3 research outputs found
Pengembangan Bahan Ajar Digital Pembangkit Listrik Tenaga Air untuk Matakuliah Pembangkit Tenaga Listrik di Jurusan Teknik Elektro Fakultas Teknik Universitas Negeri Malang
No full textABSTRAK Aziz, Faiz, Syaikhoni. 2017. Pengembangan Bahan Ajar Digital Pembangkit Listrik Tenaga Air untuk Matakuliah Pembangkit Tenaga Listrik di Jurusan Teknik Elektro Fakultas Teknik Universitas Negeri Malang. Skripsi. Jurusan Teknik Elektro, FakultasTeknik. UniversitasNegeri Malang.Pembimbing: (1) A. N. Afandi, S.T.,M.T.,Ph.D. (2) Drs. SlametWibawanto, M.T. Kata Kunci:Bahan Ajar Digital, Pembangkit Listrik Tenaga Air Pembangkit Listrik Tenaga Air merupakan jenis-jenis pembangkit yang dibahas pada matakuliah Pembangkit Tenaga Listrik Pendidikan Teknik Elektro Universitas Negeri Malang. Berdasarkan observasi pada dosen pengampu dan mahasiswa yang sedang menempuh matakuliah Pembangkit Tenaga Listrik, diketahui bahan ajar yang digunakan dalam pembelajaran yaitu power point. Bahan ajar yang memanfaatkan laptop atau smartphone lebih dikenal dengan sebutan bahan ajar digital. Keunggulan dari bahan ajar digital yaitu dapat menampilkan unsur media secara lengkap (multimedia) seperti gambar, animasi, audio, video, grafik sehingga dapat meningkatkan daya tarik dan minat mahasiswa untuk mempelajari materi dalam bahan ajar tersebut. Tujuan penelitian dan pengembangan ini adalah: (1) merancang bahan ajar digital Pembangkit Listrik Tenaga Air untuk matakuliah Pembangkit Tenaga Listrik di Jurusan Teknik Elektro Fakultas Teknik Universitas Negeri Malang, (2) mengembangkan bahan ajar digital Pembangkit Listrik Tenaga Air untuk matakuliah Pembangkit Tenaga Listrik di JurusanTeknik Elektro Fakultas Teknik Universitas Negeri Malang, dan(3) menguji kelayakan bahan ajar digital Pembangkit Listrik Tenaga Air untuk matakuliah Pembangkit Tenaga Listrik di Jurusan Teknik Elektro Fakultas Teknik Universitas Negeri Malang. Model pengembangan yang digunakan adalah model pengembangan ADDIE.Tahap-tahap yang dilakukan antara lain: (1) analyze, (2) design, (3) develop, (4)implement, dan (5) evaluate. Hasil dari pengembangan bahan ajar ini berupa modul dan video pembelajaran yang telah divalidasi oleh ahli serta telah diujicobakan kepada mahasiswa melalui uji kelayakan. Kelayakan bahan ajar diketahui berdasarkan hasil angket.Hasil uji validasi modul oleh ahli memperoleh persentase sebesar 90.73% (sangat valid), hasil uji coba kelompok kecil memperoleh persentase sebesar 93.25% (sangat layak), dan hasil uji coba lapangan memperoleh persentase sebesar 87.98% (sangat layak). Sedangkan hasil validasi video pembelajaran oleh ahli memperoleh persenta sesebesar 93.22% (sangat valid), hasil uji coba kelompok kecil memperoleh persentase sebesar 92.27% (sangat layak), dan hasil uji coba lapangan memperoleh persentase sebesar 88.6% (sangat layak). Berdasarkan skor tersebut, modul dan video pembelajaran dinyatakan valid dan layak digunakan dalam pembelajaran
Smart guided missile using accelerometer and gyroscope based on backpropagation neural network method for optimal control output feedback
No full textAs a maritime country with a large area, besides the need to defend itself with the military, it also needs to protect itself with aerospace technology that can be controlled automatically. This research aims to develop an air defense system that can control guided missiles automatically with high accuracy. The right method can provide a high level of accuracy in controlling missiles to the targeted object. With the backpropagation neural network method for optimal control output feedback, it can process information data from the radar to control missile’s movement with a high degree of accuracy. The controller uses optimal control output feedback, which is equipped with a lock system and utilizes an accelerometer that can detect the slope of the missile and a gyroscope that can detect the slope between the target direction of the missile to follow the target, control the position, and direction of the missile. The target speed of movement can be easily identified and followed by the missile through the lock system. Sampling data comes from signals generated by radars located in defense areas and from missiles. Each part’s data processing speed is calculated using a fast algorithm that is reliable and has a level of accuracy and fast processing. Data processing impacts on the accuracy of missile movements on any change in the position and motion of targets and target speed. Improved maneuvering accuracy in the first training system can detect 1000 files with a load of 273, while in the last training, the system can detect 1000 files without a load period. So the missile can be guided to hit the target without obstacles when maneuvering
Lux and current analysis on lab-scale smart grid system using Mamdani fuzzy logic controller
No full textThe increasing need for electrical energy requires suppliers to innovate in developing electric distribution systems that are better in terms of quality and affordability. In its development, it is necessary to have a control that can combine the electricity network from renewable energy and the main network through voltage back-up or synchronization automatically. The purpose of this research is to create an innovative lux and current analysis on a lab-scale smart grid system using a fuzzy logic controller to control the main network, solar panel network and generator network to supply each other with lab-scale electrical energy. In the control, Mamdani fuzzy logic controller method is used as the basis for determining the smart grid system control problem solving by adjusting the current conditions on the main network and the light intensity conditions on the LDR sensor. Current conditions are classified in three conditions namely safe, warning, and trip. Meanwhile, the light intensity conditions are classified into three conditions namely dark, cloudy and bright. From the test results, the utility grid (PLN) is at active conditions when the load current is 0.4 A (safe) and light intensity is 1,167 Lux (dark). Then the PLN + PV condition is active when the load current is 1.37 (warning) and the light intensity is 8,680 lux (bright). Finally, the generator condition is active when the load current is 1.6 (trip) and the light intensity is 8,680 (bright). Based on the test results, it is known that the system can work to determine which source is more efficient based on the parameters obtained
