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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Rotasi Jurnal Pendidikan Teknologi dan Kejuruan Mechatronics, Electrical Power, and Vehicular Technology Semesta Teknika Jurnal Teknologi Elektro ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika EMITTER International Journal of Engineering Technology JURNAL NASIONAL TEKNIK ELEKTRO KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Briliant: Jurnal Riset dan Konseptual JTT (Jurnal Teknologi Terpadu) INVOTEK: Jurnal Inovasi Vokasional dan Teknologi JURNAL INTEGRASI INOVTEK POLBENG Techne : Jurnal Ilmiah Elektroteknika Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Jurnal Teknologi Elekterika Jurnal Teknik Elektro dan Komputasi (ELKOM) Jurnal INOVTEKSeri Elektro JAREE (Journal on Advanced Research in Electrical Engineering) Jurnal Nasional Teknik Elektro dan Teknologi Informasi J-Innovation Jurnal Ilmiah Teknik Elektro Jurnal Rekayasa elektrika Suara Teknik : Jurnal Ilmiah The Indonesian Journal of Computer Science Emitor: Jurnal Teknik Elektro Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Novie Ayub Windarko
Politeknik Elektronika Negeri Surabaya
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation Other

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Estimasi State-of-Charge Pada Baterai Lithium-Ion Menggunakan Deep Neural Network Amrullah, Haniifan Patra; Windarko, Novie Ayub; Sumantri, Bambang
BRILIANT: Jurnal Riset dan Konseptual Vol 9 No 3 (2024): Volume 9 Nomor 3, Agustus 2024
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/briliant.v9i3.1874

Abstract

As electric vehicles (EV) become increasingly popular in the automotive world, an accurate State-of-Charge (SoC) estimation is critical to optimizing energy utilization, increasing driving range and ensuring long-lasting battery system. This research focuses on the application of Deep Neural Networks (DNN) as an SoC estimation method in EV, exploiting the inherent capacity of DNN to learn complex relationships in vast data sets. The results of the performed simulations show that the proposed DNN-based SoC estimation method achieves a high level of accuracy, outperforming traditional estimation techniques, especially in scenarios involving rapid changes in driving conditions. This research also explores the impact of Neural Networks architecture and hyperparameter tuning on overall performance and provides insights for optimizing DNN-based SoC estimation systems. From the tests that have been carried out, an error value of 1.3% is obtained from the results of the training carried out on the DNN structure that has been prepared.
Estimasi State Of Charge (Soc) Pada Baterai Lithium Ion Menggunakan Long Short-Term Memory (LSTM) Neural Network Husien.R, Alwi Azis; Windarko, Novie Ayub; Sumantri, Bambang
BRILIANT: Jurnal Riset dan Konseptual Vol 9 No 4 (2024): Volume 9 Nomor 4, November 2024
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/briliant.v9i4.1955

Abstract

Lithium-ion batteries have become one of the top choices for efficient and environmentally friendly mobility in today's era. Batteries play an important role in our digital lifestyles, from smartphones to electric cars. The use of this battery is inseparable from the challenge of estimating the State of Charge (SOC), which is a key parameter to monitor the availability of energy remaining in the battery. Therefore, an accurate SOC Estimation method is needed, which is important for efficient energy management and safe battery use. The Long Short-Term Memory (LSTM) model was chosen because of its ability to handle complex time series data and nonlier patterns in battery performance. This study provides the application of LSTM for SoC estimation and shows that LSTM is superior to the Feed Neural Network (FNN) method as evidenced by the simulation results that show that the LSTM model produces an RMSE of 4.92%, while the FNN model produces an RMSE of 7.82. From all the tests that have been carried out, the best RMSE value of 3.53% was obtained at a temperature of 25°C epoch 100.
EVALUASI KINERJA MODEL ARIMA DALAM PERAMALAN KONSUMSI ENERGI GEDUNG BERTINGKAT Yusvida, Rizka; Windarko, Novie Ayub; Setiawardhana, Setiawardhana
BRILIANT: Jurnal Riset dan Konseptual Vol 10 No 3 (2025): Volume 10 Nomor 3, Agustus 2025
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/briliant.v10i3.1967

Abstract

In an effort to manage and optimize the use of energy in these buildings, the ARIMA model (Autoregressive Integrated Moving Average) emerged as a very important analytical tool. The primary objective of this research is to investigate and explore how ARIMA parameter settings can be modified to improve the accuracy of energy consumption predictions on stairwell buildings. Based on an in-depth analysis of existing literature as well as empirical data, it was found that the ARIMA model, by leveraging time row kastasioneran and the use of univariate data, showed great potential in producing highly accurate short-term predictions. In this study, it was found that by performing the correct configuration of ARIMA parameters, the model was able to a level of accuracy with MAPE of 5,317% and RMSE of 8,7. These results show an excellent level of conformity, indicating that the ARIMA model can be effectively used to improve the accuracy of prediction of energy consumption in stairwell buildings. The findings of this study confirm that with proper adjustment of parameters, ARIMA can be a very useful tool in more efficient energy management in the building sector, which can ultimately contribute to reducing unnecessary energy consumption as well as improving overall energy efficiency.
Rancang Bangun Adaptive Neuro-Fuzzy Inference System (ANFIS) untuk Estimasi State-of-Charge (SOC) Baterai Salsabila, Regina; Windarko, Novie Ayub; Sumantri, Bambang
BRILIANT: Jurnal Riset dan Konseptual Vol 10 No 1 (2025): Volume 10 Nomor 1, Februari 2025
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/briliant.v10i1.2148

Abstract

The growing demand for energy around the world is driving the development of renewable resources, and batteries are the primary choice for energy storage. To carry out effective energy management, State of Charge (SOC) estimation of Lithium-ion batteries is essential. The development of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for SOC estimation using LG 18650 HG2 battery dataset is the objective of this research. It is tested with two parameters, namely two inputs consisting of voltage and current; and three inputs consisting of voltage, current, and temperature. The shape of the membership function, number of nodes, and epochs are some of the indicators tested to find the best configuration. The results show that the three-input configuration with generalized-bell membership function (Gbell MF), five nodes, and 100 epochs has the smallest Root Mean Square Error (RMSE), which is 0.0317, compared to the best two-input configuration, which has an RMSE of 0.0527. Since the three-input configuration takes longer to train, further improvements are needed for real-time implementations such as in electric vehicle battery management systems.
Indirect Sliding Mode Control Sebagai Pengendali Kecepatan Motor DC Dengan Buck-Converter Husnu Zain, Habibi Mushthofa; Windarko, Novie Ayub; Nugraha, Syechu Dwitya
Jurnal Teknologi Elektro Vol 14, No 3 (2023)
Publisher : Electrical Engineering, Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jte.2023.v14i3.005

Abstract

Sliding Mode Control merupakan salah satu kontrol non-linear yang banyak digunakan karena kesederhanaan dalam penerapannya dan dapat diterapkan pada sistem linear maupun non-linear. Modulasi pada Sliding Mode Control dapat menggunakan histerisis maupun PWM. Penelitian ini menjelaskan desain Sliding Mode Control dengan modulasi PWM yang digunakan untuk mengatur kecepatan motor dc menggunakan buck converter. Desain Sliding Mode Control dimulai dengan pemodelan dinamis yang kemudian direpersentasikan dalam bentuk state-space. Tahapan pencarian existance condition dan equivalent control juga akan dibahas. Hasil desain kontrol diverifikasi secara simulasi menggunakan MATLAB/Simulink dan secara eksperimen.
THD Minimization in Seven-Level Packed U-Cell (PUC) Inverter using Particle Swarm Optimization Amran, Osamah Abdullah Yahya; Windarko, Novie Ayub; Syarif, Iwan
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3352

Abstract

This study presents the modeling and simulation of an asymmetric seven-level Packed U-Cell (PUC) multilevel inverter employing a reduced number of power switches. A Modified Pulse Width Modulation (MPWM) scheme, optimized through the Particle Swarm Optimization (PSO) algorithm, is implemented to determine the optimal switching angles for enhanced harmonic elimination. The primary objective is to improve the output voltage waveform quality while reducing Total Harmonic Distortion (THD) and enhancing switching efficiency. The novelty of this work lies in integrating PSO with MPWM control in an asymmetric seven-level PUC inverter configuration with fewer switches, a combination that has not been previously addressed. Simulation results in Simulink demonstrate that the proposed PSO-optimized MPWM strategy achieves a THD of 17.72%, outperforming conventional modulation techniques. These findings highlight the effectiveness of intelligent optimization methods for multilevel inverter control and their potential contribution to improving power quality in renewable energy applications.
Implementasi Adaptive Neuro Fuzzy Inference System (ANFIS) untuk Estimasi Kecepatan Motor Induksi Ramdani, Dicky Rivaldo; Windarko, Novie Ayub; Purwanto, Era
BRILIANT: Jurnal Riset dan Konseptual Vol 10 No 4 (2025): Volume 10 Nomor 4, November 2025
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/briliant.v10i4.1990

Abstract

Three-phase induction motors are a major component in industrial machinery due to their simple construction, robustness, relatively low cost, easy maintenance, and high reliability. However, these motors have disadvantages in speed regulation due to their non-linear characteristics, which cause difficulties in maintaining a constant speed when the load changes. This research aims to maintain the speed of induction motors to remain constant by using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method.  This ANFIS method goes through the stages of data collection, data processing, ANFIS system design, ANFIS training, validation testing, and finally analyzing the results. The input of the induction motor speed estimation system is through a mathematical equation model in d-q coordinates. The output of this system is speed. In this study, training and testing variations. The smallest RMSE result obtained is with the Trapzeium membership function architecture with the number of epochs 100 of 0.0187542.
MPPT algorithm based on modified remora optimization algorithm for photovoltaic systems under partial shading conditions Efendi, Moh. Zaenal; Adnaurrosyid, Akhmad; Habibi, Muhammad Nizar; Eviningsih, Rachma Prilian; Windarko, Novie Ayub; Jati, Mentari Putri
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.1257

Abstract

The increasing electricity demand, driven by the growing human population, has led to the need for efficient backup power sources. Solar panels are one of the renewable energy sources that have been widely developed because they only require solar energy as their primary source. However, the phenomenon of partial shading is often a problem in solar panels because it can reduce the output power of the solar panel system, which is caused by shadows from trees or clouds. In this condition, conventional maximum power point tracking (MPPT) algorithms are often limited to the local maximum power point (LMPP). To effectively attain the global maximum power point (GMPP), it is imperative to devise more efficient algorithms. The modified remora optimization algorithm (MROA) has been proposed as a potential solution to this challenge. MROA is an adaptation of the remora optimization algorithm (ROA), inspired by the behavior of remora fish. The results indicate that the algorithm achieves an average accuracy of approximately 99.13 % in both simulation and hardware implementations. Furthermore, when comparing the results of the MROA with those of the original ROA method and particle swarm optimization (PSO), the MROA exhibited superior accuracy, tracking time, and power gain, suggesting that the MROA algorithm effectively circumvents the limitation of the local maximum power point.
Tunicate swarm algorithm based maximum power point tracking for photovoltaic system under non-uniform irradiation Sholikhah, Evi Nafiatus; Windarko, Novie Ayub; Sumantri, Bambang
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4559-4570

Abstract

A new maximum power point tracking (MPPT) technique based on the bio-inspired metaheuristic algorithm for photovoltaic system (PV system) is proposed, namely tunicate swarm algorithm-based MPPT (TSA-MPPT). The proposed algorithm is implemented on the PV system with five PV modules arranged in series and integrated with DC-DC buck converter. Then, the PV system is tested in a simulation using PowerSim (PSIM) software. TSA-MPPT is tested under varying irradiation conditions both uniform irradiation and non-uniform irradiation. Furthermore, to evaluate the performance, TSA-MPPT is compared with perturb & observe-based MPPT (P&O-MPPT) and particle swarm optimization-based MPPT (PSO-MPPT). The TSA-MPPT has an accuracy of 99% and has a reasonably practical capability compared to the MPPT technique, which already existed before.
AC-DC PFC Converter Using Combination of Flyback Converter and Full-bridge DC-DC Converter Efendi, Moh. Zaenal; Rizal, Abdul; Erzanuari, Aldi; ., Suryono; Windarko, Novie Ayub
EMITTER International Journal of Engineering Technology Vol 2 No 1 (2014)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (6864.867 KB) | DOI: 10.24003/emitter.v2i1.21

Abstract

This paper presents a combination of power factor correction converter using Flyback converter and Full-bridge dc-dc converter in series connection. Flyback converter is operated in discontinuous conduction mode so that it can serve as a power factor correction converter and meanwhile Full-bridge dc-dc converter is used for dc regulator. This converter system is designed to produce a 86 Volt of output voltage and 2 A of output current. Both simulation and experiment results show that the power factor of this converter achieves up to 0.99 and meets harmonic standard of IEC61000-3-2.Keywords: Flyback Converter, Full-bridge DC-DC Converter, Power Factor Correction.
Co-Authors - Sutedjo Abdilla, M. Rafi Damas Abdilla, Moch Rafi Damas Abdul Rizal, Abdul Abdurrahman, Rizqy Abdussalam, Muhammad Zayyana ACHMAD AFANDI Achmad Afandi, Achmad Adnaurrosyid, Akhmad Ahmad Firyal Adila Akhmad Puryanto Aldi Erzanuari, Aldi ALDIANTAMA, GIGIH HERNAIN NANDA Alvin Noer Ramadhan Alwi Daffa` Rosydi Amran, Osamah Abdullah Yahya Amrullah, Haniifan Patra Anang Tjahjono, Anang Anggara Trisna Nugraha Arief Rahmadani Arini, Nu Rhahida Armanto, Ony Ashary, Wima Bambang Sumantri Bambang Sumantri Bambang Sumantri Bima Dwi Priya Setiawan Diah Septi Yanaratri Dimas Nur Prakoso Dimas Okky Anggriawan Ebrahimi, Faizulddin Efendi, M. Zaenal Eka Prasetyono, Eka Endro Wahjono Epyk Sunarno Era Purwanto Evi Nafiatus Sholikhah Fahmi Ahyar Izzaqi Fakhruddin, Hanif Hasyier Ferdiansyah, Indra Firmansyah Nur Budiman, Firmansyah Nur Fuad, Muchamad Chaninul Fuad, Muchamad Chaninul Gede Patrianaya Margayasa Wirsuyana Gunawan, Agus Indra Habibi, Muhammad Nizar Hasnira Hasnira Hazlie Mokhlis Husien.R, Alwi Azis Husnu Zain, Habibi Mushthofa Irianto Irianto iwan Syarif Kadek Reda Setiawan Suda Khafidli, Muhammad Khanif Kuswadi, Son Lestyanto, Dicky Satria Nanda Loegimin, Maruto Swatara Lucky Pradigta Setiya Raharja Lucky Pradigta Setiya Raharja Luki Septya Mahendra Luluk Badriyah Mas Sulung Wisnu Jati Mentari Putri Jati Miftahul Arrijal MOCHAMAD ARI BAGUS NUGROHO Mochammad Ari Bagus Nugroho Moh Rifqi Faqih Moh. Faisal Amir Moh. Faisal Amir Moh. Zaenal Efendi Mohammad Imron Dwi Prasetyo Mohammad Imron Dwi Prasetyo Mohammad Zaenal Efendi Muchammad Ruben Imawan Muhammad Abdul Haq Muhammad Farizky Alvianandy Muhammad Prihadi Eko Wahyudi Muhammad Wildan Alim Muhtar, Muhdalifah Naafilah Widya Mulya Nugraha, Syechu Dwitya Prakoso, Rifqi Noviantono Prasetya, Farhan Dwi Puspita Ningrum Q., Ony Asrarul Q., Ony Asrarul Qoriatul Fitriyah Qudsi, Ony Asrarul Rachma Prilian Eviningsih Rachma Prilian Eviningsih Rachma Prilian Eviningsih, Rachma Prilian Ragil Wigas Wicaksana Ramdani, Dicky Rivaldo Renny Rakhmawati, Safira Nur Hanifah, Renny Rakhmawati, Rheinanda, Audya Elisa Rizal Nurdiansyah Rizqy Abdurrahman Romadloniyah, Nur Shinta S Aisyah Salsabila, Regina Setiawardhana Setiawardhana, Setiawardhana Sholikhah, Evi Nafiatus Sony Junianto Suhariningsih Suhariningsih Suryono . Suryono Suryono Suryono Suryono Sutedjo Sutedjo Ummah, Karunia Vio Nita Rusyatul Wicaksana, Ragil Wigas Wima Ashary Wirsuyana, Gede Patrianaya Margayasa Wishnuprakasa, Ardhia Wishnuprakasa, Ardhia Yusvida, Rizka Zainal Arief