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
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.
Baterai sebagai sumber tenaga utama AGV memiliki kapasitas muatan listrik terbatas sehingga perlu diisi ulang. Alat pengisi muatan baterai AGV konvensional memerlukan bantuan operator untuk melakukan pengisian sehingga kurang efektif dan efisien. Alat tersebut juga tidak memiliki sistem pengirim status pengisian muatan. Operator AGV umumnya melakukan pemantauan pada beberapa charging station yang sedang beroperasi di lokasi yang berbeda. Kelalaian operator dalam melakukan pemantauan dapat berakibat buruk pada kinerja AGV. Oleh karena itu, dibuat alat pengisi muatan baterai AGV otomatis menggunakan Arduino Mega 2560 yang dilengkapi sistem pewaktu dan sistem pengiriman status pengisian, agar dapat melakukan pengisian sesuai waktu yang dibutuhkan dan dapat ditindaklanjuti bila terjadi kegagalan atau kesalahan pada pengisian muatan baterai. Waktu pengisian dapat diatur melalui HMI. Status pengisian dikirim melalui SMS ke handphone operator menggunakan Modul GSM SIM900.
Caries may be halted or reversed in their progression by early detection, better hygiene habits, and coadministered drugs. The major clinical procedures for identifying dental caries are visual-tactile examination and dental radiography. However, due to their location, approximate caries exceedingly difficult to detect and affect the clinical assessment. Incorrect interpretations may also hinder the diagnostic procedure. Computational approaches and technology can be used to help dentists assess caries. Teledentistry has the ability to improve dental health care by providing access to dental care services from a remote location. Teledentistry helps identifying various stages of caries lesions using neural network and devices connected to the internet. This research develops an image classification for teledentistry systems using depthwise separable convolutional neural network. The trainable parameters reduction of depthwise separable convolution (DSC) successfully reduces the computational cost of conventional convolutional neural networks (CNN). As a result, the DSC model is reduced by 91.49% when compared to the traditional CNN model. Several DSC models improve conventional CNN accuracies in the training, validation, evaluation, and testing stages.
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