<pre><em>Gout is the final metabolite of purines. Purines are part of the nucleic acids found in the nuclei of body cells. Increased gout can cause rheumatic pain in the joint area and is often associated with extreme pain for those exposed to the disease. Doctors need to monitor so that they can assist patients in monitoring and treatment. Gout detection devices are only available in hospitals, clinics, health centers, laboratories, and equipment that was previously portable but could not be controlled directly by a doctor. The purpose of this study is to help make it easier for doctors to monitor patients with gout remotely or telemedicine via the internet. The research method uses blood to determine gout levels using the Internet of Things. Data acquisition is carried out with a resistance sensor (Authocheck) which is processed by the Arduino microcontroller. The processed data is then sent to the ESP8266 web server via WiFi. The use of the Internet of Things as a data transmission method for online use does not require human-to-human interaction. The sensor resistance value in the analog range obtained is 441.03 to 782.32 with a sensor voltage of 1.91 to 3.82 volts. The measured gout level is between 4 mg/dL and 8 mg/dL. The percentage of measured data with an average accuracy of 95.74% and an average error rate of 4.26% for the seven test data. Data is displayed directly on the device's LCD screen and on a web server that sends data from the ESP8266.</em><em></em></pre><pre><em> </em></pre><p><strong><em>Keywords:</em></strong><strong><em> </em></strong><em>gout</em><em>, Internet of Thing</em><em>s</em><em>, resistance</em><em> sensor</em><em> (</em><em>A</em><em>utocheck</em><em>), telemedicine,</em><em> </em><em>web server</em></p><p><em> </em></p><p align="center"><strong>ABSTRAK</strong></p><p>Asam urat merupakan metabolit akhir dari purin. Purin adalah bagian dari asam nukleat yang ditemukan dalam inti sel tubuh. Asam urat yang meningkat dapat menyebabkan nyeri rematik di area persendian dan sering dikaitkan dengan rasa sakit yang luar biasa bagi yang terpapar penyakit. Dokter perlu memantau agar dapat membantu pasien dalam pemantauan dan pengobatan. Alat pendeteksi asam urat hanya terdapat di rumah sakit, klinik, puskesmas, laboratorium dan alat-alat yang sebelumnya <em>portable</em> namun tidak dapat dikontrol langsung oleh dokter. Tujuan dari penelitian ini membantu memudahkan dokter untuk memantau pasien dengan penyakit asam urat secara jarak jauh atau <em>telemedicine</em> melalui internet. Metode penelitian menggunakan darah untuk menentukan kadar asam urat menggunakan <em>Internet of Things</em>. Akuisisi data dilakukan dengan sensor resistansi (<em>Authocheck</em>) yang diproses oleh mikrokontroler Arduino. Data yang telah diproses kemudian dikirim ke web server ESP8266 melalui  WiFi. Penggunaan <em>Internet of Things</em> sebagai metode transmisi data untuk penggunaan secara <em>online</em> tidak memerlukan interaksi manusia ke  manusia. Nilai  resistansi sensor pada rentang analog yang diperoleh adalah 441,03 hingga 782,32 dengan tegangan sensor 1,91 hingga 3,82 volt. Kadar asam urat yang diukur adalah antara 4 mg/dL dan 8 mg/dL. Persentase data terukur dengan akurasi rata-rata 95,74% dan rata-rata tingkat kesalahan  4,26% untuk tujuh data uji. Data ditampilkan langsung di layar LCD perangkat dan di <em>web server</em> yang mengirimkan data dari ESP8266.</p>

<p class="IndexTerms">Along with the development of the times the need for electrical energy is increasing. PT. PLN (Persero) as a company that supplies electrical energy must be able to meet the needs of electrical energy to run a business to supply electric power in an even amount to meet the needs of the household, industrial, social and business sectors.. The purpose of this research to determine how much the growth of electrical loads in 2020 to 2025 at PT. PLN (Persero) Rayon Juwana. To find out the amount of increase in electrical energy required. Electrical data used data for 8 years, from 2012 to 2019. The results of this study are in the form of an estimate of the use and need for load electrical energy load for the next 5 years, from 2020 to 2025, the data used are the number of subscribers, connected power, and the amount of energy using a simulated neural network with the method (radial basis function). the results of this study an increase per year an average of 1% per year. in 2019 the value is 1.07%, in 2020 it is 1.10%, 2021 is 1.21%, for 2022 it is 1.27%, 2023 is 1.28%, 2024 is 1.17% and 2025 is 1.31 %.</p>

High economic growth makes the demand for comfortable and safe houses increase and the application of technology that is most clearly seen is technology with automatic systems. With this technology, the use of electricity in the house can be minimized and offer convenience in controlling the house. Sometimes homeowners forget to turn off the lights when they are outside the house so they have to go back and do checks that are very inefficient both in terms of time and financially such as the cost of gasoline to return to the house. Based on this, the Smart Home Prototype was created with the concept of the Internet of Things (IoT) using Raspberry Pi Web-Based, which is a system that can remotely control electronic home appliances using Raspberry Pi as a base system, which is connected to Web Applications through the internet network. The electronic equipment used in this study is in the form of 5 lamps, 1 stepper motor to control the garage, 1 servo motor to control the door lock, and 1 brushless motor that functions as a fan. Blocking and overall test results on Bedroom Lights, Living Room Lights, Kitchen Lights, Bathroom Lights, Porch Lights, Garages, Door Locks, and Fans, all work well. The testing of the distance between cities against the Prototype Smart Home was successfully carried out, where the Prototype Smart Home in the City of Demak was successfully controlled by the User who at the time of testing was in the City of Semarang, Kudus, Japan, Surabaya, and Jakarta.

<p><strong>D</strong>esign of Smart Home Automatic Control with Communications Via Bluetooth HC-05-Based Microcontroller Arduino and Android, will provide all the comfort, safety, security and energy savings, which takes place automatically and programmed via microcontroller, the house or building. Design of Smart Home Automatic with Control Communications Via Bluetooth HC-05-Based Microcontroller Arduino and Android is intended for automatic control processes.</p><p>The system consists of seven sensors, keypad password for security doors, HC-05 Bluetooth GSM alarm application and device control applications aktuaktor, and aktuaktor buzzer, dc motors, lights, fans and fan dc exhausting. Among these sensors is a PIR sensor to detect theft, sensor MQ6 to detect leakage of LPG gas and smoke from fires, sensor HC-SR04 ultrasonic for automatic garage doors, fire sensors to detect a fire, the sensor LM35 to detect the temperature of the room, when the room temperature is hot the fan will be on automatically, when the cold room temperature will shut off the fan and LDR sensor to detect the intensity of light during the day which turn the lights off automatically and vice versa, thus saving energy. Homeowners can also control lights and monitoring sensors with android phones that are connected with communication via Bluetooth HC-05 microcontroller arduino, and can receive GSM alarm application sensors such as calling and sms.</p><p>Test results are used UART communication data transmission between the microcontroller serial monitor monitor has an error of 0 % . HC - 05 Bluetooth can accept orders at the farthest distance of 11 meters . The fire sensor can work well when the fire was 1-10 cm . Sensor HC - SR04 is already working with an error rate of 0.3 %, so the accuracy of this tool by 100 % - 0.3 % = 99.7 % .</p><p><strong>Keywords :</strong> Security Door , Bluetooth HC - 05 , Microcontroller Arduino and Android , Application Control and Application GSM alarm.</p>

<p>The need for water is very important for the production process, the water quality must also be considered the pH value so that the water is suitable for use in boilers or chillers. Seeing from these needs a simulation was made on matlab R2017a using a fuzzy toolbox that can find out the water quality with fuzzy mamdani method. This fuzzy logic simulation can determine the standard value of boiler water pH (10.5-11.5) and the standard pH value of chiller water (6-8) in the production machine. The methods in fuzzy logic used are mamdani and centroid methods as deffuzification with R2017a matlab simulation tool. The fuzzy inference system used is the mamdani method. This study made a simulation of mamdani method that can determine the quality of the PH water produced by using fuzzy logic. Chiller water pH can be used if iron ion values (0-2 ppm), silica ions (50-150 ppm), Total Dissolved Solid (TDS) (500-1000 ppm), total Hardness (100-200 ppm), M-alkalinity (500-750 ppm) from the standard value, the pH of the chiller water can be proven to reach a water pH value of 6-8. Boiler water pH can be used if iron ion values (0-2 ppm), silica ions (100-150 ppm), Total Dissolved Solid (TDS) (2000-3500 ppm), total Hardness (0-3 ppm), M-alkalinity (700-800 ppm) from the standard value, the pH of boiler water can be proven to reach a water pH value of 10.5-11.5.<br />Keywords: fuzzy logic, Mamdani method, pH of boiler water</p>

Electric power is one of the main needs of society today, ranging from household consumers to industry. The demand for electricity increases every year. So as to achieve adjustments between power generation and power demand, the electricity provider (PLN) must know the load needs or electricity demand for some time to come. There are many studies on the prediction of electricity loads in electricity, but they are not specific to each consumer sector. One of the predictions of this electrical load can be done using the Radial Basis Function Artificial Neural Network (ANN) method. This method uses training data learning from 2010 - 2017 as a reference data. Calculations with this method are based on empirical experience of electricity provider planning which is relatively difficult to do, especially in terms of corrections that need to be made to changes in load. This study specifically predicts the electricity load in the Semarang Rayon network service area in 2019-2024. The results of this Artificial Neural Network produce projected electricity demand needs in 2019-2024 with an average annual increase of 1.01% and peak load in 2019-2024. The highest peak load in 2024 and the dominating average is the household sector with an increase of 1% per year. The accuracy results of the Radial Basis Function model reached 95%.