cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 783 Documents
 41   42   43   44   45 
SQL Injection Vulnerability Detection Using Deep Learning: A Feature-based Approach Md. Maruf Hassan; R. Badlishah Ahmad; Tonmoy Ghosh
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v9i3.3131

Abstract

SQL injection (SQLi), a well-known exploitation technique, is a serious risk factor for database-driven web applications that are used to manage the core business functions of organizations. SQLi enables an unauthorized user to get access to sensitive information of the database, and subsequently, to the application’s administrative privileges. Therefore, the detection of SQLi is crucial for businesses to prevent financial losses. There are different rules and learning-based solutions to help with detection, and pattern recognition through support vector machines (SVMs) and random forest (RF) have recently become popular in detecting SQLi. However, these classifiers ensure 97.33% accuracy with our dataset. In this paper, we propose a deep learning-based solution for detecting SQLi in web applications. The solution employs both correlation and chi-squared methods to rank the features from the dataset. Feed-forward network approach has been applied not only in feature selection but also in the detection process. Our solution provides 98.04% accuracy over 1,850+ recorded datasets, where it proves its superior efficiency among other existing machine learning solutions.
Responsive Motion Control for Robot Soccer Navigation Using Adaptive Social Force Framework Bima Sena Bayu Dewantara; Bagus Nugraha Deby Ariyadi; Hary Oktavianto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v9i3.2859

Abstract

This paper presents a modified Social Force Model (SFM) for navigation control of a soccer robot application. We modified the way of determining the parameter value of the gain factor, , of the SFM using the Fuzzy Inference System (FIS), so that the value of the gain factor, , is adaptive. The purpose of the gain factor adaptation is that the robot can move responsively but not over-reactive when it encounters an obstacle at high speed, which is a weakness of SFM with fixed parameters. Modification of SFM parameters using FIS is hereinafter referred to as the Fuzzy-based Social Force Model (F-SFM). We used this technique on a soccer robot with an omnidirectional drive platform with three motors. As an experiment, several modifications to the FIS rules were made and compared to the SFM with fixed parameters. The simulation-based experimental results show that the proposed method outperforms the SFM method with fixed-parameters, and the computation time does not differ significantly so that it can be applied for real implementation.
Voltage Rise Problem in Distribution Networks with Distributed Generation: A Review of Technologies, Impact and Mitigation Approaches Kehinde Adeleye Makinde; Daniel Oluwaseun Akinyele; Abraham Olatide Amole
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v9i3.2971

Abstract

Energy demand has constantly been on the rise due to aggressive industrialization and civilization. This rise in energy demand results in the massive penetration of distributed generation (DG) in the distribution network (DN) which has been a holistic approach to enhance the capacity of distribution networks. However, this has led to a number of issues in the low voltage network, one of which is the voltage rise problem. This happens when generation exceeds demand thereby causing reverse power flow and consequently leading to overvoltage. A number of methods have been discussed in the literature to overcome this challenge ranging from network augmentation to active management of the distribution networks. This paper discusses the issue of voltage rise problem and its impact on distribution networks with high amounts of distributed energy resources (DERs). It presents different DG technologies such as those based on conventional and unconventional resources and other DERs such as battery storage systems and fuel cells. The study provides a comprehensive overview of approaches employed to curtail the issue of voltage increase at the point of common coupling (PCC), which includes strategies based on the network reinforcement methodology and the active distribution network management. A techno-economic comparison is then introduced in the paper to ascertain the similarities and dissimilarities of different mitigation approaches based on the technology involved, ease of deployment, cost implication, and their pros and cons. The paper provides insights into directions for future research in mitigating the impact of voltage rise presented by grid-connected DGs without limiting their increased penetration in the existing power grid.
Network current quality enhancement under nonlinear and unbalanced load conditions using a four-wire inverter-based active shunt filter Karim Belalia; Mohammed Khodja; Hamid Bouzeboudja; Azeddine Bendiabdellah; Abdelkader Mostefa
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v9i3.2951

Abstract

The flow of a large current in the neutral conductor of a transmission system is one of the major problems caused by harmonic pollution. This current can assume excessive values and even exceed the current flowing in the phases which can be extremely dangerous both for the equipment and the safety of the personnel. Currently, the parallel or shunt active filter (SAF) or parallel active filter is considered as the most effective solution to mitigate harmonic pollution and restore a sinusoidal current waveform in electrical distribution networks. The SAF can be used to compensate for harmonic currents, as well as that of the reactive power. This paper proposes a SAF circuit based on a four-arm inverter topology. The designed SAF is shown to lead to better harmonic compensation with a reduced THD (Total Harmonic Distortion) level in the presence of nonlinear and unbalanced loads in the network. The other goal of this study is to eliminate the neutral current caused by the unbalance in the polluting loads connected to the distribution network, achieve a near-sinusoidal current waveform and protect the electric network equipment.
Selfish Herd Optimisation based fractional order cascaded controllers for AGC study Subhadra Sahoo; Narendra Kumar Jena; Binod Kumar Sahu; Prakash Kumar Ray
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v9i3.3009

Abstract

In a modern, and complex power system (PS), robust controller is obligatory to regulate the frequency under uncertain load/parameter change of the system. In addition to this, presence of nonlinearities, load frequency control (LFC) of a Power System becomes more challenging which necessitates a suitable, and robust controller. Single stage controller does not perform immensely against aforesaid changed conditions. So, a novel non-integer/fractional order (FO) based two-stage controller incorporated with 2-degrees of freedom (2-DOF), derivative filter (N), named as 2-DOF-FOPIDN-FOPDN controller, is adopted to improve the dynamic performance of a 3-area power system. Each area of the power system consists of both non-renewable and renewable generating units. Again, to support the superior performance of 2-DOF-FOPIDN-FOPDN controller, it is compared with the result produced by PID, FOPID, and 2-DOF-PIDN-PDN controllers. The optimal design of these controllers is done by applying Selfish Herd Optimisation (SHO) technique. Further, the robustness of the 2-DOF-FOPIDN-FOPDN controller is authenticated by evaluating the system performance under parameter variation. The work is further extended to prove the supremacy of SHO algorithm over a recently published article based on pathfinder algorithm (PFA).
Internet of Things Based Smart Vending Machine using Digital Payment System Wahidul Alam; Dhiman Sarma; Rana Joyti Chakma; Mohammad Jahangir Alam; Sohrab Hossain
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v9i3.3133

Abstract

The advent of the Internet envisions a cashless society by enabling financial transactions through digital payments. Significantly, the emergence of coronavirus (COVID-19) disrupted our traditional cash handling means and triggered an inflection point for switching towards contactless digital payments from physical cash payments. Furthermore, Internet of Things (IoT) technology escalates digital payments to the next level by enabling devices to render goods and services without requiring any human interaction. This research proposed an IoT-enabled cashless vending machine that incorporates both cloud computing and payment gateway for ordering and purchasing items through digital payment systems by using a mobile application. The system enables a pre-installed mobile application to scan the Quick Response (QR) code attached to the body of a vending machine, opens the portal of a web-based virtual machine through the code, allows user to choose and order items from the virtual vending, initiates and authorizes a digital payment through an IoT gateway installed inside the physical vending machine by establishing a connection between user's and vendor's financial entities, and finally, dispenses the ordered items by unlocking the shelves of the vending machine after the successful payment transaction. It operates in the Arduino platform with an ATmega 2560 Microcontroller and Esp8266 Wi-fi module as hardware components, mobile application software, and payment gateway API. The system performed an average response time of 14500 milliseconds to pick a product after running 150 consecutive API test calls. This result shows a satisfying time for enhancing customers' buying experiences with digital payment systems and a customizable and cost-effective IoT-based intelligent vending machine to introduce for mass production.
Open Source Big Data Platforms and Tools: An Analysis Yassine Benlachmi; Moulay Lahcen Hasnaoui
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v9i3.3170

Abstract

Big data is attracting an excessive amount of interest in the IT and academic sectors. On a regular basis, computer and digital industries generate more data than they have space to store. In the current situation, five billion people have their own mobile phone, and over two billion people are linked globally to exchange various types of data. By 2020, it is estimated that about fifty billion people will be connected to the internet. During2020, data generation, use, and sharing would be forty-four times higher than in previous years. A variety of sectors and organizations are using big data to manage various operations. As a result, a thorough examination of big data's benefits, drawbacks, meaning, and characteristics is needed. The primary goal of this research is to gather information on the various open-source big data tools and platforms that are used by various organizations. In this paper we use a three perspective methodology to identify the strength and weaknesses of the workflow in a open source big data arena. This helps to establish a pipeline of workflow events for both researcher and entrepreneur decision making.
Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction Sharipuddin Sharipuddin; Eko Arip Winanto; Benni Purnama; Kurniabudi Kurniabudi; Deris Stiawan; Darmawijoyo Hanapi; Mohd Yazid bin Idris; Bedine Kerim; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v9i3.3134

Abstract

Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%.
A Meandered Line Patch Antenna at Low Frequency Range for Early Stage Breast Cancer Detection Md Abdullah Al Rakib; Shamim Ahmad; Md. Humayun Kabir Khan; Mainul Haque; Tareq Mohammad Faruqi; Md Saroar Jahan; Jhuma Kabir Mim
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v9i3.2824

Abstract

Every year a concerning number of women are affected by breast cancer which is one of the deadliest and common types of cancers. Breast cancer is curable at early stages. For detecting breast cancer, there are several methods such as MRI, Mammography, Tomography, Ultrasound, and biopsy are available in medical technology. Still, none of them are as easy and efficient as a microwave imaging technique, in this method, the antenna plays an important role. Therefore, this paper focuses on developing an antenna at a low-frequency range for microwave imaging techniques to detect cancerous tissue inside the breast. For this, the antenna parameters, i.e., return loss, VSWR, directivity, current density, and specific absorption rate were studied, by setting the antenna over without tumor and with tumor breast as up-side-down, to ensure the compatibility of the antenna for the technique as well as for the patient’s body. A 5mm radius cancerous tumor was created inside the breast with dielectric conductivity of 4 and relative permittivity of 50. Cancerous cells were detected by reading the antenna parameters’ comparison between the healthy breast and the affected breast. The whole study was conducted by using CST MICROWAVE STUDIO SUITE 2020. 
M2CIM-DSS: A Model for Measuring Continuance Intention in Decision Support Systems Ali Hussein Mohammed; Ayad Hameed Mousa; Nawal Mousa Almeyali; Intedhar Shakir Nasir
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v9i3.3032

Abstract

Currently, the core trend of Higher Education Institutes (HEI) to invest in decision support systems (DSS) to improve their decision-making process. Due to technology emergence, HEI has been experiencing noteworthy changes. Many techniques such as DSS have adopted developed and implemented to support the educational process. Even though DSS has adopted and invested mainly in most sectors, a lack of research in investigating confirmed, the influencing factors on the intention of stakeholders to continue to use them. Consequently, the purpose of the study is to examine post-adoption users' satisfaction and users’ intention to continue using DSS. This study combining two theoretical models, the Technology Acceptance Model, and The Technology Organization Environment Framework, to examine users’ intentions to continue using DSS. The data collection process has conducted using 240 respondents, who belong to HEI institutions (Academia and management staff), who work on DSS. Structural Equation Modeling was utilized to analyze structural relationships among the proposed model’s factors. The authors used several methods such as hierarchical regression, one-way ANOVA, descriptive statistics, as well as t-test have applied to evaluate the model's components relevancy, understanding, and pertinence to each other. The result shows the proposed model fits the data and had a good explanation than the existing models. On the other hand, the results show the importance of equipping DSS with real-time support because they have positive repercussions in the decision-making process The implications as well as the limitations of this study have been extensively discussed.

Page 43 of 79 | Total Record : 783

 41   42   43   44   45 

Filter by Year

2013 2025


Reset
Filter By Issues
All Issue Vol 13, No 4: December 2025 Vol 13, No 3: September 2025 Vol 13, No 2: June 2025 Vol 13, No 1: March 2025 Vol 12, No 4: December 2024 Vol 12, No 3: September 2024 Vol 12, No 2: June 2024 Vol 12, No 1: March 2024 Vol 11, No 4: December 2023 Vol 11, No 3: September 2023 Vol 11, No 2: June 2023 Vol 11, No 1: March 2023 Vol 10, No 4: December 2022 Vol 10, No 3: September 2022 Vol 10, No 2: June 2022 Vol 10, No 1: March 2022 Vol 9, No 4: December 2021 Vol 9, No 3: September 2021 Vol 9, No 2: June 2021 Vol 9, No 1: March 2021 Vol 8, No 4: December 2020 Vol 8, No 3: September 2020 Vol 8, No 2: June 2020 Vol 8, No 1: March 2020 Vol 7, No 4: December 2019 Vol 7, No 3: September 2019 Vol 7, No 2: June 2019 Vol 7, No 1: March 2019 Vol 6, No 4: December 2018 Vol 6, No 3: September 2018 Vol 6, No 3: September 2018 Vol 6, No 2: June 2018 Vol 6, No 1: March 2018 Vol 5, No 4: December 2017 Vol 5, No 3: September 2017 Vol 5, No 2: June 2017 Vol 5, No 1: March 2017 Vol 4, No 4: December 2016 Vol 4, No 3: September 2016 Vol 4, No 2: June 2016 Vol 4, No 1: March 2016 Vol 3, No 4: December 2015 Vol 3, No 3: September 2015 Vol 3, No 2: June 2015 Vol 3, No 1: March 2015 Vol 2, No 4: December 2014 Vol 2, No 3: September 2014 Vol 2, No 3: September 2014 Vol 2, No 2: June 2014 Vol 2, No 1: March 2014 Vol 1, No 4: December 2013 Vol 1, No 3: September 2013 Vol 1, No 2: June 2013 Vol 1, No 1: March 2013 More Issue