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
Kota yogyakarta,
Daerah istimewa yogyakarta
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 9,174 Documents
 642   643   644   645   646 
Mobility-prediction and energy optimization for multi-channel multi-interface ad hoc networks in the presence of location errors Hassan Faouzi; Mohammed Boutalline
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp315-325

Abstract

We present a mobility-prediction and energy optimization solution for multi-channel multi-interface (MCMI) ad hoc networks in the presence of location errors. This solution includes routing of the MCMI communication links that adapt to dynamic channel, traffic conditions, interference and mobility of nodes. We start first with implementing a novel cross-layer routing solution in order to share information between network and MAC layer, the benefit of this technique is to collect information about the channel quality and residual energy of the nodes and send them directly to the network layer. Next, we present a mobility-prediction model using Kalman filter to predict accurate locations and enhance routing performance, through estimating link duration and selecting reliable routes. The performance of proposed mechanism is measured using NS2.35 simulations with different scenarios and varying load in a network. Comparative analysis of simulation results shows better performance of our protocol (ME-MCMI AODV) in terms of reducing end-to-end delay, total dropped packets and increasing network lifetime and packet delivery ratio (PDR).
Smart monitoring system using NodeMCU for maintenance of production machines Ignatius Deradjad Pranowo; Dian Artanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp788-795

Abstract

Maintenance is an activity that helps to reduce risk, increase productivity, improve quality, and minimize production costs. The necessity for maintenance actions will increase efficiency and enhance the safety and quality of products and processes. On getting these conditions, it is necessary to implement a monitoring system used to observe machines' conditions from time to time, especially the machine parts that often experience problems. This paper presents a low-cost intelligent monitoring system using NodeMCU to continuously monitor machine conditions and provide warnings in the case of machine failure. Not only does it provide alerts, but this monitoring system also generates historical data on machine conditions to the Google Cloud (Google Sheet), includes which machines were down, downtime, issues occurred, repairs made, and technician handling. The results obtained are machine operators do not need to lose a relatively long time to call the technician. Likewise, the technicians assisted in carrying out machine maintenance activities and online reports so that errors that often occur due to human error do not happen again. The system succeeded in reducing the technician-calling time and maintenance workreporting time up to 50%. The availability of online and real-time maintenance historical data will support further maintenance strategy.
A new modification of the quasi-newton method for unconstrained optimization Hamsa Th. Saeed Chilmeran; Huda I. Ahmed; Eman T. Hamed; Abbas Y. Al-Bayati
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1683-1691

Abstract

In this work we propose and analyze a hybrid conjugate gradient (CG) method in which the parameter is computed as a linear combination between Hager-Zhang [HZ] and Dai-Liao [DL] parameters. We use this proposed method to modify BFGS method and to prove the positive definiteness and QN-conditions of the matrix. Theoretical trils confirm that the new search directions aredescent directions under some conditions, as well as, the new search directions areglobally convergent using strong Wolfe conditions. The numerical experiments show that the proposed method is promising and outperforms alternative similar CG-methods using Dolan-Mor'e performance profile. 
Tuned bidirectional encoder representations from transformers for fake news detection Amsal Pardamean; Hilman F. Pardede
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1667-1671

Abstract

Online medias are currently the dominant source of Information due to not being limited by time and place, fast and wide distributions. However, inaccurate news, or often referred as fake news is a major problem in news dissemination for online medias. Inaccurate news is information that is not true, that is engineered to cover the real information and has no factual basis. Usually, inaccurate news is made in the form of news that has mass appeal and is presented in the guise of genuine and legitimate news nuances to deceive or change the reader's mind or opinion. Identification of inaccurate news from real news can be done with natural language processing (NLP) technologies. In this paper, we proposed bidirectional encoder representations from transformers (BERT) for inaccurate news identification. BERT is a language model based on deep learning technologies and it has found effective for many NLP tasks. In this study, we use transfer learning and fine-tuning to adapt BERT for inaccurate news identification. The experiments show that our method could achieve accuracy of 99.23%, recall 99.46%, precision 98.86%, and F-Score of 99.15%. It is largely better than traditional method for the same tasks.
Real-time high-speed mobility management Ahmed Abdelsalam Abuelgasim; Mohamed Khalafalla Hassan; Mutaz Hamed Khairi; Muhammad Nadzir Marsono; Kamaludin Mohamad Yusof
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1534-1541

Abstract

High-speed mobility system has now become a serious concern for mobile operators due to the large frameworks of a heterogeneous network made up of multiple cell types and different frequency bands. Handover (HO) is conducted in a real-life scenario when the user equipment (UE) moves from one network coverage to another by performing proper measurement with high speed. HO breakdown and call loss are observed due to a high speed; thus, high-speed mobility system needs improvement by using the UE speed as one of the key measurement monitoring criteria for the long-term evolution (LTE) network. Vendor consultation has been considered in this paper in addition to real drive test measurement in highways. Results have shown that velocity has a direct impact on the handover quality and overall timing. Results also demonstrate that 120 km/h measurement is better than 140 km/h as UE speed.
Improved Lagrangian relaxation generation decision-support in presence of electric vehicles Hossein Zeynal; Zuhaina Zakaria; Ahmad Kor
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp598-608

Abstract

Decision making strategies for resources available in macro/micro scales have long been a critical argument. Among existing methods to address such a mixed-binary optimization model, lagrangian relaxation (LR) found universal acceptance by many utilities, offering a fast and accurate answer. This paper aims at retrofitting the solution way of LR algorithm by dint of meta-heuristic cuckoo search algorithm (CSA). When integrating CSA into LR mechanism, a tighter duality gap is catered, representing more accurate feasible solution. The key performance of CSA exhibits a head start over other classical methods such as gradient search (GS) and newton raphson (NR) when dealt with the relative duality gap closure in LR procedure. Further, electric vehicles (EV) with its associated hard constraints are encompassed into model to imperiling the proposed CSA-LR if encountered with nonlinear fluctuation of duality gap. Simulation results show that the proposed CSA-LR model outperforms the solution quality with/without EV as compared with conventional NR-LR method.
Secure image hiding in speech signal by steganography-mining and encryption Amal Hameed Khaleel; Iman Qays Abduljaleel
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1692-1703

Abstract

Information hiding techniques are constantly evolving due to the increased need for security and confidentiality. This paper proposes a working mechanism in three phases. The first phase includes scrambling the values of the gray image depending on a series of keys that are generated using a quantum chaotic map. The second phase generates hybrid keys by mixing a Zaslavsky and a 3D Hanon map that are used to encrypt the gray image values produced after the scramble. Finally, in the third phase, a new algorithm is suggested to hide the encrypted gray image at random locations within a speech file.  This algorithm includes the LSB algorithm to determine the hidden bits and the zero-crossing K-means algorithm in selecting locations mining in a scattered manner so that hackers cannot easily retrieve the hidden data of any hacked person. Also used a fractional fourier transform to choose magnitude value as specific data to hide encoded image data. The measures MSE, PSNR, NSCR, and UACI are using to measure the work efficiency in the encryption algorithm, and in measuring the efficiency of the hidden algorithm, use the measures SNR, PSNR, and MSE. The results of the paper are encouraging and efficient compared to other algorithms that performed the same work. Hence our results show the larger the image dimensions used, the better the values.
Hybrid features for object detection in RGB-D scenes Sari Awwad; Bashar Igried; Mohammad Wedyan; Mohammad Alshira'H
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1073-1083

Abstract

Object detection is considered a hot research topic in applications of artificial intel-ligence and computer vision. Historically, object detection was widely used in var-ious fields like surveillance, fine-grained activities and robotics. All studies focus on improving accuracy for object detection using images, whether indoor or outdoor scenes. Therefore, this paper took a shot by improving the doable features extraction and proposing crossed sliding window approach using exiting classifiers for object de-tection. In this paper, the contribution includes two parts: First, improving local depth pattern feature along side SIFT and the second part explains a new technique presented by proposing crossed sliding window approach using two different types of images (colored and depth). Two types of features local depth patterns for detection (LDPD) and scale-invariant feature transform (SIFT) were merged as one feature vector. The RGB-D object dataset has been used and it consists of 300 different objects and in-cludes thousands of scenes. The proposed approach achieved high results comparing to other features or separated features that are used in this paper. All experiments and comparatives were applied on the same dataset for the same objective. Experimental results report a high accuracy in terms of detection rate, recall, precision and F1 scorein RGB-D scenes.
Modified artificial bee colony optimization algorithm for adaptive power scheduling in an isolated system Vijo M. Joy; S. Krishnakumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp1168-1175

Abstract

The objective of this work is to solve the power scheduling problems for efficient energy management by assigning the optimal values. Artificial neural networks are used widely in the field of energy management and load scheduling. The  backpropagation technique is used for the feed-forward neural network training and the Levenberg-Marquardt algorithm is used to minimize the errors. The slow speed of convergence and getting stuck in local minima are some negatives of   backpropagation in complex computation. To overcome these drawbacks an innovative meta-heuristicsearch algorithm called modified artificial bee colony optimization algorithm is used. A hybrid neural network is introduced in this work.  The simulation result shows that the efficiency of the systemis improved when hybrid optimization is used. With this method, the system achieves an optimalaccuracy of 99.23%
An approach for slow distributed denial of service attack detection and alleviation in software defined networks Prathima Mabel John; Rama Mohan Babu Kasturi Nagappasetty
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp404-413

Abstract

Over the last few years, the need for programmable networks has captured the interest of industrialists and academicians. It has led to the development of a paradigm called software defined network (SDN). It separates the network intelligence into the control plane and forwarding logic into the data plane. This architecture gives scope to various security issues of which denial of service (DoS) is the most common and challenging to detect. This paper focuses on the detection and mitigation of a slow DoS attack called Slowloris on Apache2 server in SDN based networks. The proposed solution is called Slowloris detection and mitigation mechanism (SDMM). Mininet, an emulator, and SimpleHTTPServer are used for simulation and the same is implemented using Zodiac FX OpenFlow switch, Ryu controller and Apache2 server. SDMM algorithm detects and mitigates prolonged Slowloris attack in typical networks as well as in slow networks with low bandwidth and high delay in 240-280s with an accuracy of 100% and 98% respectively. It uses expectation of burst size as a key factor for detection.

Page 644 of 918 | Total Record : 9174

 642   643   644   645   646 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 41, No 2: February 2026 Vol 41, No 1: January 2026 Vol 40, No 3: December 2025 Vol 40, No 2: November 2025 Vol 40, No 1: October 2025 Vol 39, No 3: September 2025 Vol 39, No 2: August 2025 Vol 39, No 1: July 2025 Vol 38, No 3: June 2025 Vol 38, No 2: May 2025 Vol 38, No 1: April 2025 Vol 37, No 3: March 2025 Vol 37, No 2: February 2025 Vol 37, No 1: January 2025 Vol 36, No 3: December 2024 Vol 36, No 2: November 2024 Vol 36, No 1: October 2024 Vol 35, No 3: September 2024 Vol 35, No 2: August 2024 Vol 35, No 1: July 2024 Vol 34, No 3: June 2024 Vol 34, No 2: May 2024 Vol 34, No 1: April 2024 Vol 33, No 3: March 2024 Vol 33, No 2: February 2024 Vol 33, No 1: January 2024 Vol 32, No 3: December 2023 Vol 32, No 1: October 2023 Vol 31, No 3: September 2023 Vol 31, No 2: August 2023 Vol 31, No 1: July 2023 Vol 30, No 3: June 2023 Vol 30, No 2: May 2023 Vol 30, No 1: April 2023 Vol 29, No 3: March 2023 Vol 29, No 2: February 2023 Vol 29, No 1: January 2023 Vol 28, No 3: December 2022 Vol 28, No 2: November 2022 Vol 28, No 1: October 2022 Vol 27, No 3: September 2022 Vol 27, No 2: August 2022 Vol 27, No 1: July 2022 Vol 26, No 3: June 2022 Vol 26, No 2: May 2022 Vol 26, No 1: April 2022 Vol 25, No 3: March 2022 Vol 25, No 2: February 2022 Vol 25, No 1: January 2022 Vol 24, No 3: December 2021 Vol 24, No 2: November 2021 Vol 24, No 1: October 2021 Vol 23, No 3: September 2021 Vol 23, No 2: August 2021 Vol 23, No 1: July 2021 Vol 22, No 3: June 2021 Vol 22, No 2: May 2021 Vol 22, No 1: April 2021 Vol 21, No 3: March 2021 Vol 21, No 2: February 2021 Vol 21, No 1: January 2021 Vol 20, No 3: December 2020 Vol 20, No 2: November 2020 Vol 20, No 1: October 2020 Vol 19, No 3: September 2020 Vol 19, No 2: August 2020 Vol 19, No 1: July 2020 Vol 18, No 3: June 2020 Vol 18, No 2: May 2020 Vol 18, No 1: April 2020 Vol 17, No 3: March 2020 Vol 17, No 2: February 2020 Vol 17, No 1: January 2020 Vol 16, No 3: December 2019 Vol 16, No 2: November 2019 Vol 16, No 1: October 2019 Vol 15, No 3: September 2019 Vol 15, No 2: August 2019 Vol 15, No 1: July 2019 Vol 14, No 3: June 2019 Vol 14, No 2: May 2019 Vol 14, No 1: April 2019 Vol 13, No 3: March 2019 Vol 13, No 2: February 2019 Vol 13, No 1: January 2019 Vol 12, No 3: December 2018 Vol 12, No 2: November 2018 Vol 12, No 1: October 2018 Vol 11, No 3: September 2018 Vol 11, No 2: August 2018 Vol 11, No 1: July 2018 Vol 10, No 3: June 2018 Vol 10, No 2: May 2018 Vol 10, No 1: April 2018 Vol 9, No 3: March 2018 Vol 9, No 2: February 2018 Vol 9, No 1: January 2018 Vol 8, No 3: December 2017 Vol 8, No 2: November 2017 Vol 8, No 1: October 2017 Vol 7, No 3: September 2017 Vol 7, No 2: August 2017 Vol 7, No 1: July 2017 Vol 6, No 3: June 2017 Vol 6, No 2: May 2017 Vol 6, No 1: April 2017 Vol 5, No 3: March 2017 Vol 5, No 2: February 2017 Vol 5, No 1: January 2017 Vol 4, No 3: December 2016 Vol 4, No 2: November 2016 Vol 4, No 1: October 2016 Vol 3, No 3: September 2016 Vol 3, No 2: August 2016 Vol 3, No 1: July 2016 Vol 2, No 3: June 2016 Vol 2, No 2: May 2016 Vol 2, No 1: April 2016 Vol 1, No 3: March 2016 Vol 1, No 2: February 2016 Vol 1, No 1: January 2016 Vol 16, No 3: December 2015 Vol 16, No 2: November 2015 Vol 16, No 1: October 2015 Vol 15, No 3: September 2015 Vol 15, No 2: August 2015 Vol 15, No 1: July 2015 Vol 14, No 3: June 2015 Vol 14, No 2: May 2015 Vol 14, No 1: April 2015 Vol 13, No 3: March 2015 Vol 13, No 2: February 2015 Vol 13, No 1: January 2015 Vol 12, No 12: December 2014 Vol 12, No 11: November 2014 Vol 12, No 10: October 2014 Vol 12, No 9: September 2014 Vol 12, No 8: August 2014 Vol 12, No 7: July 2014 Vol 12, No 6: June 2014 Vol 12, No 5: May 2014 Vol 12, No 4: April 2014 Vol 12, No 3: March 2014 Vol 12, No 2: February 2014 Vol 12, No 1: January 2014 Vol 11, No 12: December 2013 Vol 11, No 11: November 2013 Vol 11, No 10: October 2013 Vol 11, No 9: September 2013 Vol 11, No 8: August 2013 Vol 11, No 7: July 2013 Vol 11, No 6: June 2013 Vol 11, No 5: May 2013 Vol 11, No 4: April 2013 Vol 11, No 3: March 2013 Vol 11, No 2: February 2013 Vol 11, No 1: January 2013 Vol 10, No 8: December 2012 Vol 10, No 7: November 2012 Vol 10, No 6: October 2012 Vol 10, No 5: September 2012 Vol 10, No 4: August 2012 Vol 10, No 3: July 2012 More Issue