PENGARUH JENIS WELDING GROOVE TERHADAP KEKUATAN IMPACT PLAT BAJA ASTM A36 MENGGUNAKAN MESIN LAS SMAW
DOI:
https://doi.org/10.56127/juit.v4i1.1901Keywords:
SMAW Welding, ASTM A36, Charphy Impact TestAbstract
Currently, metal joining with welding process is increasingly used, both in building construction, piping, and machine construction. This is due to the many advantages obtained from joining by welding. The use of the right welding groove will also affect the welding results. The purpose of using welding groove is for the place to fill the welding material and can also strengthen the design of the metal joint. Welding groove plays an important role in improving the design and properties of the joint in the welding process. ASTM A36 welding using SMAW welding current 110 A is carried out with Groove variations, namely VGroove, Square Groove, and Bevel Groove and using E6013 electrodes with a diameter of 2.6 mm. The purpose of this study was to determine the strength of SMAW welding joints on ASTM A36 against the Charphy impact test. the required impact charphy effort results (W) are on V Groove of 73.519 J, Square Groove of 67.43 J, and Bevel Groove of 70.485 J. And produces an impact charphy value (K) on V Groove of 0.918 J / ????????2, on Square Groove of 0.842 J / ????????2 and on Bevel Groove of 0.881 J / ????????2. The impact charphy value on V Groove with SMAW welding current of 110 A on ASTM A36 carbon steel is the best with the required impact charphy results (W) namely on V Groove of 73.519 J and the impact charphy value (K) on V Groove of 0.918 J / ????????2.
References
M. Schorung, “Quick commerce: will the disruption of the food retail industry happen? Investigating the quick commerce supply chain and the impacts of dark stores,” 2023.
Simanungkalit, J. P. P. Naibaho, and A. De Kweldju, “Analisis Sentimen Berbasis Aspek Pada Ulasan Aplikasi Shopee Menggunakan Algoritma Naïve Bayes,” Jutisi J. Ilm. Tek. Inform. dan Sist. Inf., vol. 13, no. 1, p. 659, 2024.
D. A. Pradana and A. P. Wibowo, “Analisis Sentimen Ulasan Produk Sepatu Compass Di E- Commerce Tokopedia Menggunakan Algoritma Naive Bayes Classifier ( NBC ) Analisis Sentimen Ulasan Produk Sepatu Compass Di E- Commerce Tokopedia Menggunakan Algoritma Naive Bayes Classifier ( NBC ),” Inov. Pembang. – J. KELITBANGAN, vol. 12, no. 3, pp. 1–12, 2024.
K. Dewi, “Analisis Sentimen Ekspedisi Sicepat Dari Ulasan Google Play Mennggunakan Algoritma Naïve Bayes,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 2, pp. 796–805, 2022.
D. N. I. Huda and C. Prianto, “Analisis Sentimen Layanan Jasa Pengiriman Pada Ulasan Play Store: Systematic Literature Review,” J. Inform. dan Teknol. Komput., vol. 4, no. 2, pp. 87–98, 2023.
K. F. E. D. W. R. Permatasari, “Analisis Sentimen Berbasis Aspek Pada Ulasan Aplikasi Indrive Menggunakan Bidirectional Encoder Representations From Transformers (Bert),” J. Ilm. Wahana Pendidik., vol. 10, no. 20, pp. 105–115, 2024.
N. B. Sidauruk and N. Riza, “Sentimen Analisis Data Pengguna Terhadap KAI Access Systematic Literature Review,” J. Mhs. Tek. Inform., vol. 7, no. 2, pp. 1297–1303, 2023.
D. Jovanoski, V. Pachovski, and P. Nakov, “Sentiment analysis in twitter for Macedonian,” Int. Conf. Recent Adv. Nat. Lang. Process. RANLP, vol. 2015-January, pp. 249–257, 2015.
R. M. R. W. P. K. Atmaja and W. Yustanti, “Analisis Sentimen Customer Review Aplikasi Ruang Guru dengan Metode BERT (Bidirectional Encoder Representations from Transformers),” Jeisbi, vol. 02, no. 03, pp. 55–62, 2021.
M. Senjaya, A., & Achmad Benny, “Analisis Sentimen Aplikasi Vidio Berdasarkan Review Pada Google Play Store Menggunakan Metode Bidirectional Encoder Representations from Transformers (BERT),” 2022.
R. Kusnadi, Y. Yusuf, A. Andriantony, R. Ardian Yaputra, and M. Caintan, “Analisis Sentimen Terhadap Game Genshin Impact Menggunakan Bert,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 6, no. 2, pp. 122–129, 2021.
C. A. Putri, “Analisis Sentimen Review Film Berbahasa Inggris Dengan Pendekatan Bidirectional Encoder Representations from Transformers,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 6, no. 2, pp. 181–193, 2020.
L. Qadrini, H. Hikmah, and M. Megasari, “Oversampling, Undersampling, Smote SVM dan Random Forest pada Klasifikasi Penerima Bidikmisi Sejawa Timur Tahun 2017,” J. Comput. Syst. Informatics, vol. 3, no. 4, pp. 386–391, 2022.
V. V. Verianto, “Factors Influencing Segari E-Grocery’s Purchase Intention Moderated by Gender and Age,” J. Bus. Manag. Rev., vol. 4, no. 2, pp. 149–167, 2023.
B. Liu, “Sentiment Analysis: Mining Opinions, Sentiments, and Emotions,” in Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, vol. 11, no. 3, 2015, pp. 277–278.
M. F. Ramadhan, B. Siswoyo, and S. I. Cirebon, “Mengenal Model BERT dan Implementasinya untuk Analisis Sentimen Ulasan Game,” in Prosiding Seminar Nasional Sistem Informasi dan Teknologi (SISFOTEK) ke, 2024, pp. 395–398.
B. G. A. Ghodsi, Neural Network Compression and Knowledge Distillation : Tutorial and Survey. 2024.
T. A. S. Rohmah and W. Maharani, “Personality Detection on Twitter Social Media Using IndoBERT Method,” Build. Informatics, Technol. Sci., vol. 4, no. 2, pp. 448–453, 2022.
D. R. Wijaya and G. M. A. S. W. O. Vihikan, “Sentiment Analysis of Indonesian Citizens on Electric Vehicle Using FastText and BERT Method,” J. Inf. Syst. Informatics, vol. 6, no. 3, pp. 1360–1372, 2024.
