Permasalahan yang sering terjadi sekarang ini adalah tekanan air yang menurun pada saluran air saat beberapa kran dibuka sehingga hal itu menyebabkan pengisian air yang cukup lama. Agar tekanan pada pipa konstan, maka solusi dari masalah ini yaitu diperlukan suatu sistem pompa air untuk mengatasi tekanan air yang menurun ketika jumlah keran yang dibuka bervariasi yang dapat dimonitoring dari jarak jauh pada sistem instalasi air. Pada Tugas Akhir ini dirancang sebuah sistem untuk mengatur tekanan pompa air pada saluran pipa air dengan menggunakan tiga valve berbasis Internet of Things (IoT). Parameter tekanan dibaca menggunakan sensor tekanan yang keluarannya diolah dan dikalibrasi menggunakan arduino uno. Hasil keluaran digunakan sebagai parameter untuk mengatur kecepatan pompa. Untuk memonitoring sistem ini menggunakan nodeMCU ESP8266 agar dapat mengirim data ke android. Data dari bagian sensor monitoring dan alat pengontrol akan diproses oleh nodeMCU ESP8266, kemudian data akan dipublikasikan sehingga dapat dipantau menggunakan android. Tampilan pada sistem monitoring ini dibuat pada mit app inventor. Sebelum menyalakan sistem maka diatur set point tekanan minimal, maksimal, dan batas kerja sensor. Ketika tekanan kurang dari set point yang ditentukan, maka kecepatan motor akan terus bertambah. Ketika tekanan tepat pada set point maka kecepatan motor tetap. Ketika tekanan lebih dari set point maka kecepatan motor akan berkurang, dan ketika tekanan melebihi batas kerja sensor maka motor akan mati. Dengan sistem ini maka tekanan pada pipa air minum bisa stabil sesuai kebutuhan. Kata Kunci: Pompa Air, Monitoring, Internet of Things (IoT), Sensor Pressure. Tekanan Stabil

The development of installed capacity in power plants in 2018 was 41.696 MW or increased from the previous year in 2017 amounted to 39.652 MW (PLN Statistics, 2018). This has an impact on the reduced availability of fuel due to overexploitation. The highest energy sold per customer group in 2018 was the household customer group that was 41.7% higher than the industrial sector customer group by 32.8% (PLN Statistics, 2018). At present the use of electronic equipment for household needs is increasingly diverse. Many equipment that is often used in daily life is electronic equipment that is inductive load. Inductive load causes the value of the power factor to fall so that the power usage (Watt) becomes less than optimal. To overcome the problem caused by the large number of inductive loads a reactive power compensator is needed which is to use a capacitor. In this final project, a system designed to be able to measure and correct power factors automatically uses the Neural Network method and can monitor power online based on IoT. The results of testing the power factor improvement system were 97.8% successful in the trained electric load and 94.8% in the untrained electrical load.

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Microgrids are one example of a low voltage distributed generation pattern that can cover a variety of energy, such as conventional generators and renewable energy. Economic dispatch (ED) is an important function and a key of a power system operation in microgrids. There are several procedures to find the optimum generation. The first step is to find every feasible state (FS) for thermal generator ED. The second step is to find optimum generation based on FS using incremental particle swarm optimization (IPSO), FS is assumed that all units are activated. The third step is to train the input and output of the IPSO into deep learning (DL). And the last step is to compare DL output with IPSO. The microgrids system in this paper considered 10 thermal units and a wind plant with power generation based on probabilistic data. IPSO shows good results by being capable to generate a total generation as the load requirement every hour for 24 h. However, IPSO has a weakness in execution times, from 10 experiments the average IPSO process takes 30 min. DL based on IPSO can make the execution time of its ED function faster with an 11 input and 10 output architecture. From the same experiments with IPSO, DL can produce the same output as IPSO but with a faster execution time. From the total cost side, wind energy is affecting to reduce total cost until USD 22.86 million from IPSO and USD 22.89 million from DL.

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