Sistem monitoring kualitas air budidaya ikan laut akan dirancang menggunakan sensor cerdas dengan menyesuaikan kondisi lingkungan teripang, yaitu kualitas air pada salinitas 30-37%, dimana air laut umumnya mempunyai salinitas antara 33-37%, di perairan pantai berkisar antara 32-35% dan kondisi perairan dengan kisaran optimum pH 7,5-8,0 serta kondisi jumlah oksigen terlarut (Dissolved Oxygen) berkisar antara 5,0-5,5 mg/L dalam perairan. Salinitas, pH, dan DO merupakan faktor utama sebuah keramba menjadi lebih sensitif terhadap budidaya teripang, apabila tidak terpantau rutin. Maka dikembangkanlah inference engine dengan logika fuzzy untuk memantau DO, pH, dan salinitas serta model algoritma pembelajaran supervise. Hasil simulasi akan dianalisis dengan algoritma pembelajaran berbasis supervisi, menghitung bobot dan bias secara iteratif. Representasi data diakuisisi dan dikembangkan kecerdasan buatan model fuzzy untuk memantau DO, pH, dan salinitas. Kemudian menggunakan software LabVIEW yang mampu memonitor dan mengakuisisi data secara cepat dan akurat serta microcontroller sebagai pengolah data dari sensor DO, pH, dan salinitas. Luaran penelitian ini akan merealisasikan prototipe system monitoring jarak jauh dengan teknologi IoT yang ditujukan untuk memonitor nilai pH 7,77-8,27, DO pada 5,0-5,5 mg/L, dan salinitas pada 27,33-30 ppt secara kontinyu dan akurat
PT Bank Mega Tbk merupakan perusahaan swasta yang berfokus pada perbankan. Helpdesk ticketing system yang terkomputerisasi sangat diperlukan untuk menunjang kegiatan di PT. Bank Mega Tbk, dalam membuat laporan keluhan masalah yang ingin diselesaikan. Banyaknya jumlah keluhan yang masuk di bagian IT Support, penanganannya tidak optimal karena para unit lain melakukan pelaporan menggunakan banyak jalur komunikasi tanpa adanya jalur komunikasi yang jelas sehingga tidak adanya proses pencatatan, meskipun permasalahan dapat diselesaikan namun memakan waktu yang lama dan sering terjadinya redundasi data. Dari permasalahan diatas penulis mencoba membuat analisis dan perancangan sistem informasi helpdesk ticketing system yang dapat membantu staf IT Support. Rancangan sistem dibuat dengan mengacu model pengembangan Rapid Application Development (RAD) dan tool untuk perancangan sistem menggunakan UML (Unified Modelling Language). Adanya perancangan sistem ini, diharapkan dapat membantu menyelesaikan permasalahan.
Eye strain is a big concern, especially when it comes to continuous and prolonged online learning. If this is allowed to continue, it will result in Computer Vision Syndrome, also known as Digital Eye Strain (DES), which includes headaches, blurred vision, dry eyes, and even neck and shoulder pain. This condition can be observed either directly based on excessive eye blinking or indirectly based on observations of the electrical activity of eye movements or electrooculography (EOG). The observed blink signal from the EOG, as a representation of eye strain, is the focus of this study. Data acquisition was obtained using the EOG sensor and was carried out on the condition that the participants were conducting online learning activities. There are four different modes of observation taken in succession: when the eye is in a viewing state but without blinking, when the eye blinks intentionally, when the eye is closed, and finally when the eye sees naturally. Observation time is 10s, 20s and 30s, where each interval is performed three times for every mode. The obtained signal is processed by the proposed method. The resulting signal is then labeled as a Blinking signal. Determination of the number of blinks or CNT_PEAK is the result of training this signal by tunning its threshold and width. If the number of blinks is less than or more than 17 then the system will provide a prediction of eye status which is stated in two categories, the first is normal eye while the last is eye strain or fatigue.
Currently the state electricity enterprise, known as PLN, monitors the active power of customers by recording meters every month and is still prone to electricity theft because of location of the electricity measuring devices placed in each customer’s home. In this paper, we propose an electric theft detection device with an ARM microcontroller, placed centrally on the side of distribution transformer panel to measure active power in each customer and total active power in distribution transformer. The total active power of all customers is used to predict total active power in distribution transformer by the neural network method. By comparing the active power measured in distribution transformer with prediction of the active power from the neural network method and comparing the total active power of all customers with the total active power in the distribution transformer, devices that have been made are capable of detecting electrical theft if there is percentage of error between predicted value of active power on IED Master and active power measured on IED Master above 1 percent, and also the percentage of errors between the total active power from 2 pieces of IED Slave and active power measured in IED Masters above 1 percent.
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