<p>Kualitas dan ketersediaan pasokan listrik menjadi hal yang sangat penting. Kegagalan pada transformator menyebabkan pemadaman listrik yang dapat menurunkan kualitas layanan kepada pelanggan. Oleh karena itu, pengetahuan tentang umur transformator sangat penting untuk menghindari terjadinya kerusakan transformator secara mendadak yang dapat mengurangi kualitas layanan pada pelanggan. Penelitian ini bertujuan untuk mengembangkan aplikasi yang dapat memprediksi umur transformator secara akurat menggunakan metode <em>Deep Learning-LSTM. LSTM </em>adalah metode yang dapat digunakan untuk mempelajari suatu pola pada data deret waktu. Data yang digunakan dalam penelitian ini bersumber dari 25 unit transformator yang meliputi data dari sensor arus, tegangan, dan suhu. Analisis performa yang digunakan untuk mengukur kinerja LSTM adalah <em>Root Mean Squared Error</em> (RMSE) dan <em>Squared Correlation (SC</em>). Selain LSTM, penelitian ini juga menerapkan <em>algoritma Multilayer Perceptron, Linear Regression,</em> dan <em>Gradient Boosting Regressor</em> sebagai algoritma pembanding.  Hasil eksperimen menunjukkan bahwa LSTM mempunyai kinerja yang sangat bagus setelah dilakukan pencarian komposisi data, seleksi fitur menggunakan algoritma KBest dan melakukan percobaan beberapa variasi parameter. Hasil penelitian menunjukkan bahwa metode <em>Deep Learning-LSTM</em> mempunyai kinerja yang lebih baik daripada 3 algoritma lain yaitu nilai RMSE= 0,0004 dan nilai <em>Squared Correlation</em>= 0,9690.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em></em><em>The quality and availability of the electricity supply is very important. Failures in the transformer cause power outages which can reduce the quality of service to customers. Therefore, knowledge of transformer life is very important to avoid sudden transformer damage which can reduce the quality of service to customers. This study aims to develop applications that can predict transformer life accurately using the Deep Learning-LSTM method. LSTM is a method that can be used to study a pattern in time series data. The data used in this research comes from 25 transformer units which include data from current, voltage, and temperature sensors. The performance analysis used to measure LSTM performance is Root Mean Squared Error (RMSE) and Squared Correlation (SC). Apart from LSTM, this research also applies the Multilayer Perceptron algorithm, Linear Regression, and Gradient Boosting Regressor as a comparison algorithm. The experimental results show that LSTM has a very good performance after searching for the composition of the data, selecting features using the KBest algorithm and experimenting with several parameter variations. The results showed that the Deep Learning-LSTM method had better performance than the other 3 algorithms, namely the value of RMSE = 0.0004 and the value of Squared Correlation = 0.9690.</em></p>

Media sosial yang digunakan setiap saat dapat menarik minat pengguna dalam hal ketertarikan dalam sebuah trend yang ada di masyarakat. Dari cepatnya penyebaran informasi menggunakan media sosial dapat mempengaruhi perilaku dan trend. Oleh sebab itu terdapat pergeseran dalam pemasaran, promosi dan periklanan. Dengan melihat potensi pengguna media sosial yang besar maka para pengiklan dan cara memasarkan produk banyak menggunakan media yang bersifat visual. Media sosial bisa dimanfaatkan untuk beragam kepentingan. Adanya kemajuan yang pesat dibidang teknologi informasi diimbangi dengan kemajuan teknologi transportasi dan manajemen logistik. Hal tersebut makin memudahkan terjadinya lalu lintas manusia sekaligus barang. Dua hal tersebut selayaknya menjadi peluang usaha yang harus dimanfaatkan. Dalam hal ini, bagaimana mendayagunakan media sosial yang banyak digunakan di semua kalangan seperti Whatsapp, Instagram, Youtube, Tiktok, Facebook dan Twitter atau yang lainnya untuk memasarkan produk. Produk lokal berskala industri rumah tangga banyak memiliki variasi produk namun permasalahannya adalah dalam memperkenalkan produk lokal serta belum optimal dalam pengemasan serta pemasaran. Salah satu kelompok industri rumah tangga yang menjadi mitra adalah kelompok industri rumah tangga dalam penghasil produk olahan makanan ringan seperti kerupuk, keripik sayuran dan kue kering. Kegiatan ini bertujuan untuk memberi pendampingan dan praktik langsung dalam penggunaan media sosial kepada warga  untuk mengetahui bagaimana pemasaran dengan media sosial dan pengemasan secara unik dan menarik. Hasil dari kegiatan ini adalah diharapkan pada mitra dapat meningkatkan pendapatan dan pengetahuan dalam pemanfaatan teknologi Informasi.

The main problem with duck workers who want to incubate eggs is that eggs turn two times a day. It causes farmers still use conventional hatching to incubate eggs, usually done manually. This study aims to make an automatic duck-egg incubator using a wireless sensor network. The making of incubator uses a 5-watt incandescent lamp, 5V relay, DHT11, soundsensor, LCD I2C, Wifi ESP8266 01, and a pipe designed to perform egg turning assisted by a servo motor as a driver for the pipe. The average temperature for this automatic incubator sets at 39 – 40 degrees Celcius. The results of this research hatching machine can work as planned. In the first experiment, we should insert four eggs and managed to incubate them, and two eggs managed to come out of the eggshell. In the second experiment, we should insert four eggs and managed to incubate three eggs to come out of the eggshell. So the percentage of success in hatching eggs using an automatic duck egg incubator using this wireless sensor network is in the first experiment as many as 50% or two eggs and in the second experiment as much as 75% or three eggs.

Due to the development of IoT-based technology, it is easier to exchange data between devices on a massive scale. Its application has expanded to all sectors. One of them is in the field of animal husbandry. In duck egg hatching, seeds are one of the keys to success. Efforts can obtain excellent sources are to using an automatic incubator. In this study, we tried to combine the technology of combining WSN concept and updating the technology of the conventional duck egg hatching process. This study uses a DHT11 sensor, a sound sensor, and a Servo Motor, which can automatically produce a duck egg incubator and be monitored in real time on the website. Based on the results of this study was able to create a hatching process of 87%. We tested this device in the summer and the rainy season. It expects to reduce the risk of failure of the duck egg hatching process.

pH stands for the power of Hydrogen, which is an important factor in hydroponic systems. The hydrogen ion concentration determines the value of the acidity in the solution. If the pH value of the solution is below 7.0, then it is called acidic, and if the pH value is above 7.0, it is called alkaline. The pH value can change, so it is necessary to pay attention to the acidity of the water so that the roots absorb nutrients properly that farmers often find it difficult to take measurements. Mustard plants have a pH range of 5.5 – 6.5. Based on the pH range, it is necessary to have a tool to monitor the quality of the pH in mustard plants. This monitoring is carried out by creating a hydroponic nutrient water disposal system by combining agriculture and mechatronics. Mechatronics will control the nutrient distribution system so that it can be monitored with a smartphone via wireless connectivity such as the GSM module (800L. The water quality monitoring system works by using ultrasonic sensors, sensor probes and TDS sensors, as well as the GSM 800L module. monitoring of water quality in hydroponic mustard plants) using an Arduino microcontroller and detecting pH conditions and solute content. As for the disposal of nutrient water, using a water pump connected to the microcontroller via a relay. Based on the prototype carried out on June 26, 2021, the condition of water quality has decreased in nutritional quality with the first data (pH 6.31 solute 529 ppm) and the latest data (pH 9.09 and solute 662 ppm). When the water quality is below the nutritional standard, the water pump turns on to remove the nutrient water. The results of the prototype that has been carried out have succeeded in monitoring water quality hydroponics and can be used by farmers.