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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Applied Power Engineering (IJAPE) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Sinergi Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Bulletin of Electrical Engineering and Informatics Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer INTEGER: Journal of Information Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan TIJAB (The International Journal of Applied Business) JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Abdimasku : Jurnal Pengabdian Masyarakat Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Layanan Masyarakat (Journal of Public Service) Journal of Vocational Nursing Jurnal Nasional Pengabdian Masyarakat ALINIER: Journal of Artificial Intelligence & Applications Jurnal Sistim Informasi dan Teknologi Jurnal Ilmiah Hospitality Management Jurnal Rekayasa elektrika RENATA Jurnal Pengabdian Masyarakat Kita Semua JGEN : Jurnal Pengabdian Kepada Masyarakat
Firdaus, Aji Akbar
Department Of Engineering, Faculty Of Vocational, Universitas Airlangga, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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Journal : telkomnika telecommunication computing electronics and control

 1 
Short-term photovoltaics power forecasting using Jordan recurrent neural network in Surabaya Aji Akbar Firdaus; Riky Tri Yunardi; Eva Inaiyah Agustin; Tesa Eranti Putri; Dimas Okky Anggriawan
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 | 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.
Voice recognition system for controlling electrical appliances in smart hospital room Eva Inaiyah Agustin; Riky Tri Yunardi; Aji Akbar Firdaus
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 | 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.
Quantum binary particle swarm optimization for optimal on-load tap changing and power loss reduction Aji Akbar Firdaus; Irrine Budi Sulistiawati; Vicky Andria Kusuma; Dimas Fajar Uman Putra; Hamzah Arof; Novian Patria Uman Putra; Sena Sukmananda Suprapto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 2: April 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i2.25744

Abstract

Over time, there has been a continuous surge in the demand for electrical energy, necessitating the development of larger and more intricate electrical power networks. These extensive networks pose a significant challenge, primarily in the form of considerable loss of electrical energy, which, if not effectively addressed, may lead to persistent and imperceptible losses. In response to this challenge, this research proposes the application of quantum binary particle swarm optimization (QBPSO) for the coordinated management of on-load tap changers (OLTC) in loaded transformers within a distribution network, with a specific emphasis on reducing power losses. The experimental results demonstrate that the implementation of QBPSO results in a reduction of power loss from 21.756107 kW to 19.157321 kW and an increase in the average voltage from 19.00467941 kV to 19.93068 kV in a 20 kV 34-bus distribution network. This has the potential to significantly enhance overall system efficiency.
Short-term photovoltaics power forecasting using Jordan recurrent neural network in Surabaya Aji Akbar Firdaus; Riky Tri Yunardi; Eva Inaiyah Agustin; Tesa Eranti Putri; Dimas Okky Anggriawan
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 | 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.
Co-Authors Achmad Luki Satriawan Adi Soeprijanto Alim Prasaja Aly, Muhammad Nilzam Anang Budikarso Anang Tjahjono, Anang Andria Kusuma, Vicky Aprillia, Happy Arof, Hamzah Asy'ari, Vaizal Azmita, Mimi Bambang Suharto Bambang Suharto Belgis, Belgis DARMAWAN SETIA BUDI Daud, Muhamad Zalani Deny Arifianto Dewanto, Muhammad Ridho Dewi Ambarwati Dimas Fajar U.P. Dimas Fajar Uman Putra Dimas Okky Anggriawan Dwi Retna Kumalaningrum Eka Prasetyono, Eka Eka Saputra Elsyea Adia Tunggadewi Endro Wahjono Eva Inaiyah Agustin Farid, Mifta Nur Fasya, Arif Habib Firillia Filliana Gagas Gayuh Aji, Gagas Gayuh Ginting, Ina Veronica Hadi Suyono Hadi Suyono Hamzah Arof Hidayatul Nurohmah Indhana Sudiharto Irrine Budi Sulistiawati Jenrychk Marcelino Tandi Karrang Kadek Juni Setiawati Kenconojati, Hapsari Kharis Sugiarto Khuria Kirana, Angkita Wasito Kristanto, Damar Kristanto, Damar Kurnia Wijaya Kusuma Li Wang Machrus Ali Mahmud Iwan Solihin Maria Agustina Pardede Maulana, Muhammad Hilmy Maurisia Putri Permatasari Mifta Nur Farid Mimi Azmita Muhaimin Muhaimin Muhammad Agung Nursyeha Muhammad Aziz Muslim Muhammad Aziz Muslim Muhammad Nizhom Ramadhani Muhammad Ruswandi Djalal Nabila Salvaningtyas Nasa Zata Dina Novian Patria Uman Putra Novian Patria Uman Putra Novian Uman Putra Nuruddin Nuruddin Nyoman Juniarta, Nyoman Ontoseno Penangsang Prasaja, Alim Prayogo Prayogo Priyanto, Yun Tonce Kusuma Putra, Dimas Fajar Uman Putra, Wahyu Haryanto Putri, Rahmah Fadiyah Rachman Sinatriya Marjianto Rafdi Syah, Muhammad Abby Rahayu, Ragil Puji Rahmat Yuliawan Rajendran, Parvathy Risty Jayanti Yuniar Sena Sukmananda Suprapto Setiawati, Kadek Juni Sinulingga, Rizky Amalia Sisca Dina Nur Nahdliyah Sri Endah Kinasih Sri Endah Nurhidayati Suciyono, Suciyono Sugiarto, Kharis Suharno, Novianto Edi Suprapto, Sena Sukmananda Susanto, Fajar Annas Teguh Aryo Nugroho Teguh Herlambang Tesa Eranti Putri Trimulya, Hanif Uman Putra, Novian Patria Vicky Andria Kusuma Wilda Imama Sabilla Wilda Prihasty Winarno Winarno Winarno, Winarno Yanuar Mahfudz Safarudin Yuli Prasetyo Yunardi, Riky Tri