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All Journal Jurnal Rekayasa Mesin JURNAL CRANKSHAFT
Imaduddien Ariefa
Politeknik Negeri Semarang
Control & Systems Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering

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Analisis Pengaruh Variasi Beban Terhadap Energi dan Eksergi Pada Heat Recovery Steam Generator di Blok 2 PLTGU Muara Karang Della Kumalaningrum; Novix Jefri Al Fama; Imaduddien Ariefa
JURNAL CRANKSHAFT Vol. 9 No. 2 (2026): Jurnal Crankshaft Vol. 9 No. 2 (2026)
Publisher : Badan Penerbit Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/cra.v9i2.17397

Abstract

Pada suatu sistem Pembangkit Listrik Tenaga Gas Uap (PLTGU) untuk meningkatkan suatu performa dari suatu pembangkit dapat dilakukan dengan menggunakan analisis energi dan eksergi. Analisis eksergi menggunakan konservasi massa berdasarkan Hukum II Termodinamika dimana proses termodinamika tidak selalu ideal dan terjadi penurunan kualitas energi. Metode analisis eksergi (analisis kemanfaatan) sangat tepat digunakan untuk mendorong tercapainya penggunaan sumber daya energi dengan lebih efektif karena eksergi memungkinkan untuk menentukan lokasi, penyebab, dan besar sebenarnya dari kerugian atau pemborosan suatu sistem termal yang dipengaruhi entalpi, entropi, suhu, dan tekanan. Pada penelitian ini dilakukan analisis energi dan eksergi pada Heat Recovery Steam Generator (HRSG) dengan menggunakan variasi beban. Hasil penelitian menunjukkan berapa besar kerugian atau kehilangan panas yang dihasilkan pada sub-komponen HRSG. Efisiensi energi dan efisiensi eksergi yang dihasilkan berbanding lurus dengan beban yang dihasilkan turbin gas. Pada beban 134 MW, 213 MW, dan 236 MW berturut-turut diperoleh efisiensi energia sebesar 72,7%; 75,7%; 76,7% dan efisiensi eksergi 55,3%: 59,2%; 64,1%.
Performance Enhancement of 2D CNN-Based Visual Inspection Using Data Augmentation for Defect Classification in Metal Casting Products Imaduddien Ariefa; Hutomo Jiwo Satrio; Della Kumalaningrum; Rieky Handoko; Anton Harseno; Fariz Wisda Nugraha
Jurnal Rekayasa Mesin Vol. 20 No. 3 (2025): Volume 20, Nomor 3, December 2025
Publisher : Mechanical Engineering Department - Semarang State Polytechnic

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jrm.v20i3.7170

Abstract

Deep learning-based automated visual inspection has become increasingly important for reducing the subjectivity and mistakes that come with manual inspection.  However, when the image dataset is small, Convolutional Neural Networks (CNN) often do not perform optimally because the model overfits and fails to generalize effectivelyl.  This study investigates the effect of data augmentation on enhancing the performance of an AlexNet-based CNN model for classifying defect and non-defect casting images.  There were 13266 grayscale images in total, and they were divided into two groups: defect and non-defect.  To increase data variability, several augmentation techniques were used, such as rotation, flipping, zooming, and brightness adjustment.  We evaluated two different training scenarios: training a model without adding anything and training a model with adding something.  We used accuracy, precision, recall, F1-score, validation loss, and confusion matrix analysis to evaluate model perfomance.  The findings demonstrate that data augmentation significantly improves model performance.  The validation loss decreased from 0.019747 to 0.014853, and the accuracy, precision, recall, and F1-score all showed slight improvements.  The enhanced model also achieved higher true positive and true negative values, signifying improved recognition proficiency.  Tests on previously unseen samples yielded 100% correct predictions, indicating enhanced generalization.  To sum up, data augmentation is an effective strategy for mitigating small datset limitations and improving the reliability of CNN-based visual inspection systems in industrial environments.
Co-Authors Anton Harseno Della Kumalaningrum Fariz Wisda Nugraha Hutomo Jiwo Satrio Novix Jefri Al Fama Rieky Handoko