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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Nico Surantha
Bina Nusantara University
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
Smart hydroponic based on nutrient film technique and multistep fuzzy logic Prabadinata Atmaja; Nico Surantha
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3146-3157

Abstract

Automation in hydroponics is have been a great change. Research with fuzzy logic control it’s designed to add to each parameter one by one. In a way microcontroller will activate one by one relay to regulate the parameters with fuzzy logic. While parameter calibration is done, calibration is needed for the next checking if the parameter were not optimal, until its parameter optimal. Multistep fuzzy is used to counter measure the same activation of the relay. With adding real time data monitoring to the system. From test result evaluating multistep fuzzy logic method were 100% works as expected. with another testing approach for best module for sending real time data monitoring for hydroponics. From the real time data transmission method, the success of sending data is 30% from the ESP82166 and 75% of the NRF24L01 with a shortage of the NRF24L01 data loss. For the relay activation can be accommodate with dynamic programming. As for multistep fuzzy logic for hydroponic tested to reach optimal water condition for kale crops resulting in average 12.8 iterations calibration from condition where researches add water only from the start.
Sleep Apnea Identification using HRV Features of ECG Signals Billy Sulistyo; Nico Surantha; Sani M. Isa
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (548.931 KB) | DOI: 10.11591/ijece.v8i5.pp3940-3948

Abstract

Sleep apnea is a common sleep disorder that interferes with the breathing of a person. During sleep, people can stop breathing for a moment that causes the body lack of oxygen that lasts for several seconds to minutes even until the range of hours. If it happens for a long period, it can result in more serious diseases, e.g. high blood pressure, heart failure, stroke, diabetes, etc. Sleep apnea can be prevented by identifying the indication of sleep apnea itself from ECG, EEG, or other signals to perform early prevention. The purpose of this study is to build a classification model to identify sleep disorders from the Heart Rate Variability (HRV) features that can be obtained with Electrocardiogram (ECG) signals. In this study, HRV features were processed using several classification methods, i.e. ANN, KNN, N-Bayes and SVM linear Methods. The classification is performed using subject-specific scheme and subject-independent scheme. The simulation results show that the SVM method achieves higher accuracy other than three other methods in identifying sleep apnea. While, time domain features shows the most dominant performance among the HRV features.
Co-Authors Billy Sulistyo Felix Ivan Gilang Intan Permatasari Duppa Prabadinata Atmaja Rezki Saputra Ritchie Chandra Salman Alfarisi Sani M. Isa Sugianto Sugianto Suharjito Suharjito Vincent Simadiputra