AbstrakJaringan saraf tiruan merupakan suatu ilmu yang terus berkembang pesat hingga saat ini. Jaringan saraf tiruan merupakan suatu ilmu komputasi yang didasarkan dan terinspirasi dari cara kerja sistem saraf manusia. Sama halnya dengan sistem saraf manusia, jaringan saraf tiruan bekerja melalui proses pembelajaran terhadap data-data yang sudah ada untuk memformulakan keluaran dari data-data baru. Jaringan saraf tiruan dengan metode backpropagation mampu melakukan peramalan untuk data nonlinear seperti bentuk data harian harga saham. Salah satu algoritma inisialisasi bobot yang dapat meningkatkan waktu eksekusi adalah nguyen-widrow. Pada penelitian ini akan dilakukan implementasi metode backpropagation dengan inisialisasi bobot nguyen widrow untuk meramalkan harga saham. Proses implementasi melalui 3 tahapan, yaitu preprosesing data, pelatihan jaringan, dan pengujian jaringan. Hasil dari penelitian ini menunjukkan bahwa pelatihan jaringan saraf tiruan dengan jumlah dataset yang banyak membutuhkan perhitungan yang kompleks, sehingga jaringan saraf tiruan dengan arsitektur jaringan yang sederhana kurang efektif dan dapat terjebak pada titik lokal minimum. Hasil peramalan untuk harga close saham BBCA.JK memiliki nilai MAPE 0,85% dan untuk harga close saham AALI.JK memiliki nilai MAPE sebesar 1,84%. AbstractArtificial neural network is a hot topic and invite a lot of admiration in the last decade. Artificial Neural Network is one of the artificial representations of the humans brain who always try to simulate the learning process of the humans brain. Artificial neural network with backpropagation method is able to forecast nonlinear data such as daily data form stock price. One of the weight initialization algorithms that can be increase the execution time is nguyen-widrow. In this research will be implemented backpropagation method with nguyen widrow weight initialization to forecast stock prices. The process of implementation through 3 stages, that is preprosesing data, training, and testing or simulate. The results of this research indicate that the training of artificial neural networks with many datasets required a complex calculations, so the artificial neural network with simple architectures is less effective and can get stuck at minimum local points. The results forecasting for the close price of BBCA.JK have a MAPE value 0.85% and for the close price of AALI.JK have 1.84% of MAPE value..

This study examines the potential of solar energy in the urban environment with a case study in Semarang, Indonesia by analyzing the intensity of solar radiation and the residential rooftop area. The study aims to obtain a quantitative description of the potential for electricity production from rooftop solar photovoltaic systems in residential areas and estimate the mitigation potential of CO2. The estimation method has adopted the hierarchies assessment: estimation of physical, geographic, and technical potential. This study shows the residential roof area spread over 16 districts in the city of Semarang is 412,987.50 m 2 to 2,083,387 m 2 has the average potential to of solar energy every year of 44,051 -222,222 MWh/year. Total the low-carbon electricity is equivalent to 40.87% of the total electricity consumption in 2018 at Semarang City and reduce 1,394 tonCO2 in a year. Potential electricity production is proposed to set rules for the future empowerment of solar energy and analyze the potential at different time levels, such as monthly, weekly and daily.

Smart Micro Grid in household areas aims to meet electricity needs through the integration between state power plant with renewable energy sources so that the electricity used does not depend entirely on state utility. Smart Micro Grid also enables the availability of energy management services supported by Machine Learning (ML) technology, Big Data, Artificial Intelligence (AI), Internet of Things (IoT) and smart sensors so that consumer use of electricity is more efficient. To improve energy management services and distribution of renewable energy sources, new innovations in ML technology are needed to produce accurate learning models that can be used in the energy analysis process, such as monitoring, prediction, forecasting, scheduling and decision-making. However, the complexity of the problems in the smart grid system, which includes uncertainty and non-linearity, affects the more complex the energy data structure generated. Therefore, the simple ML method will not be able to perform the Learning process because it is limited to simple raw data processing. Therefore, the Deep Learning (DL) method can be used as a Learning method on data that has a complex and large structure. In this paper, Deep Neural Network (DNN) method will be developed using Long Short-Term Memory (LSTM) as a Learning model to provide Future Accurate Prediction (FAP) on electricity use and on renewable energy plants. Prediction test using Confusion Matrix accuracy value and RMSE error value

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