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All Journal Rekayasa Mesin Jurnal Pengabdian UntukMu NegeRI JURNAL PHOTON Journal Of Khairun Community Services International Journal of Multidisciplinary Research and Literature (IJOMRAL) BATOBO: Jurnal Pengabdian Kepada Masyarakat Jurnal Polimesin IJIMCE : International Journal of Innovation in Mechanical Construction and Energy
Lega Putri Utami
Universitas Andalas
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Social Sciences Other

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STUDI PENGARUH PERLAKUAN ALKALI TERHADAP SIFAT MEKANIK DAN MORFOLOGI KOMPOSIT SERAT PELEPAH KELAPA SAWIT BERMATRIK UREA FORMALDEHIDA Istana, Budi; Utami, Lega Putri
Jurnal Rekayasa Mesin Vol. 15 No. 2 (2024)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v15i2.1725

Abstract

Palm frond residue is one of the valuable wastes from palm oil plantations. This refuse can be repurposed and transformed into materials for producing acoustic composites. This study investigates the mechanical and morphology characteristics of a composite material reinforced with natural fiber palm fronds and a urea-formaldehyde (UF) as a matrix. Two parameters are formulated: the effect of alkali treatment on the fiber and the effect of density. The treatment parameter refers to the particles without treatment, 60 and 180 minutes of 2% alkaline immersion. Composite densities were determined with 0.4, 0.5, and 0.6 g/cm3. The composite was made using hot pressed at a pressure of 1.8 MPa, a temperature of 140OC for 5 minutes with 10% Urea Formaldehyde resin. Alkaline treatment and density of composite have a significant effect on mechanical and morphological characteristics. The best mechanical characteristics were obtained from panels with a 0.6 g/cm3 density, without treatment, MOE: 533.53 N/mm2. The results of this study have the potential to lead to the use of sustainable palm oil waste materials in novel products, which has a significant impact and great relevance not only from environmental aspects but also from social and economic aspects in Indonesia.
Prediction of aluminum alloy mechanical properties using synthetic data generated by generative adversarial networks Utami, Lega Putri; Armijal, Armijal; Leni, Desmarita; Kasmar, Andre Febrian
Jurnal Polimesin Vol 23, No 2 (2025): April
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v23i2.6084

Abstract

Machine learning models are widely used to predict the mechanical properties  of aluminum  alloys.  However,  their  accuracy  is  often hindered   by  the  scarcity  of high-quality   tensile  test  data,  as experimental   data  collection  is  costly  and  time-consuming.  To address this limitation, this study employs Generative Adversarial Networks  (GANs)  to  generate   synthetic   tensile  test  data  for aluminum alloys, improving the accuracy of predictive models. The dataset consists of 200 real samples containing the compositions of nine chemical elements and two mechanical properties-Yield Strength (YS) and Ultimate Tensile Strength (UTS). A trained GAN model   was  used  to  generate   1,000  synthetic  samples,  whose statistical similarity to the original dataset was validated using the Kolmogorov-Smirnov (KS)  test and Pearson  correlation  analysis. The results confirmed  that all synthetic variables  retained  similar distributions and correlation patterns to the original dataset. To evaluate the impact of synthetic data on predictive  accuracy,  three machine learning algorithms-Random Forest Regressor (RF), Gradient Boosting Regressor (GBR), and Ada Boost Regressor (ABR)-were tested under two training schemes: (1) synthetic data for training and real data for testing and (2) real data for both training and testing. The RF model showed the highest improvement in UTS prediction,  with reductions of 38.3% and 46.3% in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE),  respectively. The GBR model exhibited notable enhancements in YS prediction, with MAE and RMSE reductions of22.5% and 28.3%. These results demonstrate that GAN-generated  synthetic data is highly effective in  improving  machine  learning  predictions   of aluminum  alloy properties, particularly when experimental data is limited.
Co-Authors Abrar Ridwan Afdhal Afdhal Ahmad Kafrawi Nasution Ahmad Kafrawi Nasution, Ahmad Kafrawi Armijal, Armijal Budi Istana Camim, Moh. Delovita Ginting, Delovita Desmarita Leni Iskandar R Iskandar R, Iskandar R Kasmar, Andre Febrian Khan, Sharif Legisnal Hakim Ridwan Abdurahman Ridwan Abdurrahman Ruzita Sumiati Sunaryo Sunaryo Vitriani Vitriani Widian, Raihan Khairi Willyansyah Willyansyah Yani, Endri Zulkifli Amin