Hasil Pencarian  ::  Simpan CSV :: Kembali

Abstrak :
ABSTRAK

Perkembangan Internet Of Things (IoT) pada era saat ini sudah semakin berkembang. Jumlah IoT device sudah mencapai 31 miliar perangkat yang tersebar di dunia. IoT adalah suatu objek yang memiliki kemampuan untuk mengirimkan data melalui jaringan tanpa memerlukan interaksi manusia ke manusia atau manusia ke computer. Dalam perkembangannya, dibutuhkannya teknologi Machine Learning untuk mendukung fitur-fitur yang ada pada IoT device, seperti prediksi konsumsi energi pada perangkat IoT. Machine learning diperlukan untuk mempelajari behaviour yang ada pada mesin yang diterjemahkan menjadi kondisi atau kata kata lain yang mencerminkan behaviour tersebut. Dalam implementasinya membutuhkan neural network yang didalamnya terdapat memory untuk mengingat behaviour tersebut sehingga proses learning dari alat menjadi cepat atau disebut dengan Recurrent Neural Network (RNN). Dengan tujuan dapat mengetahui dan memprediksi dari suatu nilai agar dapat memperkirakan besar konsumsi energi yang berakibat pada kenaikkan penggunaan listrik perkapita dalam negeri. Salah satu arsitektur dari RNN yaitu LSTM dapat digunakan untuk menjawab permasalahan. Data yang digunakan berasal dari sebuah alat dispenser. Hasil pengujian LSTM mencapai kategori baik dengan mendapatkan RMSE sebesar 3.9265.

---

ABSTRACT
The development of the Internet of Things (IoT) has increasingly developed many features. The number of IoT devices has reached 31 billion devices spread across the world. IoT is an object that has the ability to transfer data over a network without requiring human-to-human or human interaction to a computer. In its development, Machine Learning technology is needed to support features that exist on IoT devices, such as power predictions on IoT devices. Machine learning is needed to study the behaviors that exist on the machine which are translated into conditions or other words that reflect that behavior. For the implementation requires a neural network in which there is memory to remember the behavior so that the learning process of the tool becomes fast or called Recurrent Neural Network (RNN). The purpose of this research is to predict values from consumption energy, which related to average energy consumption on current country. One of the architectures of the RNN, namely LSTM can be used to answer the problem. The data comes from a smart dispenser. The LSTM test results reached a good category by getting RMSE of 3.9265.

Abstrak :
This book offers a new philosophical interpretation of classical mechanics and the special theory of relativity. According to Belkind?s view the role of physical theory is to describe the motions of the parts of a physical system in relation to the motions of the whole. This approach provides a new perspective into the foundations of physical theory, where motions of parts and wholes of physical systems are taken to be fundamental, prior to spacetime, material properties and laws of motion.

Abstrak :
Buku komik ini bercerita ttg rumpun ilmiah yaitu energi/tenaga ut listrik, penerangan, komputer, telepon, gas, pelayanan air dll. Bahan bakar fosil dipakai ut membuat barang-barang kebutuhan plastik. Maka anak-anak akan mengetahui akan manfaat, setelah membaca buku ini anak-anak kita menjadi kaya akan ilmu pengetahuan. Apakah energi itu? Dari mana sumber energi yg kita gunakan?

Abstrak :
This volume represents the perspectives of scholars from across Asia, looking at diverse aspects of energy security through a non-traditional security lens. The issues covered include environmental and socioeconomic impacts, the role of the market, the role of civil society, energy sustainability and policy trends in the ASEAN region.

Abstrak :
This book includes studies quantifying the role of energy in socioeconomic development, analysis of the interplay between supranational and national institutions in policy implementation, the energy implications of demographic trends such as urbanisation, and exploration of supply-side issues such as the potential role of nuclear energy and ‘cleaning’ fossil fuel energy generation through carbon capture.