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Journal : JAVA Journal of Electrical and Electronics Engineering

Penerapan Wireless Sensor Network (Wsn) Dengan Topologi Tree Pada Pemantauan Tanah Longsor Lesmana, Wahyu Indra; Harianto, Harianto; Wibowo, Madha Christian
JAVA Journal of Electrical and Electronics Engineering Vol 13, No 1 (2015)
Publisher : JAVA Journal of Electrical and Electronics Engineering

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Abstract

Currently wireless technology has grown rapidly. Wireless technology is wireless communication technology, and currently has a lot of growing up and one of the developments is the wireless sensor networks (WSN). WSN is a combination of wireless modules, micro module and the sensor module, the workings of WSN is the sensor response value to the microcontroller module and the response values are transmitted via wireless communication. Based on how the WSN can be used for various purposes with one of them for monitoring natural disasters. WSN is designed for monitoring natural disasters landslides. In general WSN systems have problems such as limited distance. In this system, the authors apply topology tree and spanningtreeprotocol (STP). Tree topology is a collection of star topology are connected into the bus topology as spine or backbone lines, while STP is a network communications protocol that has automatic backup paths if the main line is not active. By implementing this system, the maximum distance of the receiving and sending data of node to node is 100 meters, while by applying tree topology and spanningtreeprotocol within a maximum range on the whole system is 141 meters. With these results it can be concluded that the overall system using a maximum distance of wider scope.
Pengendalian Salinitas Pada Air Menggunakan Metode Fuzzy Logic Mubarok, Fahmi; Harianto, Harianto; Wibowo, Madha Christian
JAVA Journal of Electrical and Electronics Engineering Vol 13, No 1 (2015)
Publisher : JAVA Journal of Electrical and Electronics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1410.154 KB)

Abstract

some fish farmers has been doing some way of controlling salinity in the water for marine fish farming, the farmers will turn on the water pump when they found the taste of the water used to grow fish is not salty, this indicates that the levels of salinity in the water too low. With this method will certainly lead to the growth of fish disturbed. So monitoring and adjusting the condition of pond water is continuously required to maintain water salinity. In this research we analyze one of the supporting factors for the cultivation of marine fish by adjusting the amount of salinity in the water and maintain it in accordance with the needs of tiger grouper. Using Arduino Uno R3 as a data processor, water level or salinity sensor as measuring salinity and input system, while for the actuator system using DC water pump. Salinity measurements using the water level or salinity sensor has an accuracy rate of 98.99%. Calculation of salinity by using fuzzy method goes well with a 100% success rate in accordance with the fuzzy analysis manually. By using salt water pump system can work well in controlling salinity and maintain between 30-33 ppt salinity.
PENGAMBILAN FITUR ANGKA JAWA MENGGUNAKAN SHADOW FEATURE EXTRACTION Anam, Angsorul; Rasmana, Susijanto Tri; Wibowo, Madha Christian
JAVA Journal of Electrical and Electronics Engineering Vol 13, No 2 (2015)
Publisher : JAVA Journal of Electrical and Electronics Engineering

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Abstract

Many Applications developed to recognition handwritten called Optical Character Recognition (OCR), who generally it only presented alphabet recognition. Javanese numbers (aksara wilangan) or Javanese characters are culture of Indonesia from great grandmother and must be knowed by rising genereation. The final project presented “Get Character Feature Handwriting Javanese Numbers Used Shadow Feature Extraction Method and Multi Layer Perceptron (MLP). The shadow feature method used to recognition characterstic of handwritten before it classification by MLP. Application test have two stage that is a sample training test 100 of data set and sample testing test 50 of data set. Percentage successed pattern concerning training samples 99% and error recognition 0,10%, whereas testing samples 90,8% and error recognition 9,2%.