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All Journal Majalah Ilmiah MOMENTUM International Journal of Engineering, Science and Information Technology
Dian Kurnia
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Pengaruh Kuat Arus Pengelasan SMAW Terhadap Kekuatan Tarik dengan Metode Double Joint pada Material Baja Carbon Bukhari Bukhari; Herry Darmadi; Muhammad Iqbal Harapan Muslim Siregar; Poltak Evencus Hutajulu; Dian Kurnia; Noer Akbar Tambunan
JURNAL ILMIAH MOMENTUM Vol 19, No 1 (2023)
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jim.v19i1.8048

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh kuat arus pengelasan  terhadap kekuatan tarik hasil pengelasan bahan baja carbon dengan metode SMAW menggunakan elektroda E7018. Dalam penelitian ini juga dilakukan pengujian kuat arus pengelasan dengan beberapa variasi kuat arus yaitu 80A, 100A, 120A, pengujian ini akan diketahui daya tahan material baja carbon yang telah dilakukan pengelasan dan direpresentatifkan pada grafik kuat arus vs tegangan Tarik.  pada pengelasan bahan baja  carbon dengan metode SMAW menggunakan elektroda E7018 melalui metode   Non Destructive Test (NDT) pada penelitian ini didapat hasil pengujian yang paling sesuai dari tiga hasil pengujian yang telah dilakukan yaitu dengan menggunakan arus pengelasan 120 Ampere dengan hasil pengujian tarik sebesar 1,2063 kgf/mm2. Untuk hasil pengujian cacat las dengan metode penetrant maka di peroleh arus yang paling bagus dikarenakan mengalami kecacatan paling sedikit dalam melakukan pengelasan bahan baja carbon yaitu dengan menggunakan arus 120 Ampere. Manfaat dari penelitian ini yaitu diketahuinya arus yang sesuai pada proses pengelasan bahan baja carbon dengan metode SMAW menggunakan elektroda E7018 serta dapat mengetahui kekuatan dan kualitas hasil pengelasan       dengan metode Non Destructive Test (NDT) yang terbaik yang mana dengan menggunakan arus 120 Ampere, diketahui terdapat cacat las porositas. porositas yang terjadi dapat berupa cacat pengelasan dengan hasil pengelasan yang  berlubang-lubang keci l pada weld metal (logam las) baik berada dipermukaan maupun didalamnya
Hybrid Deep Fixed K-Means (HDF-KMeans) Muhammad Khahfi Zuhanda; Kelvin Leonardi Kohsasih; Pieter Octaviandy; Hartono Hartono; Dian Kurnia; Nurliana Tarigan; Manan Ginting; Manahan Hutagalung
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.913

Abstract

K-Means is one of the most widely used clustering algorithms due to its simplicity, scalability, and computational efficiency. However, its practical application is often hindered by several well-known limitations, such as high sensitivity to initial centroid selection, inconsistency across different runs, and suboptimal performance when dealing with high-dimensional or non-linearly separable data. This study introduces a hybrid clustering algorithm named Hybrid Deep Fixed K-Means (HDF-KMeans) to address these issues. This approach combines the advantages of two state-of-the-art techniques: Deep K-Means++ and Fixed Centered K-Means. Deep K-Means++ leverages deep learning-based feature extraction to transform raw data into more meaningful representations while employing advanced centroid initialization to enhance clustering accuracy and adaptability to complex data structures. Complementarily, Centered K-Means improve the stability of clustering results by locking certain centroids based on domain knowledge or adaptive strategies, effectively reducing variability and convergence inconsistency. Integrating these two methods results in a robust hybrid model that delivers improved accuracy and consistency in clustering performance. The proposed HDF-KMeans algorithm is evaluated using five benchmark medical datasets: Breast Cancer, COVID-19, Diabetes, Heart Disease, and Thyroid. Performance is assessed using standard classification metrics: Accuracy, Precision, Recall, and F1-Score. The results show that HDF-KMeans outperforms traditional K-Means, K-Means++, and K-Means-SMOTE algorithms across all datasets, excelling in overall accuracy and F1 Score. While some trade-offs are observed in specific precision or recall metrics, the model maintains a solid balance, demonstrating reliability. This study highlights HDF-KMeans as a promising and effective solution for complex clustering tasks, particularly in high-stakes domains like healthcare and biomedical analysis.
Co-Authors Bukhari Bukhari Hartono Hartono Herry Darmadi Hutajulu, Poltak Evencus Kelvin Leonardi Kohsasih Manahan Hutagalung Manan Ginting Muhammad Iqbal Harapan Muslim Siregar Muhammad Khahfi Zuhanda Noer Akbar Tambunan Novia Nelza Nurliana Tarigan Pieter Octaviandy Yudhi Bastanta