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The new challenge faced in carrying out IPA learning in elementary school, especially in SDN 01 Kubang Siamang Bunyi is to increase student activeness and involvement. The aim of this research is to improve student liveliness and engagement through Funscience methods in IPA learning. Fun science is a method by using simple experiments that are interestingly packaged and cause fun effects for students in IPA learning. This type of research is descriptive, involving student respondents in SDN 01 Kubang Siamang Bunyi Regency 50 cities of West Sumatra Province. Data collection instruments in the form of observation sheets and interviews. Data is analyzed descriptively. The results of the data analysis showed that the application of Funscience methods improved the activeness of students in IPA learning in SDN 01 Kubang Siamang Bunyi Kenagarian Kubang Regency 50 City. Funscience can be one of the alternative methods that can be applied in IPA learning to improve and improve the quality of learning. Funscience is a method of learning that has been adapted to the characteristics of students in elementary school.

Deposisi lapisan TiO2 didoping Cu telah berhasil ditumbuhkan dengan menggunakan metoda Liquid Phase Deposition (LPD). Lapisan TiO2-Cu dibuat dengan menggunakan material Ammonium hexafluorotitanate ((NH4)2TiF6), Copper (II) Nitrate hydrate (Cu(NO3)2·xH2O), dan Hexamethylen tetramine (C6H12N4). Dalam penelitian ini dilakukan variasi lama penumbuhan lapisan yaitu 3 jam, 5 jam, 7 jam dan 10 jam. Lapisan TiO2-Cu dikarakterisasi menggunakan Spektrometri Ultraviolet-Visible (UV-Vis) untuk menentukan energi gap melalui spektra difusi reflektansi. Hasil energi gap yang diperoleh pada lapisan TiO2-Cu dengan variasi lama penumbuhan yaitu antara 3,26-3,30 eV.Kata kunci: Titanium dioksida (TiO2), Liquid Phase Deposition (LPD) dan doping

This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.