Firdaus, Aji Akbar
Universitas Airlangga

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Short-term photovoltaics power forecasting using Jordan recurrent neural network in Surabaya Firdaus, Aji Akbar; Yunardi, Riky Tri; Agustin, Eva Inaiyah; Putri, Tesa Eranti; Anggriawan, Dimas Okky
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (429.163 KB) | DOI: 10.12928/telkomnika.v18i2.14816

Abstract

Photovoltaic (PV) is a renewable electric energy generator that utilizes solar energy. PV is very suitable to be developed in Surabaya, Indonesia. Because Indonesia is located around the equator which has 2 seasons, namely the rainy season and the dry season. The dry season in Indonesia occurs in April to September. The power generated by PV is highly dependent on temperature and solar radiation. Therefore, accurate forecasting of short-term PV power is important for system reliability and large-scale PV development to overcome the power generated by intermittent PV. This paper proposes the Jordan recurrent neural network (JRNN) to predict short-term PV power based on temperature and solar radiation. JRNN is the development of artificial neural networks (ANN) that have feedback at each output of each layer. The samples of temperature and solar radiation were obtained from April until September in Surabaya. From the results of the training simulation, the mean square error (MSE) and mean absolute percentage error (MAPE) values were obtained at 1.3311 and 34.8820, respectively. The results of testing simulation, MSE and MAPE values were obtained at 0.9858 and 1.3311, with a time of 4.591204. The forecasting has minimized significant errors and short processing times.
Robotic Leg Design to Analysis the Human Leg Swing from Motion Capture Yunardi, Riky Tri; Firdaus, Aji Akbar; Agustin, Eva Inaiyah
Bulletin of Electrical Engineering and Informatics Vol 6, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (766.684 KB) | DOI: 10.11591/eei.v6i3.645

Abstract

In this paper presented the prototype of robotic leg has been designed, constructed and controlled. These prototype are designed from a geometric of human leg model with three joints moving in 2D plane. Robot has three degree of freedom using DC servo motor as a joint actuators: hip, knee and ankle. The mechanical leg constructed using aluminum alloy and acrylic material. The control movement of this system is based on motion capture data stored on a personal computer. The motions are recorded with a camera by use of a marker-based to track movement of human leg. Propose of this paper is design of robotic leg to present the analysis of motion of the human leg swing and to testing the system ability to create the movement from motion capture. The results of this study show that the design of robotic leg was capable for practical use of the human leg motion analysis. The accuracy of orientation angles of joints shows the average error on hip is 1.46º, knee is 1.66º, and ankle is 0.46º. In this research suggesting that the construction of mechanic is an important role in the stabilization of the movement sequence.
Robotic Leg Design to Analysis the Human Leg Swing from Motion Capture Yunardi, Riky Tri; Firdaus, Aji Akbar; Agustin, Eva Inaiyah
Bulletin of Electrical Engineering and Informatics Vol 6, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (766.684 KB) | DOI: 10.11591/eei.v6i3.645

Abstract

In this paper presented the prototype of robotic leg has been designed, constructed and controlled. These prototype are designed from a geometric of human leg model with three joints moving in 2D plane. Robot has three degree of freedom using DC servo motor as a joint actuators: hip, knee and ankle. The mechanical leg constructed using aluminum alloy and acrylic material. The control movement of this system is based on motion capture data stored on a personal computer. The motions are recorded with a camera by use of a marker-based to track movement of human leg. Propose of this paper is design of robotic leg to present the analysis of motion of the human leg swing and to testing the system ability to create the movement from motion capture. The results of this study show that the design of robotic leg was capable for practical use of the human leg motion analysis. The accuracy of orientation angles of joints shows the average error on hip is 1.46º, knee is 1.66º, and ankle is 0.46º. In this research suggesting that the construction of mechanic is an important role in the stabilization of the movement sequence.
Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network Putri, Tesa Eranti; Firdaus, Aji Akbar; Sabilla, Wilda Imama
Journal of Information Systems Engineering and Business Intelligence Vol 4, No 2 (2018): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1369.973 KB) | DOI: 10.20473/jisebi.4.2.96-105

Abstract

Depending on the day and time, electricity consumption tends to fluctuate and directly affects the amount of gained revenue for the company. To anticipate future economic change and to avoid losses in calculating the company’s revenue, it is essential to forecast electricity consumption revenue as accurate as possible. In this paper, Jordan Recurrent Neural Network (JRNN) was used to do short term forecasting of the electricity consumption revenue from Java-Bali 500 kVA electricity system. Seven JRNN models were trained using electricity consumption revenue between January-March 2012 to predict the revenue of the first week of April 2012. As performance comparators, seven traditional feed forward Artificial Neural Network (ANN) models were also constructed. The forecasting results were as expected for both models, where both producing steady repeating pattern for weekdays, but failed quite poorly to predict the weekends’ revenue. This suggests that in Indonesia, weekends’ electricity consumption revenue has different characteristics than weekdays. Evaluation of the prediction result was carried out using Sum of Square Error (SSE) and Mean Square Error (MSE). The evaluation showed that JRNN produced smaller SSE and MSE values than traditional feed forward ANN, thus JRNN could predict the electricity consumption revenue of Java-Bali electricity system more accurately.
Voice recognition system for controlling electrical appliances in smart hospital room Agustin, Eva Inaiyah; Yunardi, Riky Tri; Firdaus, Aji Akbar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (576.686 KB) | DOI: 10.12928/telkomnika.v17i2.11781

Abstract

Nowadays, most hospitals have new problem that is lack of medical nurse due to the number of patient increas rapidly. The patient especially with physical disabilities are difficult to control the switch on electrical appliances in patient’s room. This research aims to develope voice recognition based home automation and being applied to patient room. A miniature of patient’s room are made to simulate this system. The patient's voice is received by the microphone and placed close to the patient to reduce the noise.V3 Voice recognition module is used to voice recognition process. Electrical bed of patient is represented by mini bed with utilising motor servo. The lighting of patient room is represented by small lamp with relay. And the help button to call the medical nurse is represented by buzzer. Arduino Uno is used to handle the controlling process. Six basic words with one syllable are used to command for this system. This system can be used after the patient's voice is recorded. This system can recognize voice commands with an accuracy 75%. The accuracy can be improved up to 85% by changing the voice command into two syllables with variations of vowels and identical intonation. Higher accuracy up to 95% can be reached by record all the subject’s voice.