Design and Simulation of Analog Modulation with the Infinite Impulse Response Method

Penelitian ini berdasarkan salah satu roadmap prodi Teknik Telekomunikasi Jurusan Teknik Elektro Politeknik Negeri Medan. Materi dasar dalampraktikum Dasar Telekomunikasi I bersifat dasar suatu sinyal. Sinyal yang diberikan ke sistem atau rangkaian, keluarannya diukur oleh osiloskop dan/atau spectrum analyzer. Rangkaian dasar sistem analog dalam telekomunikasi meliputi filter, penguat, osilator dan mixer. Menurut penelitian rancangan filter terdahulu yang telah dilakukan, kemudian dilanjutkan kepada penelitian karakteristik penguat dan mixer analog pada modul ED-2950P. Penguat dan mixer dikelompokkan menurut RF, IF dan daya.,Rangkaian dibangun berbasis IC 555, μ741 atau LM348 terbaru dan didukung generator sinyal, 10 MHz, 50 MHz. bentuk gelombang dan analisa respon frekuensi menggunakan Osiloskop digital, 100 MHz. Rancang bangun rangkaian tersebut diiringi dengan program simulasi menggunakan MATLAB dengan metode IIR, keduanya akan dibandingkan hasilnya. Hasilnya cukup memadai dengan faktor error bervariasi rata-rata 0,0964 Vpp sampai 0,236 Vpp dan standar deviasi dari 0,070 Vpp sampai 0,0340 Vpp untuk tegangan masukan 1 Vpp. Mixer terdiri dari modulator AM

Rancang Bangun Water Level Detection dengan Sensor Elektronik Berbasis Fuzzy Logic

Berlaku selama hidup Pencipta dan terus berlangsung selama 70 (tujuh puluh) tahun setelah Pencipta meninggal dunia, terhitung mulai tanggal 1 Januari tahun berikutnya

Applications For Detecting The Rate Of Fruit In Mangrove Plants

Mangrove plants are one of the plants that really help aquatic ecosystems between the sea, coast, and land. Mangrove plants provide many ecological, social, and economic benefits. In Indonesia, mangrove plants have 202 species with the same anatomy as other plants in general, consisting of roots, fruits, stems and leaves. Nowadays, the location of mangrove plants in Indonesia has experienced the fastest damage in the world due to conversion to ponds, settlements, industry and plantations. One of the efforts to restore aesthetic value and restore the ecological function of mangrove forest areas is rehabilitation using mangrove fruit. In the rehabilitation process, farmers generally use the manual method with the naked eye to determine fruit ripeness on mangrove plants, so the resulting level of accuracy is not optimal. To overcome this problem, an application is needed that can facilitate farmers in determining fruit maturity in mangrove plants so that it can help determine the maturity level of mangrove fruit. The development of this application utilizes the Deep Learning method as well as the utilization of digital image processing techniques with Grayscaling, Adaptive Threshold, Sharpening and Smoothing techniques. The results of this study are an application that can detect the level of fruit maturity in mangrove plants with an accuracy of 99.11%. With this application, determining the maturity level of fruit on mangrove plants can be easily done

Smart Door System using Face Recognition Based on Raspberry Pi

A smart door system is a door with a smart digital lock system where someone can open the door or give permission to enter the house by authenticating the user. Basically, the technology used for smart door implementation uses a microcontroller as its controller and is combined with identification in the form of a password. The technology can be combined with other techniques, such as using facial recognition. This is done because data security using alphanumeric combination passwords is no longer used, so it is necessary to add security that is difficult to manipulate by certain people. The type of security offered is facial recognition biometric technology which has different characteristics. This study will design a smart door system that is built using Raspberry Pi-based facial recognition as a controller. The facial recognition algorithm will interact with the webcam and solenoid lock using the Raspberry Pi.Based on the results of the study, it was found that the smart door system with facial recognition can be done well and obtains an accuracy of 94%. The application of the smart door system proposed in this study is considered capable of increasing home security which can be controlled automatically using facial recognition. A smart door system is a door with a smart digital lock system where someone can open the door or give permission to enter the house by authenticating the user. Basically, the technology used for smart door implementation uses a microcontroller as its controller and is combined with identification in the form of a password. The technology can be combined with other techniques, such as using facial recognition. This is done because data security using alphanumeric combination passwords is no longer used, so it is necessary to add security that is difficult to manipulate by certain people. The type of security offered is facial recognition biometric technology which has different characteristics. This study will design a smart door system that is built using Raspberry Pi-based facial recognition as a controller. The facial recognition algorithm will interact with the webcam and solenoid lock using the Raspberry Pi.Based on the results of the study, it was found that the smart door system with facial recognition can be done well and obtains an accuracy of 94%. The application of the smart door system proposed in this study is considered capable of increasing home security which can be controlled automatically using facial recognition