4 research outputs found
Pengaruh Penggunaan Metode Pembelajaran Induktif Terhadap Hasil Belajar Mata Pelajaran Pemeliharaan Sistem Pengapian Elektronik Siswa Kelas XII SMK Negeri 1 Udanawu Blitar
No full textABSTRAK Mahfudianto, Fuad. 2015. Pengaruh Penggunaan Model Pembelajaran Induktif terhadap Hasil Belajar Pemeliharaan Sistem Pengapian Elektronik Siswa Kelas XII SMK Negeri 1 Udanawu Blitar. Skripsi, Progam Studi Pendidikan Teknik Otomotif, Jurusan Teknik Mesin, Fakultas Teknik Universitas Negeri Malang, Pembimbing: (I) Dr. Syarif Suhartadi, M.Pd., (II) Drs. Ir. Eko Edi Purwanto, S.E,. M.Pd., M.M. Kata Kunci:Model Pembelajaran Induktif, Hasil Belajar Model pembelajaran yang baik akan menciptakan kondisi kelas yang kondusif, sehingga pada akhirnya akan memberi efek yang berbeda terhadap hasil belajar siswa. Namun pada praktiknya masih banyak guru yang kurang memperhatikan penggunaan model pembelajaran secara tepat. Oleh karena itu,dalam penelitian ini diterapkan sebuah model pembelajaran induktif sebagai alternatif untuk mengurangi masalah tersebut. Tujuan dari penelitian ini adalah untuk mengetahui perbedaan hasil belajar antara kelompok siswa yang menggunakan model pembelajaran induktif dan kelompok siswa yang menggunakan metode pembelajaran ceramah pada mata pelajaran pemeliharaan sistem pengapian elektronik kelas XII di SMK Negeri 1 Udanawu Blitar. Penelitian ini merupakan penelitian eksperimen semu dengan pola posttest only control design.Penetapan subyek penelitian dilakukan dengan cluster random sampling. Teknik analisis data dilakukan dengan uji t (Independent sample test) pada taraf signifikansi 5%. Hasil penelitian menunujukanada perbedaan hasil belajar antara kelompok siswa yang menggunakan model pembelajaran induktif dan kelompok siswa yang menggunakanmetode pembelajaran ceramah pada mata pelajaran pemeliharaan sistem pengapian elektronik kelas XII di SMK Negeri 1 Udanawu Blitar (Probabilitas 0,00
Estimation of Contact Tip to Work Distance (CTWD) using Artificial Neural Network (ANN) in GMAW
Get PDFA method for optimizing monitoring by using Artificial Neural Network (ANN) technique was proposed based on instability of arc voltage signal and welding current signal of solid wire electrode (GMAW). This technique is not only for effective process modeling, but also to illustrate the correlation between the input and output parameters responses. The algorithms of monitoring were developed in time domain by carrying out the Moving Average (M.A) and Root Mean Square (RMS) based on the welding experiment parameters such as travel speed, thickness of specimen, feeding speed, and wire electrode diameter to detect and estimate with a satisfactory sample size. Experiment data was divided into three subsets: train (70%), validation (15%), and test (15%). Error back-propagation of Levenberg-Marquardt algorithm was used to train for this algorithm. The proposed algorithms on this paper were used to estimate the variety the Contact Tip to Work Distance (CTWD) through Mean Square Error (MSE). Based on the results, the algorithms have shown that be able to detect changes in CTWD automatically and real time with takes 0.147 seconds (MSE 0.0087)
Estimation of Contact Tip to Work Distance (CTWD) using Artificial Neural Network (ANN) in GMAW
No full textA method for optimizing monitoring by using Artificial Neural Network (ANN) technique was proposed based on instability of arc voltage signal and welding current signal of solid wire electrode (GMAW). This technique is not only for effective process modeling, but also to illustrate the correlation between the input and output parameters responses. The algorithms of monitoring were developed in time domain by carrying out the Moving Average (M.A) and Root Mean Square (RMS) based on the welding experiment parameters such as travel speed, thickness of specimen, feeding speed, and wire electrode diameter to detect and estimate with a satisfactory sample size. Experiment data was divided into three subsets: train (70%), validation (15%), and test (15%). Error back-propagation of Levenberg-Marquardt algorithm was used to train for this algorithm. The proposed algorithms on this paper were used to estimate the variety the Contact Tip to Work Distance (CTWD) through Mean Square Error (MSE). Based on the results, the algorithms have shown that be able to detect changes in CTWD automatically and real time with takes 0.147 seconds (MSE 0.0087)
