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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.
Co-Authors - Sutedjo Abdilla, M. Rafi Damas Abdilla, Moch Rafi Damas Abdul Rizal Abdul Rizal, Abdul Abdurrahman, Rizqy Abdussalam, Muhammad Zayyana ACHMAD AFANDI Achmad Afandi, Achmad Ahmad Firyal Adila Akhmad Puryanto Aldi Erzanuari 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 Ardhia Wishnuprakasa Arief Rahmadani Arini, Nu Rhahida Armanto, Ony Ashary, Wima Audya Elisa Rheinanda 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 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 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 Muchamad Chaninul Fuad Muchammad Ruben Imawan Muhammad Abdul Haq Muhammad Farizky Alvianandy Muhammad Khanif Khafidli Muhammad Prihadi Eko Wahyudi Muhammad Wildan Alim Muhtar, Muhdalifah Naafilah Widya Mulya Nugraha, Syechu Dwitya Ony Asrarul Q. Prakoso, Rifqi Noviantono Prasetya, Farhan Dwi Puspita Ningrum Q., Ony Asrarul Qoriatul Fitriyah Qudsi, Ony Asrarul Rachma Prilian Eviningsih Rachma Prilian Eviningsih Rachma Prilian Eviningsih, Rachma Prilian Ragil Wigas Wicaksana Renny Rakhmawati, Safira Nur Hanifah, Renny Rakhmawati, Rizal Nurdiansyah Rizqy Abdurrahman Romadloniyah, Nur Shinta S Aisyah Salsabila, Regina Setiawardhana Setiawardhana, Setiawardhana 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 Yusvida, Rizka Zainal Arief Zainal Arief