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%.
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.
Kebutuhan jaringan internet LTE tidak hanya dibutuhkan oleh masyarakat perkotaan, tetapi juga masyarakat pedesaan. Kuantan Singingi adalah salah satu kabupaten yang terdapat di provinsi Riau, dimana daerah ini memiliki banyak potensi yang dapat dikembangkan, terutama pariwisata. Untuk mengembangkan potensi tersebut, Kuantan singingi membutuhkan jaringan internet dengan kualitas sinyal yang lebih baik. Untuk memenuhi kebutuhan jaringan internet dengan level sinyal yang lebih baik di Kuantan Singingi, maka pada penelitian ini telah dilakukan perancangan jaringan internet LTE di daerah Kuantan Singingi dengan menggunakan metode Coverage Dimensioning dengan jenis duplex FDD pada bandwidth 5 MHz. Perancangan dilakukan untuk perencanaan hingga 2020. Dari hasil perancangan yang telah dilakukan diperoleh bahwa Kuantan Singingi membutuhkan 54 eNodeB dengan jari-jari sel sebesar 6690 Meter. Selain itu diperoleh RSRP rata-rata -80dBm dengan cakupan area terluas mencapai 99,5%. Kemudian service area uplink mencapai 100% untuk modulasi 16QAM 3/5, sedangkan service area downlink mencapai 71% untuk modulasi QPSK 3/5.
Mobile Ad hoc Network (MANET) is a wireless network that contains a collection of nodes without infrastructure and interconnected to communicate. MANET works dynamically when a group of nodes moves spontaneously, so the network topology can change quickly and cannot be predicted. It causes changes in wireless network topology according to existing conditions. The node functions in determining the route to be selected. Ad Hoc networks have limited transmission range, so routing is needed to send data over the network. The problem with mobile nodes is that routing must provide a path when the node changes. The speed of a node obtaining information is affected by the routing protocol used in the network. Each routing protocol has different capabilities in network speed, so the discovery routing time for each routing is also different. The selected routing protocols are Ad Hoc On-Demand Distance Vector (AODV), Optimized Link-state (OLSR), and Zone Routing Protocol (ZRP). The study will conduct a comparative analysis of ad hoc network initialization speeds on AODV, OLSR, and ZRP routing protocols. The parameter tested is the speed of routing discovery. After the data is collected, an analysis is carried out by looking at the routing discovery speed of each routing protocol. The test results show that each of the routing protocols examined, the AODV routing protocol, has a faster routing discovery time than the OLSR and ZRP routing protocols.
Pekanbaru city is a large area, therefore traffic congestion often occurs due to the density of society’s vehicles. From this problem, it is needed a technology that can exchange information between vehicles. Information Technology that can involve many vehicles with special network types without dependence on an infrastructure is Ad Hoc Network. One type of this network is Vehicular Ad Hoc Network (VANET). VANET is a new concept in enabling communication between Vehicle to Vehicle (V2V). For efficient data packet delivery, VANET requires a routing protocol. In this research, for simulated and analyzed performance is used the Dynamic Source Routing (DSR) and Temporally Ordered Routing Algorithm (TORA) protocol. NS-2 is used to simulated a moved nodes, SUMO software is used to simulated real map of SKA Mall crossroad and parameter the quality of performance routing protocol DSR can determined by End to End Delay, Packet Delivery Ratio (PDR) and Routing Overhead (RO). This simulation uses scenario 100 nodes, 150 nodes, 200 nodes and 250 nodes. The simulation results with the scenario of changing the number of nodes, the DSR routing protocol produces better performance with an average of End to End Delay is 0.1066 s, average of PDR is 95.45% and average of RO is 1.0076. While the TORA routing protocol has an average of End to End Delay is 0.1163s, average of PDR is 93.49% and average of RO is 1.0801. And in the scenario of node speed changes, the TORA routing protocol produces better performance with an average of End to End Delay is 0.0861 s and average of PDR 97.37%. While the DSR routing protocol is better with an average of RO is 1.0076.
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