This paper proposes a noninvasive dual optical photoplethysmography (PPG) sensor to classify the degree of arteriovenous fistula (AVF) stenosis in hemodialysis (HD) patients. Dual PPG measurement node (DPMN) becomes the primary tool in this work for detecting abnormal narrowing vessel simultaneously in multi-beds monitoring patients. The mean and variance of Rising Slope (RS) and Falling Slope (FS) values between before and after HD treatment was used as the major features to classify AVF stenosis. Multilayer perceptron neural networks (MLPN) training algorithms are implemented for this analysis, which are the Levenberg-Marquardt, Scaled Conjugate Gradient, and Resilient Back-propagation, to identify the degree of HD patient stenosis. Eleven patients were recruited with mean age of 77 ± 10.8 years for analysis. The experimental results indicated that the variance of RS in the HD hand between before and after treatment was significant difference statistically to stenosis (p < 0.05). Levenberg-Marquardt algorithm (LMA) was significantly outperforms the other training algorithm. The classification accuracy and precision reached 94.82% and 92.22% respectively, thus this technique has a potential contribution to the early identification of stenosis for a medical diagnostic support system.

The most common treatment for end-stage renal disease (ESRD) patients is the hemodialysis (HD). For this kind of treatment, the functional vascular access that called arteriovenous fistula (AVF) is done by surgery to connect the vein and artery. Stenosis is considered the major cause of dysfunction of AVF. In this study, a noninvasive approach based on asynchronous analysis of bilateral photoplethysmography (PPG) with error correcting output coding support vector machine one versus rest (ESVM-OVR) for the degree of stenosis (DOS) evaluation is proposed. An artificial neural network (ANN) classifier is also applied to compare the performance with the proposed system. The testing data has been collected from 22 patients at the right and left thumb of the hand. The experimental results indicated that the proposed system could provide positive predictive value (PPV) reaching 91.67% and had higher noise tolerance. The system has the potential for providing diagnostic assistance in a wearable device for evaluation of AVF stenosis.

This study aims to apply the Internet of Things to monitoring electricity energy consumption. The system is designed to replace the manual and conventional measurement of electrical energy. Uncontrolled use of electricity is one of the causes of high levels of electrical energy consumption. Therefore, consumers need to know the amount of electrical energy consumption in real-time. The device is designed using the PZEM-004T which is used as a sensor to read electrical multi-parameter, Arduino as the main control, and ESP8266 as a data sender on the Cloud system. The test results showed that the device has been able to read and display electricity quantity data in the form of voltage, current, power, and electricity tariff accumulation in real-time displayed on the ThingSpeak platform. The test on the PZEM-004T sensor has an accuracy rate of 94.96% for reading current values and 99.42% for reading voltage values. Penelitian ini bertujuan untuk menerapkan teknologi Internet of Things pada monitoring konsumsi energi listrik. Sistem dirancang untuk menggantikan pengukuran energi listrik yang masih manual dan konvensional. Penggunaan listrik yang tidak terkontrol merupakan salah satu penyebab tingginya tingkat pemakaian energi listrik. Oleh karena itu, konsumen perlu mengetahui jumlah pemakaian energi listrik secara realtime. Alat dirancang menggunakan PZEM-004T yang digunakan sebagai sensor untuk membaca multi-parameter listrik, Arduino sebagai kendali utama, dan ESP8266 sebagai pengirim data pada sistem Cloud. Hasil pengujian menunjukan bahwa  alat telah mampu membaca dan menampilkan data besaran listrik berupa tegangan, arus, daya, dan akumulasi tarif listrik secara realtime yang ditampilkan pada platform ThingSpeak. Pengujian pada sensor PZEM-004T memiliki tingkat akurasi 94.96% untuk pembacaan nilai arus dan 99. 42% untuk pembacaan nilai tegangan.

Sustainable agriculture or aquaponics in the province of Maluku especially in Ambon city began to be popular because it provides many benefits. However, it has several problems, namely not being able to control its water pump, monitoring temperature and humidity which are not effective. In order to solve these problems, it needs a touch of technology, which can make the system smarter. Integration between a microcontroller and a smartphone under the android platform embedded in the aquaponic system is the answer to the problem to control and monitor its environment in real time basis. ABSTRAKPertanian berkelanjutan atau akuaponik di provinsi maluku khusunya kota ambon mulai digemari. Hal ini dikarenakan sistem pertanian ini sangat memberi banyak manfaat. Akan tetapi sistem yang ada masih memliki kekurangan, yaitu tidak dapat mengontrol pompa air, memonitoring suhu dan kelembaban yang tidak efektif. Dan sifat sistem ini pada umumnya masih bersifat konvensional. Untuk mengatasi kekurangan ini dibutuhkan sentuhan teknologi yang membuatnya lebih cerdas. Integrasi antara mikrokontroler dan telpon selular dengan platform android didalam sistem akuaponik menjadi jawaban untuk mengendalikan dan memonitor lingkungan sekitar secara real time.

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