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PERBANDINGAN NILAI KEKUATAN TARIK KOMPOSIT MENGGUNAKAN METODE HAND LAY UP DAN METODE VARI Utami, Lega Putri; Ginting, Delovita; Nasution, Ahmad Kafrawi; Istana, Budi
Sistem Informasi Vol 9 No 2 (2019): Jurnal Photon
Publisher : Fakultas MIPA dan Kesehatan Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (152.428 KB)

Some Types of the method of making composite materials are the Vacuum Assited Resin Infusion (VARI) Method and the Hand Lay Up Method. The purpose of this study was to compare the tensile strength of composite materials made using the hand lay up and VARI methods. The types of fibers used as composite materials are palm frond fibers and matrices used in polyester resin. Composites reinforced with palm fronds are printed using the hand lay up and VARI methods. The results showed the value of composite tensile strength with the hand lay up method of 27.37 MPa and composite tensile strength using the VARI method of 28.40 MPa. From the results of the study, the differences in the tensile strength values of the two methods were obtained.

PELATIHAN HEAT TREATMENT SEDERHANA UNTUK SISWA SMK SE-PEKANBARU Budi Istana; Sunaryo Sunaryo; Ahmad Kafrawi Nasution; Abrar Ridwan; Legisnal Hakim; Lega Putri Utami
Jurnal Pengabdian UntukMu NegeRI Vol 1 No 2 (2017): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Industri pengecoran logam umumnya menggunakan baja karbon sebagai bahan baku utama. Hal ini disebabkan oleh besarnya kebutuhan industri terutama industri pengolahan kelapa sawit, kertas dan industri lainnya terhadap komponen mesin yang diproduksi dengan teknik pengecoran logam. Banyak dipakainya baja karbon pada industri tersebut mengakibatkan bahan tersebut harus mengalami penyesuaian pada sifat mekanis yang diinginkan oleh pemakainya, salah satu langkah yang dapat diambil adalah dengan melakukan proses perlakuan panas, proses ini akan sangat bergantung pada komposisi kimia bahan, suhu pemanasan, waktu penahanan (hold time) dan kecepatan pendinginan (cooling rates). Kegiatan Pengabdian ini bertujuan untuk memberikan pengetahuan kepada siswa SMK di lingkungan kampus tentang pengaruh proses pendinginan paska perlakuan panas terhadap sifat mekanik logam terutama nilai kekerasannya. Kegiatan ini diharapkan dapat menjadi motivasi bagi siswa dalam mempelajari ilmu metalurgi.

PELATIHAN PENELITIAN TINDAKAN KELAS (PTK) BAGI GURU SMKN 1 KECAMATAN MEMPURA KABUPATEN SIAK Lega Putri Utami; Legisnal Hakim; Ahmad Kafrawi Nasution; Sunaryo Sunaryo; Abrar Ridwan; Budi Istana; Afdhal Afdhal; Ridwan Abdurahman
Jurnal Pengabdian UntukMu NegeRI Vol 3 No 1 (2019): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Classroom Action Research (CAR) is research conducted by teachers in their own class by planning, implementing and reflecting actions collaboratively and participatively with the aim of improving performance as a teacher so that student learning outcomes can improve. Thus PTK can facilitate teachers to develop an understanding of pedagogy in order to improve their learning. This training also invites teacher friends to step into various records that after being processed can manifest into a quality study. Carry out research on what is done daily by a teacher who can finally produce a work called PTK. This can happen if a general sequence of procedures, which starts from the identification of research problems encountered until the final report is recorded. So, it is very important that this procedure is understood and adhered to by the teacher who is researching. The overall service activities are quite good in terms of the target number of participants and enthusiasm in receiving the material provided.

Pemanfaatan Smartphone Untuk Meningkatkan Kreatifitas Kube Posdaya Permata Bunda Di Kecamatan Rumbai Pesisir Vitriani Vitriani; Lega Putri Utami; Willyansyah Willyansyah
Jurnal Pengabdian UntukMu NegeRI Vol 4 No 1 (2020): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Kecamatan Rumbai Pesisir memiliki beberapa Kelompok Usaha Bersama (KUBE). Diantaranya adalah KUBE Posdaya Permata Bunda. KUBE ini dikelola oleh ibu-ibu rumah tangga kreatif yang mempunyai keinginan tinggi untuk berkarya guna membantu perekonomian keluarga. KUBE ini menghasilkan beragam kerajinan tangan dari bahan plastik bekas antara lain tas, tempat buah, tissue. Tujuan pelaksanaan kegiatan ini adalah memberikan informasi dan pelatihan kepada ibu ibuKUBE Posdaya Permata Bunda tetang pemanfaatan smatrphone sebagai tempat promosi produ yang dihasilkan oleh KUBE Posdaya Permata Bunda. Pelaksanaan kegiatan ini dimulai dengan pengenalan macam macam media promosi melalui sosial media seperti facebook, instagram, WA dan sosial media lainnya. Kegiatan dilanjutkan dengan pembuatan akun media sosial dan bagaimana cara mempromosikan hasil kerajinan melalui media sosial. Target dari pelaksanaan pengabdian ini adalah memberikan kontribusi kepada masyarakat melalui penggunaan smartphone guna meningkatkan penjualan hasil produksi.

Perbandingan Nilai Kekuatan Tarik Komposit Menggunakan Metode Hand Lay Up Dan Metode Vari Lega Putri Utami; Delovita Ginting; Ahmad Kafrawi Nasution; Budi Istana
Sistem Informasi Vol 9 No 2 (2019): Jurnal Photon
Publisher : LPPM Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jp.v9i2.1357

Some Types of the method of making composite materials are the Vacuum Assited Resin Infusion (VARI) Method and the Hand Lay Up Method. The purpose of this study was to compare the tensile strength of composite materials made using the hand lay up and VARI methods. The types of fibers used as composite materials are palm frond fibers and matrices used in polyester resin. Composites reinforced with palm fronds are printed using the hand lay up and VARI methods. The results showed the value of composite tensile strength with the hand lay up method of 27.37 MPa and composite tensile strength using the VARI method of 28.40 MPa. From the results of the study, the differences in the tensile strength values of the two methods were obtained.

Pemanfaatan Panas Buang Tungku Gasifikasi Penghasil Listrik Menggunakan Termoelektrik Sebagai Solusi Limbah Pabrik Tahu di Desa Tanah merah Kecamatan Siak Hulu Kabupaten Kampar, Provinsi Riau Ridwan Abdurrahman; Abrar Ridwan; Lega Putri Utami
BATOBO: Jurnal Pengabdian Kepada Masyarakat Vol 1 No 1 (2023): BATOBO: Juni 2023
Publisher : Jurusan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/batobo.1.1.29-35

Limbah pabrik tahu di Desa Tanah Merah, Kecamatan Siak Hulu, Kabupaten Kampar, Provinsi Riau, dapat menjadi masalah lingkungan yang signifikan. Limbah ini terutama terdiri dari panas buang yang dihasilkan selama proses produksi tahu menggunakan tungku gasifikasi. Namun, panas buang ini biasanya tidak dimanfaatkan secara efisien dan hanya terbuang sia-sia. Oleh karena itu, perlu dicari solusi yang ramah lingkungan dan berkelanjutan untuk memanfaatkan panas buang ini. Dalam penelitian ini, kami mengusulkan pemanfaatan panas buang tungku gasifikasi pabrik tahu menggunakan modul termoelektrik. Modul termoelektrik adalah perangkat yang dapat mengubah perbedaan suhu menjadi energi listrik. Dalam konteks ini, panas buang dari tungku gasifikasi akan digunakan untuk menghasilkan suhu tinggi pada satu sisi modul termoelektrik, sedangkan suhu lingkungan akan berfungsi sebagai suhu rendah pada sisi lainnya. Perbedaan suhu ini akan menciptakan gradien suhu yang akan menghasilkan potensi listrik melalui efek Seebeck dalam modul termoelektrik. Dengan menerapkan sistem ini, panas buang dari tungku gasifikasi dapat diubah menjadi energi listrik yang dapat dimanfaatkan untuk memenuhi kebutuhan listrik pabrik tahu. Selain itu, ini juga akan membantu mengurangi emisi gas rumah kaca dan dampak negatif lainnya terhadap lingkungan. Dengan memanfaatkan energi panas yang sebelumnya terbuang sia-sia, pabrik tahu dapat mengurangi penggunaan energi konvensional dan meminimalkan dampak negatif terhadap lingkungan. Penelitian lebih lanjut dan implementasi praktis perlu dilakukan untuk memastikan kelayakan dan efektivitas pemanfaatan panas buang ini dalam skala yang lebih besar.

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

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.

MODELING PISTON DAMAGE DETECTION USING A CONVOLUTIONAL NEURAL NETWORK BASED ON DIGITAL IMAGE Utami, Lega Putri; Leni, Desmarita
International Journal of Multidisciplinary Research and Literature Vol. 3 No. 2 (2024): INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND LITERATURE
Publisher : Yayasan Education and Social Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53067/ijomral.v3i2.189

Product inspection is a crucial component of product quality control, aiming to evaluate and ensure that products meet predefined standards. In this research, the modelling of piston damage detection is conducted using a Convolutional Neural Network (CNN). The dataset employed consists of images of pistons categorized into three groups: Defected1, Defected2, and Normal. Two hundred eighty-five images are utilized as training data, with the data distribution percentages for Defected1, Defected2, and Normal being 30.9%, 34.4%, and 34.7%, respectively. The model is validated using newly generated data through augmentation techniques, resulting in 60 images. The CNN model uses a sequential Keras architecture comprising convolutional layers, pooling layers, fully connected layers, and softmax activation. The Adam optimizer with a learning rate 0.0001 is employed for model training, with validation using a 5-fold cross-validation. The model is evaluated using the Loss, Accuracy, and Confusion Matrix, achieving a training accuracy of 0.722 and a validation accuracy of 0.689. An early stopping function is applied to halt training when there is no improvement in validation accuracy. The confusion matrix results indicate that the model adequately classifies data with Accuracy, Recall, and Precision values of 69%, 69%, and 70%, respectively

Predictive Modeling For Low Alloy Steel Mechanical Properties: A Comparison Of Machine Learning Algorithms And Parameter Optimization Leni, Desmarita; Putri Utami, Lega; Sumiati, Ruzita; Camim, Moh.; Khan, Sharif
IJIMCE : International Journal of Innovation in Mechanical Construction and Energy Vol. 1 No. 1 (2024): IJIMCE : International Journal of Innovation in Mechanical Construction and Ene
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ijimce.v1i1.7

The development of machine learning in predicting the mechanical properties of alloy steel has become an important research subject in recent years. This is due to the ability of machine learning to extract complex patterns from large and intricate data, which can be used to understand the relationship between chemical composition, microstructure, and mechanical properties of alloy steel. This research aims to design a machine learning model to predict the mechanical properties of low alloy steel, such as Yield Strength (YS) and Ultimate Tensile Strength (UTS), based on the percentage composition of chemical elements in low alloy steel and the heat treatment applied. The machine learning model in this study consists of 10 input variables and 2 target variables. The research compares the performance of 3 machine learning algorithms, namely Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The research findings indicate that the ANN algorithm model performs best in predicting the mechanical properties of low alloy steel. This model has Mean Absolute Error (MAE) values of 16.5 and 19.593 for predicting YS and UTS, Root Mean Square Error (RMSE) values of 19.111 and 22.005, and coefficient of determination (R) values of 0.964 and 0.947 for YS and UTS respectively. The modeling uses the ANN algorithm with an 80% training data and 20% testing data split, and applies the K-Fold Cross Validation method with a value of K=5. The best parameters obtained are a learning rate of 0.001, momentum of 0.1, and a hidden layer neuron count of 9. These results indicate that ANN has great potential in addressing the complexity and variability in material data. The implications of these findings are that the implementation of ANN in manufacturing and material engineering industries can enhance the accuracy and efficiency in material strength prediction processes, which, in turn, can aid in designing and developing better and more durable products.

Prediction of aluminum alloy mechanical properties using synthetic data generated by generative adversarial networks Lega Putri Utami; Armijal Armijal; Desmarita Leni; Andre Febrian Kasmar
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

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.

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