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All Journal Jurnal Teknik Mesin Rekayasa Mesin Prosiding Semnastek JURNAL INTEGRASI Jurnal Rekayasa Material, Manufaktur & Energi Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Journal of Dynamics Logista: Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal TEMAPELA JTM - Jurnal Teknik Mesin Jurnal Teknik Mesin Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Engine: Energi, Manufaktur, dan Material Jurnal Vokasi Jurnal Teknik Industri Terintegrasi (JUTIN) Jurnal Surya Teknika Jurnal Pengabdian dan Peningkatan Mutu Masyarakat (JANAYU) International Journal of Research in Community Services Teknika Sains : Jurnal Ilmu Teknik Journal of Energy, Material, and Instrumentation Technology Jurnal Teknik Mesin International Journal of Multidisciplinary Research and Literature (IJOMRAL) METAL : Jurnal Sistem Mekanik dan Termal Dulang: Jurnal Pengabdian Kepada Masyarakat Industrial Research Workshop and National Seminar Jurnal Andalas: Rekayasa dan Penerapan Teknologi International Journal of Innovation in Mechanical Engineering and Advanced Materials Proceeding Applied Business and Engineering Conference Jurnal Polimesin IJIMCE : International Journal of Innovation in Mechanical Construction and Energy IJIS Edu : Indonesian Journal of Integrated Science Education JAPEPAM, Jurnal Pengabdian kepada Masyarakat
Ruzita Sumiati
Politeknik Negeri Padang
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Journal : Rekayasa Mesin

 1 
PERANCANGAN MODIFIKASI MESIN BENDING ROTARY BAJA APLIKASI STAND POT BUNGA Ruzita Sumiati; Yuli Yetri; Fardinal Fardinal; Hamzah Putra
Jurnal Rekayasa Mesin Vol. 13 No. 2 (2022)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v13i2.1077

Abstract

Plants grown in pots continue to grow year after year. A flower pot support or flower pot stand is required to add aesthetic value and save space. The purpose of this research is to develop a steel rotary bending machine design for flower pot stand applications The method used is to calculate the rotational speed of the bending mall, design the transmission, calculate the power and capacity of the machine, calculate and analyze the frame strength, and produce machine design results. The design produces a mall bending rotation of 4.5 rpm with diameters of 100 mm, 120 mm, and 150 mm, an electric motor with a rotation speed of 2840 rpm and a power of 1 HP, 4 pulleys and 2 types A V-belts, a shaft diameter of 20 mm and a 6 x 6 mm key, UCP and UCF type bearings, conical gears with a reduction of 10:16, a gearbox with a reduction of 1:40. The required power is 146 watt (0,2 horsepower), and the engine capacity is 243 bends per hour. The engine frame has dimensions of 750 x 550 x 1000 mm and is made of AISI 1045 material. The highest stress that occurs in the frame is 45.55 N/mm2.
PERANCANGAN METODE MACHINE LEARNING BERBASIS WEB UNTUK PREDIKSI SIFAT MEKANIK ALUMINIUM Desmarita Leni; Arwizet K; Ruzita Sumiati; Haris Haris; Adriansyah Adriansyah
Jurnal Rekayasa Mesin Vol. 14 No. 2 (2023)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v14i2.1370

Abstract

The main objective of this research is to design a web-based machine learning model that can predict the mechanical properties of aluminum based on its chemical composition. By inputting nine variables of chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, and Si, the model is able to provide predictions for two output data, Yield Strength (YS) and Tensile Strength (TS). The research aims to understand the relationship between chemical composition and mechanical properties of aluminum, and to develop a tool that can be used to predict these properties with a high level of accuracy. Overall, the goal of this study is to enhance the understanding of the properties of aluminum and how it can be utilized in various applications. This study designs a web-based machine learning modeling to predict the mechanical properties of aluminum in the percentage of chemical composition, where the input data in the modeling consists of 9 variables of chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and has 2 output data consisting of Yield Strength (YS) and Tensile Strength (TS). The modeling machine learning is designed using the Python programming language and additional libraries such as Pandas, Numpy, Scikit-learn, and Streamlit. The modeling in this study uses three algorithms consisting of Decision Trees (DT), Random Forest (RF), and Artificial Neural Network (ANN). Each algorithm is optimized with the best search parameters, and where the RF algorithm has better performance than DT and JST. The best modeling uses the RF algorithm with optimal parameters of number of trees at 20 and maximum depth of 10, with MAE values of 11.44, RMSE of 14.282, and R of 0.93 for Yield Strength (YS) predictions, and for Tensile Strength (TS) predictions, MAE values are obtained. 21,669, RMSE 27,301, and R 0.871. 
Co-Authors ., Adriansyah ., Islahuddin ., Maimuzar ., Muchlisinalahuddin Abdul Aziz Abdullah, Irinah binti Ade Usra Berli Adriansyah Adriansyah Adriansyah Afifah Afifah, Afifah Afriyani, Sicilia Aggrivina Dwiharzandis Aidil Zamri Aidil Zamri Alfi, Rizki Andrew Kurniawan Vadreas Angelia, Nike Anissa Vivia Fidela Arifian, Naf'an Arwizet Arwizet, Arwizet Asmed Asmed Budiman, Dadi Bukhari Camim, Moh. Candra Mayana, Hendri Dandi Ilham Delffika Canra Desmarita Leni Doni Marzuki Efiandi, Nota Elvando andha elvaris manalu Fahri Reza Fahri Triharyono Fajar Pradana Fajri Arsyah, Ahmad Hasnul Fanni Sukma Fardinal Fardinal Fardinal, Fardinal Fathir Alqodri Fharel Abdillah Fharel Abdillah Fitri Adona Gani Pratama Genta Ramadeto Ghandy Junne Putra Gusriwandi Gusriwandi Hamdani, Rifki Hamdani, Rifqi Hamzah Putra Hanif Hanif Hanif Hanif Haris ., Haris Haris Haris Haris Haris, Haris Helga Yermadona Hendra . Ikbal Ilham, M. Irwan Irwan Jana Hafiza Kesuma, Dytchia Septi Khairul Amri Khairul Amri Khan, Sharif kusuma, Yuda Perdana Lega Putri Utami Lim, Hooi Peng M. Luthfi Artia Maimuzar Maimuzar Meiki Eru Putra Menhendry Moh. Chamim Muchlisinalahuddin Mulyadi Mulyadi . Mulyadi Mulyadi Nandi Pinto NARO, AULIA Nasirwan Nasirwan Nasrullah Nasrullah Nofriadi Nofriadi Nofriyandi, R Nofriyanti, Elsa Nota Effiandi Nusyirwan Nusyirwan Nusyirwan Peng, Lim Hooi Rahil Abde Andika Rahmi, Nurfitri Rajimar Suhal Hasibuan Hasibuan Rakiman Rakiman Rakiman Rakiman Rama, Hilfama Ridho Pratama Fajri Riezky Idvi Alfitra Rina Rina Rina Rina Rina, Rina Rino Sukma Riswan Riswan Rivanol Chadry Robby Novrizal Rudianto Rudianto Saputra M, Dendi Adi Seno, Abdi Sir Anderson Siska Angraini Rikosa Rikosa Suliono Suliono Tri Ego Wiranata Usra Berli , Ade Uyung Gatot Syafrawi Dinata Veny Selviyanty Verdian, Riza Yanziwar Yanziwar Yazmendra Rosa Yogi Kogama Yuda Perdana Kusuma Yuhefizar Yuhefizar Yuli Yetri Yuliarman Yuliarman Yuliarman Yuliarman Yusri Mura