Penelitian ini menerapkan salah satu jenis mikrokomputer, yaitu Raspberry Pi Tipe B yang digunakan sebagai perangkat pemroses inti sebuah sistem pengelola konten berbasis web pada digital signage untuk kepentingan akademik. Sistem pengelola konten yang dibangun pada penelitian ini menggunakan framewok berbasis Drupal 7 dan sistem operasi Raspbian Wheezy. Berdasarkan penelitian sebelumnya, penggunaan digital signage sebagai media untuk menyebarkan informasi memiliki beberapa keunggulan dibandingkan dengan media konvensional, seperti media cetak. Digital signage mampu menampilkan informasi yang lebih atrakif dan dapat menarik pengunjung lebih banyak. Kelebihan yang dimiliki oleh sebuah mikrokomputer, seperti harga yang relatif terjangkau, ukurannya yang kecil (seukuran kartu ATM), dan konsumsi daya yang rendah (3,5 Watt) bisa dipadukan dengan penggunaan digital signage dalam hal pemilihan perangkat pemroses inti. Dari hasil pengamatan yang dilakukan peneliti selama eksperimen, mikrokomputer mampu menjalankan antarmuka berbasis web dinamis yang menampilkan konten multimedia seperti gambar dan efek transisi yang atraktif dengan baik untuk keperluan penyampaian informasi akademik di Laboratorium Robotics and Embedded Systems STMIK STIKOM Bali.This study implements a kind of microcomputers which is called Raspberry Pi type B as a core processing device of digital signage for academic purposes. The content management systems constructed in this study used Drupal framewok and Raspbian Wheezy as operating systems. Compared to the conventional method (printed brochures, etc.), the use of microcomputers for displaying information offer some advantages. According to the previous work, it is concluded that digital signage can be more attractively invite the audiences. A relatively affordable price, small size (at the size of an ATM card), and low power consumption (approximately 3.5 Watts), make the microcomputer as an ideal choice for digital signage. According to the obtained results, the microcomputer is capable to run a dynamic web-based interface that displays a multimedia content such as images and transition effects etc. for academic information systems implemented at the Laboratory of Robotics and Embedded Systems STMIK STIKOM Bali.
Electric energy consumption in a residential household is one of the key factors that affect the overall national electricity demand. Household appliances are one of the most electricity consumers in a residential household. Therefore, it is crucial to make a proper prediction for the electricity consumption of these appliances. This research implemented feature engineering technique and long short-term memory (LSTM) as a model predictor. Principal component analysis (PCA) was implemented as a feature extractor by reducing the final 62 features to 25 principal components for the LSTM inputs. Based on the experiments, the two-layered LSTM model (composed by 25 and 20 neurons for the first and second later respectively) with lookback number of 3 found to give the best performance with the error rates of 62.013 and 26.982 for RMSE and MAE, respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.