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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
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Design and analysis for robotic arm position for automatic electric vehicle Kharde, Mukund Ramdas; Kalam, Sayyad Abdul; Teku, Kalyani; Reddy, Thumu Srinivas; Satya Srinivas, Gollapalli Veera; Kollamudi, Pavani; Fariddin, Shaik Baba; Kumar, Gopinati Pranay
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1517-1526

Abstract

Nowadays electric vehicles (EV) utilization is increasing. Because of charging issues, EVs are troubling people at the time of the journey because of the lack of charging stations. Therefore, to overcome these issues, robotic arm position for automatic electric vehicle is introduced in this analysis. This vehicle is operated through solar, so charging issues are overcome. The robotic arm position for automatic electric vehicle is fully automated by 4 infrared radiation (IR) sensors, which are placed in variations, back and other sides with particular speed limit variations, so that accidents can be avoided. The Flux in hand gloves can operate without manual operation while driver is sleeping. This analysis uses Raspberry Pi, python software with machine learning (ML) algorithm (support vector machine). Hence, this robotic arm position for automatic electric vehicle shows better results in terms of charging issues, accident ratio and driver presence.
Implementation of an app-controlled robotic arm to optimize loading processes in Callao-Peru Gómez-Huamán, Javier Junior; Castro-Vargas, Cristian
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1591-1601

Abstract

Efficient handling of heavy loads represents a constant challenge for businesses, which traditionally rely on significant numbers of staff, involving considerable financial costs and occupational health risks exacerbated by the need for specialized infrastructure. Despite technological limitations and structural deficiencies, this solution has prevailed in practice. However, engineering has responded with innovations aimed at optimizing these processes. In this context, the study proposes to adopt an approach based on implementing a robotic arm supported by technologies such as Arduino, Bluetooth devices, servo motors, and remote-control software developed in App Inventor. This approach promises not only the reduction of labor costs and the improvement of job security but also a positive impact in social and economic terms. A preliminary prototype is presented that validates the basic functionality of the proposed robotic arm. This study presents a technically and economically viable alternative for managing heavy loads in enterprise environments, reducing dependence on a large workforce, and improving operational efficiency.
Design and development of an automated spirulina (Arthrospira platensis) algae cultivator Mariñas II, Miguel Q.; B. Enojas, Mark Joseph; Balolong, Daryll C.; B. Correa, Charissa Zandra; C. Roldan, Lemmuel Keith; Teves, Mark Lester; Dela Cruz, Christian Mari
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp139-147

Abstract

The cultivation of algae has gotten more attention from algae enthusiasts who have seen the benefits of algae in many uses. To maximize productivity, the parameters for growth of this algae must be controlled, such as pH, turbidity, light intensity, and the mixture solution for optimal growth. In this paper, an automated spirulina algae cultivator is designed and developed in a small-scale pond to replace the existing manual process. The system developed is composed of compact and modular cultivation unit, ph sensor, water level sensor, turbidity sensor, light intensity sensor, and motor actuators for mixing solutions. Each parameter was controlled individually in an on-off control system. A simple nutrient addition program (SNAP) solution is also used for better growth productivity by maximizing its nutrient contents. The pH is maintained at 9 to 12 for a healthy biomass output. Daily weight measurement was conducted using an analytical balance to monitor its growth. Using the developed prototype recorded a 33% higher rate of productivity over the manual process. This setup can potentially be used as a model for mass production of spirulina algae.
Robust k-NN approach for classifying Aquilaria oil species by compounds Ahmad Sabri, Noor Aida Syakira; Syafiqah Noramli, Nur Athirah; Nik Kamaruzaman, Nik Fasha Edora; Ismail, Nurlaila; Yusoff, Zakiah Mohd; Almisreb, Ali Abd; Tajuddin, Saiful Nizam; Taib, Mohd Nasir
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp178-189

Abstract

Accurate classification of Aquilaria oil species is essential for ensuring the quality and authenticity of agarwood oils, which are widely used in perfumes and traditional medicine. This study investigated the effectiveness of the k-nearest neighbours (k-NN) machine learning model for classifying Aquilaria oil species based on four significant chemical compounds: dihyro-βagarofuran, δ-guaiene, 10-epi-γ-eudesmol, and γ-eudesmol. The dataset comprised 480 samples of Aquilaria oil, which were analyzed using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID). The k-NN model, with an optimal k-value of 10 and using euclidean distance as the distance metric, achieved 100% accuracy, sensitivity, specificity, and precision in both training and testing datasets. These results demonstrate the robustness of k-NN in species identification, highlighting the discriminative power of the selected compounds. This study verifies that the integration of chemical profiling with machine learning offers a scalable solution for accurate species identification in the essential oil industry. Future work could explore hybrid models and data expansion techniques to further enhance the classification performance in more complex environmental conditions.
A comparative study on electricity load forecasting using statistical and deep learning approaches Butt, Tehreem Fatima; Tameer, Sana; Saleem, Muhammad; Ur Rehman, Jawwad Sami; Selvaperumal, Sathish Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1540-1552

Abstract

Load forecasting has become reproving aspect of an energy management system (EMS). It gives basic advantage to grid stability, cost effectiveness and battery storage system (BSS). For this purpose, machine learning (ML) is widely adopted to forecast the electricity load. This research paper investigates the performances of various time series estimating models applied to electricity load data for an Irish company. The research mainly adopts the autoregressive integrated moving average (ARIMA) model, long short-term memory (LSTM) networks and transformer neural network (TNN) to forecast the electricity load. A comparison evaluation is conducted encompassing various quantifying measures such as root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE). The results are then compared to get an understanding whether the TNN using attention-based mechanism is better than the two state of the art models. Hence provides a complete understanding about which of the model needs improvements in its architecture for enhancement of operational efficiency and cost effectiveness in the realm of EMS.
Influences from SiO2 particles on optical properties of white diodes verified through computer simulation Trang, Le Thi; Quoc Anh, Nguyen Doan
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1572-1579

Abstract

For typical white-illumination diodes (WLEDs) based on phosphor conversion, heat consistency would be an essential element in phosphor samples, which is based on consistent discharge intensity, apex profile, as well as location as the samples function under different heat levels. With the goal of attaining desirable heat consistency, the study herein concerns the thermic mechanism in different phosphor samples singularly or dualincorporated with Ce3+ and Eu2+. Based on our acquired data, the luminescent features for the samples exhibit copious alterations when subject to different heat levels, primarily decided by phosphor bases’ crystalline formation. The assessment of the interaction among the thermic mechanism and base latticework in the samples suggest that a merger between firm crystalline formations and symmetrical locations would result in desirable thermic consistency in samples. As such, the study herein also assesses a number of formations possessing firm foundations as well as specific approaches for avoiding thermic irregularities in phosphor samples, aiming to identify reliable samples as well as approaches for augmenting heat consistency for said samples.
Speed drives control using particle swarm optimization for PMSM drives Mat Lazi, Jurifa; Nizam Talib, Md Hairul; Bin Kasdirin, Hyreil Anuar; Bin Hashim, Mohd Ruzaini; Alias, Azrita
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1440-1449

Abstract

The paper presents a contemporary method for controlling the speed of a permanent magnet synchronous machine (PMSM) by optimizing the parameters of a proportional-integral (PI) controller using the particle swarm optimization (PSO) algorithm. This approach aims to enhance the robustness and dynamic performance of the drive system, resulting in improved accuracy and sensitivity to load changes and wide range of speed. The study evaluates two tuning techniques for the PI controller, which are the traditional trial-and-error method and the PSO optimization method. The performance of the PMSM is assessed based on speed response performance, including rise time, overshoot, and settling time. The PSOtuned controller significantly minimizes overshoot compared to the trialand-error method. And also achieves a shorter settling time, indicating a more stable response. However, the rise time is slightly longer with the PSO-tuned controller compared to the conventional tuning method just for the medium speed. For the rated speed, PSO still having shorter rise time compared to trial-and-error PI method. These findings imply that while the PSO method may result in a longer rise time, its overall advantages in reducing overshoot and settling time make it a more effective option for speed control in PMSMs. This is consistent with other research suggesting that PSO can outperform traditional methods in optimizing control parameters across various applications.
Hybrid energy storage solutions through battery-supercapacitor integration in photovoltaic installations Yousfi, Abdelkader; Mehedi, Fayçal; Bot, Youcef
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp11-22

Abstract

Batteries integrated into renewable energy storage systems may experience multiple irregular charge and discharge cycles due to the variability of photovoltaic energy production characteristics or load fluctuations. This could negatively impact the battery’s longevity and lead to an increase in project costs. This article presents an approach for the sharing of embedded energy between the battery, which serves as the main energy storage system, and the supercapacitors (SC), which act as an auxiliary energy storage system. By delivering or absorbing peak currents according to the load requirements, supercapacitors increase the lifespan of batteries and reduce their stresses. An maximum power point tracking (MPPT) algorithm regulates the connection of the photovoltaic (PV) cells to the DC bus through a boost converter. A buck-boost converter connects supercapacitors and batteries to the DC bus. A DC-AC converter connects the inductive load to the DC bus. The system regulates static converters connected to batteries and supercapacitors based on current. An energy management block supervises the system components. We implement the entire system in the MATLAB/Simulink environment. We present the simulation results to demonstrate the effectiveness of the proposed control strategy for the entire system.
A comparative analysis of hybrid of traditional load flow methods for IEEE distributed power generation networks Mohamed Hariri, Muhammad Hafeez; Daud, Noor Dzulaikha; Mohd Yusoff, Nor Azizah; Syed Zaman, Syed Muhammad Zakwan; Mat Desa, Mohd Khairunaz
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp33-44

Abstract

Analyzing power flow or load flow is crucial for planning, operating, maintaining, and controlling electrical power systems. Two traditional power flow methods namely the Newton-Raphson (NR) method are known for their accuracy and robustness nevertheless high computational intensity, and the fast decoupled load flow (FD) method, is valued for its computational efficiency and speed, however, generating less accurate data. This research aims to develop a hybrid load flow technique that integrates both strengths, achieving higher accuracy and faster convergence. The validation processes are based on several IEEE standard bus systems, including the 3-bus, 9-bus, 14-bus, and 30-bus systems. These systems, with different bus types and interconnections, represent real-world operations and help generate comprehensive data on iteration count, execution time, and the accuracy of the output data results. A new hybrid method generated from this research work compared to traditional load flow methods, provides a substantially well-balanced number of iteration counts, the fastest execution times, improved by 41.55%, and produces a similar accuracy of the data set. These improvements make the hybrid method highly advantageous in practical real-time applications and large-scale systems where both accuracy and speed are critical.
Advancing SSVEP-based brain-computer interfaces: a novel approach using cross-subject multi-modal fusion technique Swetha, Kalenahally R.; Krishnegowda, Ravikumar G.; Venkataramu, Shashikala S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1755-1764

Abstract

Brain-computer interfaces (BCIs) represent an innovative paradigm for device control and communication, relying solely on the analysis of brain activity. Steady-state visually evoked potentials (SSVEPs), characterized by neurophysiological responses synchronized with periodic visual stimuli, have gained prominence in BCI research due to their high information transfer rates (ITRs) and minimal user training requirements. However, the translation of SSVEP-based BCIs into practical applications faces challenges stemming from variations in user responses and stimuli. To address these issues, this study introduces a groundbreaking methodology known as the cross-subject multi-modal fusion technique (CMFT). CMFT revolutionizes template design by creating invariant templates resilient to user and stimulus differences, thereby enhancing SSVEP detection across diverse subjects and stimuli. The implications of this research extend to various fields, including assistive technologies, human-computer interaction, and cognitive neuroscience. CMFT presents a promising solution to make SSVEP-based BCIs more practical and widely applicable. The methodology involves intricate steps, including spatial filters, data pre-processing, and template generation, ensuring precise SSVEP detection. Through CMFT, this study contributes to advancing the effectiveness and versatility of SSVEP-based BCIs, fostering improved accessibility and interaction in a range of domains.

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