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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 66 Documents
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Search results for , issue "Vol 35, No 2: August 2024" : 66 Documents clear
A perspective on smart universities as being downsized smart cities: a technological view of internet of thing and big data Abdul Jawwad, Abdul Kareem; Turab, Nidal; Al-Mahadin, Ghayth; Owida, Hamza Abu; Al-Nabulsi, Jamal
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1162-1170

Abstract

The integration of internet of things (IoT) and big data technologies is transforming the overall perspective of managing various sectors of modern life; with higher educational sectors being no exception of this transformation. This paper explores the idea of a “smart university” as an extension of the overarching “smart city” framework, emphasizing the blending of IoT and big data technologies within higher education institutions. The study investigates the incorporation of IoT technologies throughout university campuses, including intelligent classrooms, smart infrastructure, and device networking. Moreover, the paper delves into the substantial role played by big data analytics in processing and extracting meaningful insights from extensive data generated by IoT devices in a Smart University. The use of predictive analytics, machine learning algorithms, and data-driven decision-making contributes to personalized learning experiences, adaptive campus management, and proactive maintenance of university facilities. Furthermore, this paper not only emphasizes the potential benefits of deploying IoT and big data in a university setting but also addresses challenges related to security, privacy, and ethical considerations. By embracing a comprehensive approach to technology integration, universities can leverage the capabilities of IoT and big data to establish intelligent, interconnected, and flexible educational environments that align with the broader vision of a smart city.
Fog computing in classrooms: boosting efficiency, responsiveness, user experience Hasanuddin, Tasrif; Hadi, Mokh Sholihul; Sujito, Sujito; Rosnani, Rosnani
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1287-1295

Abstract

In the context of rapidly advancing smart education systems, the effective management and optimization of modern classroom remain critical challenges. This research presents a novel methodology leveraging cloud and fog computing-based simulations, with a specific focus on the implementation of iFogSim. Empirical findings validate the efficacy of fog computing in monitoring classrooms, demonstrating significant improvements in performance metrics compared to traditional cloud computing architectures. Specifically, fog computing ensures remarkably low latency, with a mere 7 milliseconds, even with scalable integration across multiple classrooms. In contrast, cloud computing infrastructures exhibit considerably higher initial latencies, starting at 210 milliseconds, which further escalate with the increasing number of monitored classrooms. Furthermore, our analysis reveals substantially lower network overhead associated with fog computing, measuring at 5,231.8 kilobytes, in sharp contrast to the significantly higher network usage of 80,808 kilobytes observed with cloud computing solutions. These findings underscore the potential of fog computing as a promising solution for efficient and real-time management classroom in smart education environments.
Big data analysis and its impact on the marketing industry: a systematic review Patricio-Peralta, Cesar; Mondragon, Jesús Zamora; Terrones, Luis Segura; Villacorta, Jimmy Ramirez
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1032-1040

Abstract

This systematic review focused on understanding the impact of big data on marketing productivity, following the guidelines of systematic literature reviews and using the PICOC (problem/population, intervention, comparison, results, context) method. 50 high-impact articles were selected in Scopus, prioritizing those in the areas of engineering, computer science and business, and published between 2020 and 2023. These articles, selected for their relevance and contribution to the study objectives, showed that the big data offers notable benefits in the marketing industry. The ability to customize marketing strategies to individual customer needs, improved optimization, and a better understanding of customer behaviors and preferences were key aspects. These findings highlight how big data can boost productivity in marketing, strengthening customer relationships and increasing loyalty by improving understanding and adaptation to the specific demands and preferences of each customer. This deeper, more personalized approach to consumers represents a significant shift in the effectiveness and efficiency of marketing strategies in the current era.
Improving Kui digit recognition through machine learning and data augmentation techniques Nayak, Subrat Kumar; Nayak, Ajit Kumar; Mishra, Smitaprava; Mohanty, Prithviraj; Tripathy, Nrusingha; Prusty, Sashikanta
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp867-877

Abstract

Speech digit recognition research is growing decisively, and a bulk of digit recognition algorithms are used in European and a few Asian languages. Kui is a low-resourced tribal language locally used in several states of India. Despite its significance, there is not much research on Kui's speech. This research aims to present an in-depth analysis of novel Kui digit recognition using predefined machine learning (ML) techniques. For this purpose, we first gathered spoken numbers i.e. from 0 to 9 of eight different speakers containing a total of 200 words. Secondly, we choose the numbers: ଶୂନ (zero), ଏକ (one), ଦୁଇ (two), ତିନି(three), ସାରି(four), ପାସ (five), ସଅ (six), ସାତ (seven), ଆଟ (eight), ନଅ (nine). Meanwhile, we build nine different ML models to recognize Kui digits that take the Mel-frequency cepstral coefficients (MFCCs) method to extract the relevant features for model predictions. Finally, we compared the performance of ML models for both augmented and non-augmented Kui data. The result shows that the SVM+Augmentation method for Kui digit recognition combined obtained the highest accuracy of 83% than other methods. Moreover, the difficulties and potential prospects for Kui digit recognition are also highlighted in this work.
Investigations of BLDC motor speed characteristics via THD under conventional and advanced hybrid controllers Chaitanya Kumar Reddy, Kamatam Muni Naga; Kanagasabai, Nallathambi
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp729-742

Abstract

This project investigates brushless direct current (BLDC) motor speed control through total harmonic distortion (THD) analysis, employing proportional integral (PI), fuzzy logic (FLC), adaptive neuro-fuzzy inference system (ANFIS), and an innovative hybrid ANFIS-PD/PI controller. Considering the vital role of BLDC motors in precision-dependent industries like robotics, electric vehicles, and industrial automation, our primary focus is on understanding BLDC motor operation and recognizing THD's significance as a performance metric. Controllers are meticulously implemented in real-time, fine-tuned, and optimized to achieve desired speed characteristics, incorporating considerations like response time, accuracy, and energy efficiency. The project's core involves THD analysis, quantifying harmonic content in the BLDC motor's speed waveform. This facilitates a comprehensive comparative evaluation of controller performance, assessing their capability to maintain speed stability and influence power quality. The discussion covers the merits and limitations of each controller, with a special emphasis on the hybrid ANFIS-PD/PI controller, seamlessly blending ANFIS adaptability with PD/PI control stability. Results illustrate the hybrid controller's excellence in optimizing BLDC motor speed control, demonstrating superior performance in speed accuracy, disturbance rejection, and THD reduction. These findings drive advancements in motor control technology, providing practical guidance for selecting controllers tailored to specific application requirements. Simulation results can be analyzed using MATLAB/Simulink 2018a Software.
R-based neural networks decision model for water air cooler system Mohammed, Haidas; Soumia, Kerrache
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1091-1098

Abstract

Water air cooler is a process that uses an evaporative system. In its first generation, there was no temperature sensor, and its fan was turned by an electric motor in an open loop system with a fixed speed. As well, the water’s flow and level in the tank are governed by a mechanical system, which is generally a floating ball attached to a shaft. In order to ameliorate this classical system with more advantages and performances, many ideas are included in subsequent generations such the integration of embedded systems, intelligent control and components of manufacturing materials. In this paper, the current study aims to integrate an intelligent system, which is the neural networks by using R language to give a smart decision model to command relays switching dedicated to control the electric motors, where the first one is tied up with a fan and the other to an electro-pump. The HC-SR04 ultrasonic and DHT11 sensors supervise the two desired parameters control, water level in the tank and the outside temperature successively.
Recognizing gender from images with facial makeup Micheal, Annie; Palanisamy, Geetha
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1201-1209

Abstract

Recognizing the sex of an individual is a difficult task due to pose variation, occlusion, illumination effect, facial expression, plastic surgery, and makeup. In this manuscript, a novel approach for gender recognition with facial makeup is proposed. A novel Log-Gabor COSFIRE (LG-COSFIRE) filter is a shape-selective filter that is trained with prototype patterns of interest. The geometrical structure of the faces is acquired using the dual-tree complex wavelet transform (DT-CWT). Dense SIFT descriptor extracts the shape attributes of an image by building local histograms of gradient orientation. Finally, least square support vector machine (LS-SVM) is utilized to recognize the gender of an individual. The experiment was performed on self-built facial makeup for male and female (FMMF) database and achieves 89.7% accuracy.
Evaluating machine learning models for precipitation prediction in Casablanca City Tricha, Abdelouahed; Moussaid, Laila
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1325-1332

Abstract

Accurate precipitation forecasting is a vital task for many domains, such as agriculture, water management, flood prevention, and crop yield estimation. The use of machine learning (ML) approaches has improved precipitation forecasting accuracy, exhibiting promising results in capturing the intricate connections between various meteorological variables and precipitation patterns. However, given the vast array of available ML models, a comparative analysis is imperative for identifying the most effective models for precipitation prediction. This study aims to examine the capacities of ML algorithms to forecast precipitation based on weather data for the city of Casablanca, Morocco, which faces challenges in water management and climate change adaptation. Eight different ML models’ performances are compared: linear regression, polynomial regression, K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), XGBoost, and an ensemble learning model. These models are evaluated based on their mean absolute error (MAE), mean squared error (MSE), and R-squared (R2 ) value to determine their effectiveness. The study showcases the potential of ML models in predicting precipitation by utilizing meteorological parameters such as temperature, humidity, wind speed, and pressure.
Dual tri-isolated DC H-6 inverter with minimal power components design Maheswaran, Anusuya; Ramanujam, Geetha; Rengaraju, Ilango; Arumugam, Iyyappan S.; Ramachandran, Gayathri
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp711-719

Abstract

Multilevel inverters have been forecasted in recent years for industrial and renewable energy applications due to its inherent characteristics of shaping the output voltage nearer to sinusoidal shape through concatenating several two/three level inverters using isolated DC sources or DC-link capacitors. However, the classical topologies used for synthesizing stepped voltage have several outwards like more number of DC sources or DC-link capacitors and switching devices used. In this paper, an effort has been sighted to bring a new topology for generating stepped voltage to overcome the above mentioned demerits. In addition to this, a new digital pulse width modulation (PWM) strategy is developed in-line with a new topology to eliminate the use of carrier and reference signals. The performance of the proposed topology and developed control strategy are evaluated in MATLAB/ Simulink platform and an laboratory prototype is constructed for experimental investigations to accord the simulation results.
Forecasting livestock feed sales using machine learning techniques: an analysis of the Moroccan market Nebri, Mohamed Amine; Moussaid, Abdellatif; Bouikhalene, Belaid
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1139-1150

Abstract

Agriculture, especially cereals, is important in sustaining economies and food security globally. This study delves into the Moroccan agricultural landscape, specifically focusing on predicting livestock feed sales to assist cereal company producers in optimizing production, streamlining supply chain operations, and enhancing customer satisfaction. Data collected from various markets across Morocco, including sales dates and locations, was combined with climate data and analyzed using advanced machine learning techniques, particularly the gradient boosting regression (GBR) algorithm, which achieved high accuracy with a mean absolute error (MAE) of 0.0203 and a root mean square error (RMSE) of 0.0281. The evaluation of multiple regression models revealed promising results, demonstrating the effectiveness of predictive models in accurately forecasting sales. These findings contribute valuable insights to sales forecasting in the cereal industry by considering weather conditions, production methods, and livestock-related variables, highlighting the importance of leveraging advanced machine learning techniques for optimizing production processes and meeting market demands efficiently in the agribusiness sector.

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