanical, Mechatronics and Material (IJATEC) Teknomekanik JTTM: Jurnal Terapan Teknik Mesin Akuntansi dan Humaniora: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Bhinneka International Journal of Innovation in Mechanical Engineering and Advanced Materials Journal of Engineering and Technological Sciences
Wiwit Suprihatiningsih
Universitas Mercu Buana
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences

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Opacity Results Diesel Fuel: Bio Solar, Dexlite, Dex and Analysis Theoretical Flammability Limit Hadi Pranoto; Wiwit Suprihatiningsih; Muhammad Idil Fadil; Supaat Zakaria
International Journal of Advanced Technology Vol 1, No 1 (2020)
Publisher : Institute for Research on Innovation and Industrial System (IRIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1238.016 KB) | DOI: 10.37869/ijatec.v1i1.10

Abstract

Each mixture of fuel and gas has a different flame speed. Gas will only burn at a suitable percentage of air and produce different exhaust gas opacity, opacity is a ratio of the rate of light absorption by smoke expressed in units of percent. This study aims to theoretically analyze the relationship between the flammability limit and the variation of fuel which has a different setana number associated with the exhaust gas opacity value of the engine performance test equipment. The machine performance test equipment used is the L300 engine. The methodology used is the testing of exhaust gas opacity using the Koeng OP-201 opacity meter and theoretically analyzed its relationship with the bio solar, dexlite and pertamina dex flame limits. The results of this study found that bio solar has an upper flame limit of 6.65%, a lowerflame limit of 0.53%, and an average opacity value of 12.1%. Dexlite has an upper limit of 6.70%, a lower limit of 0.53%, and an average opacity value of 10.5%. Pertamina dex has an upper limit of 6.68%, a lower limit of 0.53%, and an average opacity value of 9.21%.
Development of a smart system for gasoline car emissions diagnosis using Bayesian Network Dedik Romahadi; Wiwit Suprihatiningsih; Yudha Aji Pramono; Hui Xiong
SINERGI Vol 27, No 2 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2023.2.009

Abstract

A vehicle exhaust emissions test is an activity carried out to determine the content of the remaining combustion products that occur in the fuel in the vehicle engine. Many people do not understand exhaust gas content from emission tests, so to make this easier, this study aims to create a smart application that can diagnose vehicle emissions quickly and accurately using the Bayesian Network (BN) algorithm. Application development begins with BN modeling using the MSBNx application until the appropriate results are achieved. Validation of the BN structure that has been designed with various inputs is carried out to ensure that the BN modeling is correct. The next step is to compile the BN modeling algorithm in the MATLAB application so that it becomes a system that can process input in the form of measurement results for Toyota car emissions. The new BN model for vehicle emission gas diagnosis has been successfully constructed. The results of the system reading when there is an HC content of 217 ppm, the probability value of bad emissions increases to 63.5%. Of the 10 tests performed, the system was able to diagnose them all correctly.
INDUCED DRAFT FAN DOMINANT FREQUENCY DETECTION USING SHORT-TIME FOURIER TRANSFORM METHOD Dedik Romahadi; Wiwit Suprihatiningsih; Gian Villany Golwa; Mahesh Kumar
Jurnal Rekayasa Mesin Vol. 14 No. 2 (2023)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v14i2.1305

Abstract

Weak suction and large vibrations indicate an Induced Draft Fan (IDF) problem. The Fast Fourier Transform (FFT) method cannot be applied to non-stationary vibration signals. Therefore, this study aims to analyze non-stationary vibration signals for IDF vibration signals at start-up so that the source of damage to the IDF can be found. The research process begins with a brief measurement of both bearing locations with horizontal and axial axes. Processing of the vibration signal from the measurement using the FFT method and the Short Time Fourier Transform (STFT). Based on the STFT spectrogram graph for measurements on the horizontal and axial axes, the dominant frequency values are the same. The frequency with the largest amplitude value is at one RPM IDF or 25 Hz. High vibration at 1 RPM is a big indication that the IDF is experiencing unbalance.
Performance analysis of a three-blade spiral horizontal axis wind turbine with an aspect ratio of 0,116 Wiwit Suprihatiningsih; Abdul Hamid; Adji Nur Fadilah
JTTM : Jurnal Terapan Teknik Mesin Vol 6 No 1 (2025): JTTM: Jurnal Terapan Teknik Mesin
Publisher : Teknik Mesin - Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/jttm.v6i1.1291

Abstract

The horizontal-axis wind turbine (TASH) is a wind energy technology that can generate electricity, but its performance still has to be improved. To determine how much an aspect ratio affects wind turbine performance, this study used a horizontal-axis wind turbine with three spiral blades and an aspect ratio of 0,116. The primary performance metrics of horizontal-axis wind turbines with three spiral blades and an aspect ratio of 0,116 are to be measured and examined in this work. Understanding how aspect ratio affects voltage (volts), current (amperes), torque (τ), turbine rotational speed (rpm), and in line with wind speed (ρw) in the process of generating electrical energy is the specific goal. A horizontal-axis wind turbine with three spiral blades and an aspect ratio of 0,116 will be constructed and tested in a controlled setting as part of this research's experimental methodology. The systematic measurement of essential performance characteristics, such as wind speed, torque, turbine rotational speed, voltage, and current, will provide a comprehensive understanding of the wind turbine's ability to generate electrical energy. The TASH experiment 3 spiral blades with an aspect ratio of 0,116 yielded the highest value when tested using a planetary gearbox at 24,23 rpm, 53,09 V of voltage, 1,25 A of electric current, and 30,94 Nm of torque at 5,0 m/s of wind speed. In the calculation of the power coefficient, torque coefficient, and tip speed ratio, the minimum value obtained in the test without a planetary gearbox was 0,0231 at wind speed 2,5 m/s, 0,2183 at wind speed 2,5 m/s, and 0,1059 at wind speed 2,5 m/s. The minimum value is obtained in the TASH test without a planetary gearbox with a rpm value of 105,89 rpm, Voltage 8,60V, Electric Current 0,05A, and Torque 2,85Nm at wind speed 2,5 m/s. A planetary gearbox with a power coefficient of 0,4713 at a wind speed of 4.5 m/s, a torque coefficient of 0,6813 at a wind speed of 4,0 m/s, and a tip speed ratio of 0,7525 at a wind speed of 5,0 m/s was used to test the system and determine the maximum value.
Application of Science and Technology in Waste Management, Digital Marketing-Branding Strategies, and Accounting Systems for Environmentally Friendly Fertilisers and Recycled Products IMANINGSIH, Erna Sofriana; SUPRIHATININGSIH, Wiwit; SETIYAWATI, Hari; PUTRA, Rangga Aji; SALMAN, Kina Bahru; ARIYANTO , Satrio Muhammad
Akuntansi dan Humaniora: Jurnal Pengabdian Masyarakat Vol. 3 No. 2 (2024): Akuntansi dan Humaniora: Jurnal Pengabdian Masyarakat (Juni – September 2024)-I
Publisher : Indonesia Strategic Sustainability

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38142/ahjpm.v3i2.1156

Abstract

This PKM was carried out by the UMB team in partnership with the Nurul Yaqin Recitation Group, Parigi Baru Village, Pondok Aren District, South Tangerang, and was funded by DRTPM DIKTI through the PPM Grant Program for the 2024 fiscal year scheme. The Nurul Yaqin Recitation Group has 19 members; apart from being homemakers, they also have informal workers and small home industry businesses. This recitation group carries out its routine activities, namely conducting religious studies under the care of the DKM Masjid Baitul Mukminin. It is active in various community activities and can mobilize other community members through activities. Therefore, they have the potential to be pioneers among grassroots communities to change people's lifestyles for the better through the green movement in their area. The problems experienced by partners are: 1) the accumulation of organic and inorganic waste in the area around their residence has not been appropriately managed, 2) There is no utilization of business opportunities for compost and recycled crafts, 3) There is no accounting application system to record transactions and financial reporting from the production of liquid fertilizer and solid fertilizer and the recycling of inorganic waste. The solutions offered are: 1) Provide training and counseling related to processing organic and inorganic waste into valuable products 2) Provide training related to digital marketing strategies 3) Provide training related to recording transactions and financial reporting through accounting software applications.
Pemodelan Kipas Angin Dengan Penggabungan Benda Geometri Ruang Hasil Deformasi Bola Dan Ruang Suprihatiningsih, Wiwit
Jurnal Teknik Mesin (Journal Of Mechanical Engineering) Vol 10, No 2 (2021)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jtm.v10i2.6555

Abstract

Deformasi adalah perubahan bentuk atau ukuran dari sebuah objek. Hasil dari deformasi bola dan tabung akan menghasilkan berbagai bentuk bangun ruang yang dapat digunakan untuk mengkontruksi kipas angin. Di pasaran sudah banyak kipas angin yang bentuknya berasal dari benda-benda geometri ruang seperti tabung, bola, elips dan sebagainya. Penelitian ini mengkontruksi kipas angin yang ada dipasaran dengan menggabungkan benda-benda geometri ruang yaitu bola, tabung serta hasil deformasi bola dan tabung. Tujuan dari penelitian ini adalah mengkontruksi kipas angin dari hasil deformasi bola dan tabung. Penelitian ini menghasilkan kontruksi kipas angin dari berbagai deformasi bola dan tabung dengan 2 tahap yaitu (1) menentukan deformasi bola dan tabung dan (2) menggabungkan hasil deformasi bola dan tabung menjadi sebuah model kipas angin.
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%.
Pelatihan Pengoperasian Mesin Pengurai Sabut Kelapa Di RPTRA Menara Kelurahaan Kembangan Selatan Jakarta Barat Subekti, Subekti; Indah, Nur; Pratiwi, Swandya Eka; Wahyudi, Haris; Anggara, Fajar; Sudarma, Andi Firdaus; Carles, Henry; Sari, Andarany Kartka; Suprihatiningsih, Wiwit
Jurnal Pengabdian Masyarakat Bhinneka Vol. 3 No. 4 (2025): Bulan Juli
Publisher : Bhinneka Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58266/jpmb.v3i4.250

Abstract

\ Proses pengolahan limbah sabut kelapa dengan menggunakan mesin pengurai sabut kelapa dapat memudahkan proses produksi. Hasil produksi dari penguraian sabut menghasilkan cocofiber dan cocofeat yang dapat di manfaatkan dalam berbagai macam produk seperti jok mobil, matras, keset, kerajinan tangan, papan serat, serta produk ramah lingkugan lainnya. Hal tersebut menunjukkan bahwa penggunaan produk-produk rumahan tidak hanya berasal dari bahan baku sintetis. Pemanfaatan sabut kelapa dengan sumber bahan baku yang relatif mudah didapatkan dapat membantu perindustrian ekonomi kecil dan menengah, dengan demikian pengabdian masyarakat melalui merancang mesin pengurai sabut kelapa menggunkan motor bakar bensin sebagai penggerak mesin untuk mempermudah proses penguraian sabut kelapa sebelum diproduksi. Sehingga tujuan pengabdian masyarakat dalam menanfaatkan limbah serabut kelapa menjadi barang dapat bernilai ekonomis sehingga dapat meningkatkan taraf hidup masyarakat sekitar. Kegiatan ini dilakukan dengan ceramah dan demo mesin pengurai sabut kelapa. Masyarakat yang akan terlibat dari kegiatan ini sekitar 40 orang yang diatur oleh RT/RW setempat, Kegiatan ini sangat diapresiasi oleh para peserta dimana hampir 88 % peserta memahami isi materi dan praktek Mesin Serabut Kelapa. Sedangkan untuk nilai terendah sekitar 72 % menyatakan bahwa kegiatan ini sangat diperlukan oleh para peserta dan berlangsung sukses dengan banyaknya pertanyaan dan saran agar kegiatan ini dilanjutkan.
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.
Co-Authors Abdul Hamid Adji Nur Fadilah Andi Firdaus Sudarma Anggara, Fajar Arief Priyanto Arief Suardi Nur Chairat ARIYANTO , Satrio Muhammad Chairat, Arief Suardi Nur Dedik Romahadi Denny Prumanto Erna Sofriana Imaningsih Feleke, Aberham Genetu Genetu Feleke, Aberham Gian Villany Golwa Hadi Pranoto Hadi Pranoto Hari Setiyawati Henry Carles Hui Xiong Kina Bahru SALMAN Mahesh Kumar Muhammad Idil Fadil Nur Indah, Nur Nurato Priyanto, Arief Prumanto, Denny PUTRA, Rangga Aji Rahmatullah, Rizky Rangga Aji PUTRA Rikko Putra Youlia SALMAN, Kina Bahru Sari, Andarany Kartka Satrio Muhammad ARIYANTO Subekti Subekti Supaat Zakaria Swandya Eka Pratiwi Wahyudi, Haris Yudha Aji Pramono