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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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Challenges in big data adoption for Malaysian organizations: a review Lee Qi Zian; Nur Zareen Zulkarnain; Yogan Jaya Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp507-517

Abstract

Big data has played an ever-increasing role in various sectors of the economy. Despite the availability of big data technologies, many companies and organizations in Malaysia remain reluctant to adopt them. Numerous studies have been published on big data adoption; however, there is a lack of research focusing on identifying the challenges faced by Malaysian organizations. Therefore, this study will implement the technology-organization-environment (TOE) framework to examine the challenges faced by Malaysian organizations with regards to big data adoption. A systematic literature review (SLR) was conducted to examine the challenges. From the result of this study, it was found that the factors from technology context are deemed to be the major challenge faced in big data adoption followed by organization and environment factors. Furthermore, the insights derived from the TOE framework-based information can help address concerns that hinder big data adoption among organizations in Malaysia. Finally, this study concludes with several recommendations.
Enhancing machine learning algorithm performance through feature selection for driver behavior classification Bouhsissin, Soukaina; Sael, Nawal; Benabbou, Faouzia; Soultana, Abdelfettah
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp354-365

Abstract

Machine learning (ML) techniques empower computers to learn from data and make predictions or decisions in various domains, while preprocessing methods assist in cleaning and transforming data before it can be effectively utilized by ML. Feature selection in ML is a critical process that significantly influences the performance and effectiveness of models. By carefully choosing the most relevant and informative attributes from the dataset, feature selection enhances model accuracy, reduces overfitting, and minimizes computational complexity. In this study, we leverage the UAH-DriveSet dataset to classify driver behavior, employing Filter, embedded, and wrapper methods encompassing 10 distinct feature selection techniques. Through the utilization of diverse ML algorithms, we effectively categorize driver behavior into normal, drowsy, and aggressive classes. The second objective is to employ feature selection techniques to pinpoint the most influential features impacting driver behavior. As a results, random forest emerges as the top-performing classifier, achieving an impressive accuracy of 96.4% and an F1-score of 96.36% using backward feature selection in 7.43 s, while K-nearest neighbour (K-NN) attains an accuracy of 96.29% with forward feature selection in 0.05 s. Following our comprehensive results, we deduce that the primary influential features for studying driver behavior include speed (km/h), course, yaw, impact time, road width, distance to the ahead vehicle, vehicle position, and number of detected vehicles.
A low-cost dual bandpass planar filter for WiMAX and mobile communications Amal Kadiri; Abdelali Tajmouati; Jamal Zbitou; Ahmed Lakhssassi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp757-766

Abstract

This study proposes a new design of low-cost dual-bandpass filter for worldwide interoperability and microwave access (WiMAX) band at 3.50 GHz and mobile communications band at 1.19 GHz. A high pass filter and a stopband filter make up the new dual-bandpass filter structure. Different theoretical studies were carried out for the design of the proposed filter. This filter’s base is a RO5880 substrate with a dielectric permittivity constant of 2.2, loss tangent of 0.0009 and 1.6 mm thickness. High mashing density was used to validate the various simulated structures while accounting for two numerical methods: the moment technique and the finite integration method. The final circuit's overall dimensions are 60×178,3 mm2.
Internet of things-based garbage monitoring system integrated with Telegram Siti Nur Syuhada Ahmad Tarmizi; Nik Nur Shaadah Nik Dzulkefli; Rina Abdullah; Syila Izawana Ismail; Suziana Omar
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1370-1377

Abstract

This paper presents the development of smart garbage monitoring system usin g internet of things (IoT) to keep the environment clean thus reducing cleaners’ burden. The present era is characterized by smart cities, where precision and organization are the norm. This initiative was launched because population is progressing rapidly, increasing more garbage hence esclating cleaner’s frequency of dustbin checking daily whether the dustbin is full or not which mean more labour costs. The main purpose of this researc h is to develop a systematic garbage monitoring system which can help cleaners schedule their work in monitoring and picking up garbage from dustbins. It used node microcontroller (NodeMCU) ESP8266 Wi - Fi module as the main controller to control ultrasonic and rain input sensors and provide notifications via Telegram. A limit switch is used to detect whether the lid is open or closed. When the lid is closed, the ultrasonic sensor is activated and measures the garbage distance depending on the amount. If an overrun of the maximum amount is detected, the red - light emitting diode (LED) will turn on that connects to the Wi - Fi module, which sends notification to the cleaners. As a result, the IoT based garbage monitoring system was fully functioned and accomplish ed its objectives.
Potential microgrid model based on hybrid photovoltaic/wind turbine/generator in the coastal area of North Sumatra Habib Satria; Rahmad Syah; Dadan Ramdan; Muhammad Khahfi Zuhanda; Jaka Windarta; Syafii Syafii; Almoataz Youssef Abdelaziz
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp768-776

Abstract

The high potential for renewable energy in the North Sumatra region, especially the coast of the Belawan area, needs to be exploited properly. The design that will be carried out is to explore the potential in coastal areas by simulating microgrid systems and hybrid system-based electricity installations. The method that will be used is to find the accuracy of strategic location points by considering the panel surface temperature which will later influence the power output of the power plant. Then find the ideal installation location as a reliable system when irregular climate conditions occur, of course this phenomenon will have a significant effect on energy balance and energy conversion, especially in coastal areas. The potential for installation construction will be carried out with a hybrid system using power sources from photovoltaics, wind turbines and diesel generators assisted by HOMER Pro software. The results of testing with simulations and information data that have been recorded in the software can later be used as a benchmark in planning electrical installations and also for identifying microgrid protection challenges. Then the measurement results that have been obtained for the installation of a hybrid-based microgrid system on Photovoltaic (PV) are DC output power of 618.80 W with measurements of sunny weather conditions, then the potential wind speed on the wind turbine reaches 5 m/s and the potential use of a diesel generator reaches 40% with power output capacity 1 kW.
Fuzzy expert system design for detecting stunting Linda Perdana Wanti; Oman Somantri; Nur Wachid Adi Prasetya; Lina Puspitasari
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp556-564

Abstract

Stunting is a chronic nutritional problem that occurs in toddler due to lack of nutritional intake which results in impaired growth toddler. Usually, toddler who experience stunting are characterized by not increasing weight over a long period of time. Application utilization health which makes it easier for users to access information, one of which can be used to identify toddler who are stunted by selecting symptoms. The symptoms experienced by toddlers go through a system known as the system expert. In this research an expert system will be developed that is capable of early detection developmental disorders in toddlers using the Mamdani fuzzy method. The results obtained from this research are an expert system design for early detection of stunting using the Mamdani fuzzy method. The Mamdani fuzzy method was implemented to group the criteria for toddlers who fall into the stunting category or not from the initial data which is still gray because they are still unsure whether to categorize the toddler as having stunting or not. The detection accuracy rate using the Mamdani fuzzy method is 80.87% compared to expert diagnosis.
Melanoma image synthesis: a review using generative adversarial networks Ahmed, Mohammed Altaf; Qureshi, Mohammad Naved; Umar, Mohammad Sarosh; Bedoui, Mouna
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp551-569

Abstract

Melanoma is a highly malignant skin cancer that may be fatal if not promptly detected and treated. The limited availability of high-quality melanoma images, which are needed for training machine learning models, is one of the obstacles to detecting melanoma. Generative adversarial networks (GANs) have grown in popularity as a strong technique for image synthesis. This research is also targeted at the sustainable development goal (SDG) for health care. In this study, we survey existing GAN-based melanoma image synthesis methods. In this work, we briefly introduce GANs and how they may be used for generating synthetic images. Ensuring healthy lifestyles and promoting well-being for everyone, regardless of age, is the main aim. A comparative study is carried out on how GANs are used in current research to generate melanoma images and how they improve the classification performance of neural networks. Various public and proprietary datasets for training GANs in melanoma image synthesis are also discussed. Lastly, we assess the examined studies' performance using measures like the Frechet Inception distance (FID), Inception score, structural similarity ındex (SSIM), and various classification performance metrics. We compare the evaluated findings and suggest further GAN-based melanoma image-creation research.
Enhancing fault tolerance: dual Q-learning with dynamic scheduling Chetankumar Kalaskar; Thangam Somasundaram
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1150-1168

Abstract

Cloud computing has revolutionized IT delivery by offering scalable on-demand internet services encompassing software, platforms, and infrastructure. However, cloud services face significant performance challenges due to their susceptibility to failures given their vast operational scale. Implementing fault tolerance in dynamic cloud services is a key challenge, with complex configurations and dependencies complicating deployment. This paper introduces an innovative approach that combines double deep Q-learning (DDQL) with a dynamic fault-tolerant real-time scheduling algorithm (DFTRTSA) to enhance fault tolerance in real-time systems. DDQL, an extension of deep Q-learning, optimizes the fault-tolerance decision-making process. The algorithm adjusts scheduling strategies dynamically based on system conditions and errors. The fusion of DDQL and DFTRTSA aims to create a resilient and adaptive fault-tolerant mechanism, ensuring uninterrupted operation while meeting real-time requirements. This adaptive approach efficiently manages resources, meets deadlines, and gracefully handles errors, as demonstrated through experiments. Our DDQL-DFTRTSA method outperforms conventional fault-tolerant mechanisms in defect tolerance, energy efficiency, downtime reduction, and system dependability. It proves to be an ideal solution for real-time systems in dynamic and unpredictable environments.
Optimization deep learning with rough set approach model classification Otitis Irzal Arief Wisky; Teri Ade Putra
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1795-1804

Abstract

Otitis is a disease that occurs in the middle ear in the form of inflammation. This research aims to develop an analysis model for the classification of Otitis disease based on knowledge patterns based on symptoms and type of disease. The analysis methods used include the performance of the certainty factor (CF), rough set (RS), artificial neural network (ANN), and decision tree (DT) methods. CF and RS performance can be used to generate classification rule patterns. These rule patterns become new knowledge in the classification analysis process using the concept of deep learning (DL). DL analysis with ANN and DT performance can work optimally in exploring and discovering hidden knowledge. Based on the results of performance testing, the combination of CF and RS in preprocessing can present a classification pattern of 106 rules. The output of DL analysis results is proven to produce precise and accurate classification results with an accuracy of 89%. Based on these results, the analytical model developed was proven to be effective in classifying Otitis disease. Not only that, this research is also able to contribute to updating the knowledge-based system in the classification process.
Modeling load sensing pressure and flow control of axial piston pump by analyzing impact of bulk modulus Vivek Verma; Sachin Kumar; Apurva Anand
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1530-1543

Abstract

This research is focused on investigating the impact of the bulk modulus on the dynamics of variable delivery hydraulic axial piston pump (VAPP). The bulk modulus decreases exponentially with an increase in temperature whereas there is a linear positive relationship with pressure. The research revealed that there is a 6% (1 litre/s) increase in flow rate and a 2.6% (1.5 MPa) decrease in delivery pressure with a 38.75% (0.434 GPa) decrease in bulk modulus. Flow ripple and pressure pulsation are reduced by 39.3% and 43.2% respectively with a corresponding 38.75% decrease in bulk modulus. Pressure pulsation and flow ripple are responsible for the generation of noise and vibrations in the system. Flow rate increase contributes to better response and control of the VAPP. While a reduction in bulk modulus offers improved dynamic performance and overall response of the VAPP, it is noteworthy that a decrease in bulk modulus hurts the pump delivery pressure. The research allows the pump designer to formulate a strategy to optimize the bulk modulus under dynamic operating conditions to achieve optimal pump performance.

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