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"This book discusses the revolution of cycles and rhythms that is expected to take place in different branches of science and engineering in the 21st century, with a focus on communication and information processing. It presents high-quality papers in vibration sciences, rhythms and oscillations, neurosciences, mathematical sciences, and communication. It includes major topics in engineering and structural mechanics, computer sciences, biophysics and biomathematics, as well as other related fields. Offering valuable insights, it also inspires researchers to work in these fields. The papers included in this book were presented at the 1st International Conference on Engineering Vibration, Communication and Information Processing (ICoEVCI-2018), India."

"ABSTRAKPemantauan getaran vibration monitoring merupakan metode yang masih digunakan sampai sekarang bahkan selalu berkembang dari waktu kewaktu. Metode ini sangat dibutuhkan di industri karena dengan metode ini dapat mengurangi biaya jika terjadi kerusakan pada mesin di industri serta dengan metode ini perawatan pada mesin lebih baik. Pada penelitian ini, telah dikembangkan sebuah sistem untuk mendeteksi kerusakan komponen pada mesin pompa air diantaranya komponen kapasitor, bearing dan impeller. Kerusakan dideteksi dengan mengambil data getaran menggunakan akselerometer MEMS. Data keluaran akselerometer diakuisisi dengan menggunakan mikrokomputer Raspberry Pi kemudian data tersebut diubah dalam bentuk Relative Power Ratio RPR dimana data dalam bentuk ini akan dipelajari oleh Artificial Neural Network ANN dengan begitu kerusakan dapat dideteksi dimana hasil pengujian kerusakan akan dikirim melalui pesan text menggunakan modul GSM. Hasil pengujian dari penelitian ini, dengan pembacaan kerusakan selama 10 kali dalam 10 menit, semua kasus kecuali kasus kapasitor rusak menghasilkan 100 sedangkan untuk kasus kapasitor rusak menghasilkan 90 akurasi kerusakan. Pengujian dilakukan pada mesin pompa air Shimizu PS-128 BT dan dilakukan di Universitas Indonesia.

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ABSTRACTVibration monitoring is a method that is still used today even always evolving from time to time. This method is very needed in the industry because with this method can reduce the cost if there is damage to the machine in the industry as well as with this method of maintenance on the machine more better. In this research, a system has been developed to detect the damage of components in water pump machine such as capacitor, bearing and impeller . Damage detected by taking vibration data using MEMS accelerometer. The accelerometer output data is acquired using Raspberry Pi microcomputer then the data is converted in the form of Relative Power Ratio RPR where the data in this form will be studied by Artificial Neural Network ANN so detectable damage where the damage test result will be sent via text message using GSM module. Test results from this study, with a 10 times damage reading in 10 minutes, all cases except the case of defective capacitor yield 100 while for the case the defective capacitor yields 90 damage accuracy. The test was performed on the Shimizu PS 128 BT water pump machine and performed at the University of Indonesia."

"Kebutuhan energi untuk rumah tangga atau bangunan di Indonesia sedang tumbuh secara signifikan. Oleh karena itu, efisiensi pada energi pendinginan sangat dibutuhkan. Penelitian ini bertujuan untuk mengembangkan model Artificial Neural Network (ANN) yang dapat memprediksi jumlah konsumsi energi pendinginan untuk pengaturan yang berbeda dari variabel kontrol sistem pendingin VRF. Bangunan dimodelkan oleh perangkat lunak Sketchup dan sistem pendinginan dimodelkan dengan EnergyPlus. MATLAB digunakan untuk training dan testing model ANN. Untuk model testing, set data dikumpulkan melalui simulasi yang sudah divalidasi dengan pengukuran lapangan. Empat langkah yang dilakukan dalam proses training yaitu pengembangan model awal, pemilihan variabel input, optimasi model, dan evaluasi kinerja. Model yang telah dioptimalkan menunjukkan akurasi prediksi yang akurat, sehingga membuktikan potensinya untuk aplikasi dalam algoritma kontrol yang diharapkan dapat menciptakan lingkungan termal ruangan yang nyaman serta energi yang efisien. Hasil analisis TOPSIS menunjukkan penghematan daya listrik sistem VRF sebesar 26% dari daya listrik observasi.

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Energy needs for households or buildings in Indonesia are growing significantly. Therefore, efficiency in cooling energy is needed. This study aims to develop an Artificial Neural Network (ANN) model that can predict the amount of cooling energy consumption for different settings of the VRF cooling system control variable. The building is modeled by the Sketchup software and the cooling system is modeled by EnergyPlus. MATLAB is used for training and testing ANN models. For model testing, data sets are collected through simulations that have been validated with field measurements. The four steps involved in the training process are initial model development, selection of input variables, model optimization, and performance evaluation. The optimized model shows accurate prediction accuracy, thereby proving its potential for application in control algorithms that are expected to create a comfortable and energy efficient indoor thermal environment. The results of the TOPSIS analysis show that the VRF system's electrical power savings are 26% of the observed electrical power."

"The book is a collection of best selected research papers presented at 6th International Conference on Innovations in Electronics and Communication Engineering at Guru Nanak Institutions Hyderabad, India. The book presents works from researchers, technocrats and experts about latest technologies in electronic and communication engineering. The book covers various streams of communication engineering like signal processing, VLSI design, embedded systems, wireless communications, and electronics and communications in general. The authors have discussed the latest cutting edge technology and the volume will serve as a reference for young researchers."

"Peramalan beban listrik memegang peranan yang sangat penting bagi efisiensi dan kinerja dari PLN. Berbagai jenis metode dipakai untuk mendapatkan hasil peramalan beban yang akurat agar daya yang dikirimkan sesuai dengan kebutuhan listrik dari konsumen. Skripsi ini membahas peramalan beban jangka pendek satu minggu ke depan dengan menggunakan Jaringan Syaraf Tiruan (JST). Peramalan beban jangka pendek sangat dipengaruhi oleh faktor-faktor cuaca, yang dalam hal ini menjadi masukan JST, yaitu : Suhu, Kelembaban, Tekanan udara, dan Kecepatan angin. Data yang digunakan untuk pembelajaran adalah data sebenarnya sepanjang tahun 2011. Arsitektur yang digunakan adalah feed-forward dan algoritma yang dipakai adalah algoritma backpropagation. Berdasarkan hasil didapatkan nilai MAPE terbaik sebesar 1.8 % dan untuk 10 kali running sebesar 2.65 % sehingga berada di bawah ambang kesalahan peramalan.

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Electrical load forecasting has an important role for efficiency and performance from PLN.Various types of methods have been used to provide an accurate load forecasting on purpose that the transmitted power appropriates the demand of consumers. This research will discuss short term load forecasting using Artificial Neural Network (ANN). Short term load is influenced by weather factors, those will become input of ANN, i.e. : Temperature, Humidity, Pressure, Wind speed, Data used for study are actual data in 2011. Architecture used for this research is feed-forward and Algorithm that is used is backpropagation. The final result shows that the best MAPE is 1.8 % and for 2.65 % for 10 iterations which are below the forecasting error limit."

"This book presents select proceedings of the International Conference on Future Learning Aspects of Mechanical Engineering (FLAME 2018). The book covers mechanical design areas such as computational mechanics, finite element modeling, computer aided designing, tribology, fracture mechanics, and vibration. The book brings together different aspects of engineering design, and will be useful for researchers and professionals working in this field."