Nowadays, digital image processing is not only used to recognize motionless objects, but also used to recognize motions objects on video. One use of moving object recognition on video is to detect motion, which implementation can be used on security cameras. Various methods used to detect motion have been developed so that in this research compared some motion detection methods, namely Background Substraction, Adaptive Motion Detection, Sobel, Frame Differences and Accumulative Differences Images (ADI). Each method has a different level of accuracy. In the background substraction method, the result obtained 86.1% accuracy in the room and 88.3% outdoors. In the sobel method the result of motion detection depends on the lighting conditions of the room being supervised. When the room is in bright condition, the accuracy of the system decreases and when the room is dark, the accuracy of the system increases with an accuracy of 80%. In the adaptive motion detection method, motion can be detected with a condition in camera visibility there is no object that is easy to move. In the frame difference method, testing on RBG image using average computation with threshold of 35 gives the best value. In the ADI method, the result of accuracy in motion detection reached 95.12%.

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Otomatisasi pemberi pakan ikan lele berbasis arduino ini bertujuan untuk merancang bangun alat pemberi makan ikan lele secara otomatis berdasarkan jadwal makan dan berat ikan berbasis arduino uno.Alat Pemberi pakan ini menggunakan RTC untuk pengatur waktu dan pengatur jadwal pakan ikan dilengkapi juga dengan motor DC sebagai penebar pakan. Tegangan operasional alat yang digunakan rangkaian kontrol membutuhkan tegangan 5V dan 12V. Berat pakan yang dikeluarkan berdasarkan berat ikan lele dan banyaknya jumlah ikan. Jumlah anakan ikan lele dalam penelitian ini dibuat bervariasi, yaitu 750 ekor, 1500 ekor dan 3000 ekor dengan panjang anakan 7 sampai 8 cm per ekor. Jadwal pemberi pakan yang telah dijadwalkan yaitu pada pukul 09.00 WIB untuk pagi, pukul 16.00 WIB untuk pemberian pakan siang dan 21.00 WIB untuk pemberian pakan malam.Dari hasil pengujian, alat dapat bekerja dengan baik sesuai jadwal yang ditentukan dengan berat pakan yang keluar 150 gr selama 5,04 detik untuk anakan sebanyak 750 ekor, 300 gr selama 8,36 detik untuk anakan sebanyak 1500 ekor dan 600 gr untuk anakan sebanyak 3000 ekor.

Tempe is made from fermented soybeans with the fungus Rhizopus Oligosporus. In the manufacture of tempe producers often experience failure. The main cause is the temperature and humidity of the room where the tempe production is not maintained. The absence of supporting devices for detecting temperature and humidity in the factory is an obstacle in the tempe fermentation process. Manufacturers can only estimate the temperature and humidity in the fermentation chamber. If the temperature is considered too hot, tempe producers will come to the factory and open the air vents so that the room temperature returns to normal. To increase tempe production and reduce the risk of production failure, it is necessary to design an automatic control and monitoring tool through the use of the Internet of Things (IoT). The tools used in this research are ESP8266, DHT22, Relay, Power Bank as a power source, fans, and lights. The results obtained from the test are that if the temperature and humidity are above or below the normal temperature (250C-320C), a notification will appear on the user's smartphone via the Blynk application. If the temperature is too hot, the fan will turn on automatically. If the temperature is too cold, the light will turn on. Monitoring data can also be viewed on the things peak website in graphic form.

Computer networks are one of the main parts in the telecommunications system. To support reliable network technology, a centralized network is needed so that network traffic can be managed more easily. Software-Defined Network (SDN) technology is a centralized network that provides a separation between control planes and data planes in different systems. This study discusses the optimization of network management at the University of Riau (UNRI) using SDN. Optimization is done by designing a UNRI computer network in the form of SDN then simulated using the Mininet. Quality of Service (QoS) analysis is performed from the measurement results using Wireshark. The network simulation results give a delay value of 0.506 ms, 0% packet loss, the throughput of 590,392 Mb / s and jitter of 0.093 ms. The SDN network provides better delay and jitter performance compared to conventional UNRI networks with a delay value of 13,874 ms, 0% packet loss, 635.1 Mb/s throughput and 2.6 ms jitter. UNRI's SDN network design is worth considering because it has better QoS values, delay, and jitter below ITU standards and conventional networks.