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Hydroponics is the cultivation of plants by utilizing water without using soil by emphasizing the fulfillment of nutritional needs for plants. Deep Flow Technic (DFT) is a type of hydroponics that implements a continuous flow of nutrients and there is a pool of half of the diameter of the pipe that inundates the roots of the plant. A common obstacle experienced by DFT is the lack of maintenance of plant growth elements such as water circulation, light intensity, temperature, humidity and pH of the water which causes these plants not to grow optimally. Then it is necessary to monitor and control the circulation of water on DFT-based IoT hydroponics to anticipate changes in plant growth elements. Data on plant growth elements are acquired by sensors integrated with Raspberry Pi. In the monitoring process using the website will display data on plant growth elements in the form of pH, temperature, humidity and water level in the hydroponic reservoir. Temperature and humidity are used as water circulation control parameters that are processed using the Fuzzy Sugeno Method. From the results of the tests that have been carried out, the system can monitor plant growth elements displayed on the website in real time and control water circulation automatically. The system applied in the hydroponics of mustard greens also produces significant growth in leaf number and plant height.

<p><strong>Abstrak</strong><em><br /></em></p><p><em>Internet of Things</em> merupakan perkembangan teknologi berbasis internet masa kini yang memiliki konsep untuk memperluas manfaat yang benda yang tersambung dengan koneksi internet secara terus menerus. Sebagai contoh benda elektronik, salah satunya adalah Raspberry Pi. Teknologi ini memiliki kemampuan memberikan informasi secara otomatis dan <em>real time</em>. Salah satu pemanfaatan perkembangan teknologi ini di bidang perikanan adalah sistem pemantauan air kolam. Pada prakteknya, para pembudidaya ikan lele masih melakukan pemantauan tersebut secara konvensional yaitu dengan cara mendatangi kolam ikan. Hal ini berpengaruh terhadap efisiensi waktu dan keefektifan kerja pembudidayaan ikan.<strong></strong></p><p>Pada penelitian ini dikembangkan alat yang berfungsi untuk membantu memantau dan mengontrol kualitas air kolam ikan lele berbasis <em>Internet of Things</em>. Piranti yang diperlukan adalah sensor keasaman (pH), sensor suhu dan sebuah relay untuk mengatur aerator oksigen air. Data dari sensor-sensor tersebut direkam oleh Raspberry Pi untuk kemudian diolah menjadi informasi sesuai kebutuhan pengguna melalui perantara internet secara otomatis. Selanjutnya data-data tersebut dapat ditampilkan dengan berbagai macam platform, salah satunya dengan model <em>mobile web</em>.  <strong></strong></p><p>Hasil uji menunjukan bahwa pengembangan teknologi <em>Internet of Things</em>  pada sistem ini dapat membantu pembudidaya untuk melakukan pemantauan terhadap kualitas air secara otomatis. Sistem otomasi yang dikembangkan menjanjikan peningkatan keberhasilan dalam pembudidayaan ikan lele.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>For recent years, the Internet of Things becomes the topic interest of improvement based on technologies that have the concept of extending the benefits of an object that is connected to an internet constantly. This technology has the ability to provide information automatically and real time. One of expansion in the field of fishery is the water ponds monitoring system. In the fact, the catfish farmers are still doing conventional monitoring by coming to the fish pond. This could affects the efficiency of time and effectiveness of fish cultivation work.</em></p><p><em>In this research, the systems that can monitor and control the quality of catfish water ponds based on the Internet of Things is proposed. The necessary tools are acidity sensor (pH), temperature sensor and a relay to adjust water oxygen aerator. The data sensors have been recorded by Raspberry Pi that processed into information according to user needs through internet automatically. Furthermore, these data have been displayed with a variety of platforms, one with a mobile web model.</em></p><p><em>The results shows that the system based on Internet of Things technology can monitor the water quality automatically. The automation system promises the productivity of catfish farming.</em></p>

A cart inverted pendulum is an under actuated system that highly unstable and nonlinear. Therefore, it makes a good problem example which attracts control engineers to validate the developed control algorithms. In this paper, an augmented PID control algorithm is proposed to stabilise a cart inverted pendulum at the desired state. The derivation of a mathematical model of the cart inverted pendulum using Lagrange's equation is discussed in detail. The system dynamics is illustrated to understand better the behaviour of the system. A simulation program has been developed to verify the performance of the proposed control algorithm. The system dynamic behaviours with respect to the variation of the controller parameters are analysed and discussed. Controllers parameters are expressed into two PID gain sets which associated with 2 dynamic states: the cart position (ϰ) and the pendulum angle (θ). It can be concluded from the simulation result that the proposed control algorithm can perform well where acceptable steady errors can be achieved. The best response from the cart inverted pendulum system has been obtained with the value of kPX 190, kDX 50, kIX 5, kPθ 140, kDθ 5, and kIθ 25.

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A Cart Inverted Pendulum System is an unstable, nonlinear and underactuated system. This makes a cart inverted pendulum system used as a benchmark for testing many control method. A cart must occupy the desired position and the angle of the pendulum must be in an equilibrium point. System modeling of a cart inverted pendulum is important for controlling this system, but modeling using assumptions from state-feedback control is not completely valid. To minimize unmeasured state variables, state estimators need to be designed. In this paper, the state estimator is designed to complete the state-feedback control to control the cart inverted pendulum system. The mathematical model of the cart inverted pendulum system is obtained by using the Lagrange equation which is then changed in the state space form. Mathematical models of motors and mechanical transmissions are also included in the cart inverted pendulum system modeling so that it can reduce errors in a real-time application. The state gain control parameter is obtained by selecting the weighting matrix in the Linear Quadratic Regulator (LQR) method, then added with the Leuenberger observer gain that obtained by the pole placement method on the state estimator. Simulation is done to determine the system performance. The simulation results show that the proposed method can stabilize the cart inverted pendulum system on the cart position and the desired pendulum angle.

Sardinella lemuru is a dominant small pelagic fish (80-90%) caught by purse seiner in Bali Strait, while the remaining 10-20% consist Decapterus spp., Euthynus affinis, and others. This composition typically varies seasonally, whereas Southeast monsoon season was dominated by S. lemuru, while Northwest monsoon season replaced by Decapterus spp. and Euthynus affinis. Fishing trend in the last 14 years indicated regime shift with the shifting in species composition by a seasonal into the inter-annual due to global climate change, such as El Niño and La Niña. 2006 was indicated a cold period of water temperature, which is triggered by the El Nino and positive Indian Ocean Dipole (pIOD). In this cold period, the S. lemuru reached peak of fishing, otherwise this fish disappear when warm period (strong La Niña) in 2010. When S. lemuru disappeared during warm period, it was substituted by Decapterus spp. Furthermore, as predatory fish of both small pelagic fishes, Euthynnus affinis always appear throughout the year. Understanding the species composition trend from seasonal to longer period is important for better strategy to manage fisheries of Bali Bali Strait in climate change era.

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