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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 64 Documents
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Search results for , issue "Vol 32, No 3: December 2023" : 64 Documents clear
Design of matrix, distributive round robin, ping pong and enhanced ping lock arbiter for shared resources systems Nagaiyanallur Lakshminarayanan Venkatara; Subramanian Sumithra; Ramaiah Purushothaman; Subramani Suresh Kumar; Kathiresan Kokulavani; Velankanni Gowri
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.pp1337-1345

Abstract

Arbiter is one of the main core elements in the network scheduler. The significant goal of this work is to design a high-speed and low execution-time arbiter with lock free and fair arbitration scheme. In this work, four types of arbiters such as matrix arbiter (MA), ping pong arbiter (PPA), distributive round-robin arbiter (DRRA) and enhanced ping lock arbiter (EPLA) are designed and analyzed area, delay, and speed of arbiters. MA is worked in square matrix format and matrix transition is performed for effective routing. The DRRA is designed by using a multiplexer and counter. Hence an, effective scheduling is carried out in DRRA. Binary tree format is used in PPA. The PPA provides low chip size and high speed than existing MA and DRRA. The PPA limits fair arbitration during uniformly distributed active request patterns. To overcome this problem, PPA is improved with some lock systems to create an EPLA. A new ping lock arbiter (PLA) leaf and PLA inter structure is proposed at the gate level to reduce the execution delay, improve the speed and achieve fair arbitration over all other existing arbiters. Resource allocation, execution delay, and speed are analyzed using the Xilinx Integrated Software Environment (ISE) tool.
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.
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.
Systematic literature review on global software development based software cost estimation models and cost drivers Mehmood Ahmed; Noraini Ibrahim; Wasif Nasir; Adeel Ahmed
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.pp1485-1494

Abstract

Global software development (GSD) is a well - established discipline of software engineering that focuses on the advantage of a global environment. Effective cost estimation is critical for the success of GSD projects. Cost estimation in a GSD environment is a challenging task. As a re sult, GSD must emphasize cost estimation. Findings show that a number of researchers over the past few decades have emphasized GSD - based cost estimation in GSD; to the best of our knowledge, however, existing cost estimation have not taken into account man y GSD - based cost drivers that must be considered when estimating costs. Motivated by all this, the purpose of this study is to review the existing GSD - based cost estimation models/techniques and cost drivers that influence the accuracy of cost estimation. To identify and compile relevant research papers, a systematic literature review was carried out. From twenty - seven selected studies, initially, 86 GSD - based cost drivers and 12 GSD - based cost estimation models/techniques were extracted. After filtration, 26 cost drivers were identified as significant and to be considered in GSD - based cost estimation. This study significantly identifies GSD - based cost drivers and existing cost estimation techniques.
Ensemble learning based health care claim fraud detection in an imbalance data environment Shweta S. Kaddi; Malini M. Patil
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.pp1686-1694

Abstract

Healthcare fraud has become a common encounter in the healthcare finance industry. The financial security of healthcare payers and providers is seriously impacted by healthcare fraud. When incorrect or exaggerated medical services are invoiced for reimbursement, fraudulent healthcare claims result. The effective operation of the healthcare system depends on the detection of such fraudulent actions. This paper develops a healthcare claim fraud detection method based on ensemble learning. Stack ensemble learning algorithm performance is compared to that of methods such as multi-layer perceptron (MLP) classifier, support vector classifier (SVC), logistic regression (LR), and decision tree (DT) algorithm. Because of the healthcare data imbalance, the normal transaction is significantly higher than the fraudulent transaction. The machine learning (ML) algorithm performs poorly because imbalanced data causes it to be biased toward the majority class. As a result, the data is unsampled using the synthetic minority oversampling technique (SMOTE) technique to provide balanced data. The experimental results show that for the identification of healthcare claim fraud, the ensemble learning strategy greatly outperforms single learning algorithms. The stack ensemble learning outperforms all the area under the curve for the receiver-operating characteristic (AUC ROC) curves from various algorithms, and the AUC-ROC curve is determined to be producing results that are adequate for all approaches.
Optimal capacity threshold for reversible watermarking using score function Chaiyaporn Panyindee
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.pp1598-1604

Abstract

Histogram shifting is an important technique of reversible watermarking, which can embed large payloads into digital images with low distortion. The technique must determine two threshold values to achieve the lowest possible distortion. Appropriate threshold values might be found by iterative methods, but it is computationally inefficient when the payloads are high and varied. In this paper, we show that the optimal threshold values lie on a straight line and occur at the boundary of the payload-satisfying region. Moreover, we propose a high performance algorithm to approximate the optimal threshold values. Under the same image quality, experimental results indicate that the proposed scheme could get closer threshold values to the optimal threshold values, compared to previous work. Therefore, it requires a smaller number of iterations to obtain the desirable threshold values.
A novel hybrid radio over fiber visible light communication system combining 5G mmWave and coarse wavelength division multiplex grid Muhammad Towfiqur Rahman; Rajendran Parthiban; Masud Bakau
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.pp1451-1461

Abstract

Visible light communication(VLC)has gained attention for enabling gigabitdata transmission over a short-range. Inradio over fiber (RoF), modulatedradio frequency (RF) is carried over optical fiber. Millimeter-wave (mmW)range also offers a vast amount of spectrum and enables integration with RoF.We propose a novel hybrid network using mmWave based RoF backhaulandcoarse wavelength division multiplexed(CWDM)-VLC for indoorcommunication. Three different optical tones were introduced to produce thedesired mmW signal using optical heterodyning with one of them carryingmodulated data and the other two carrying unmodulated data. Opticalsideband signal with the carrier(OSSB+C) is used for uplink communication.Modulated mmWave signal is used for VLC downlink to drive a multi-colorCWDM system. The performance of the VLC downlink is measured usingdifferent optical filters such as Bessel, Trapezoidal, Gaussian, and FabryPerot. A maximum data rate of 2.64 Gb/s and 6.58 Gb/s were achieved with10 and 20channelsoff the shelf LEDs with 16quadrature amplitudemodulation (QAM) with reasonable BER for downlink VLC communication.The uplink communication was carried out using mmW with 2.5 Gb/s datarate usingon-off keying(OOK)modulation and the data from thiscommunication down converted atcentral office(CO) through RoF backhaul.
Classifying product review quality based on semantic and structural features Ilham Akhyar Firdaus; Dwi Rolliawati; Anang Kunaefi; Firdaus Firdaus
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.pp1495-1502

Abstract

Product reviews are written opinions submitted by consumers in assessing a product. The existence of product reviews is important because it can help consumers make better product purchasing decisions. But product reviews can also be unimportant if the quality of the information from the reviews is not helpful. This can be minimized if a classification is carried out to find out which reviews are helpful or not. For this to be achieved, this research will apply a support vector machine model using semantic and structural features to be able to classify review texts based on their characteristics. By applying the appropriate preprocessing stages, the final results show that the semantic features produce the highest F1-score value of 0.825. Whereas the structural features produce the highest F1-score value of 0.823. From this, it can be concluded that semantic features can be used to identify the characteristics of a review text that are helpful or not properly. This success also shows outstanding performance in classifying reviews as helpful or not compared to previous studies.
Characterization of the electrical properties of an optical device manufactured with CMOS 0.35 μm technology Ricardo Yauri; Vanessa Gamero; Marco Alayo
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.pp1346-1352

Abstract

Currently, the relevance of optical devices has increased due to the physical limitations of the electrical transmission medium and the proximity of the limit of Moore's Law. Furthermore, the fabrication of optical devices on monolithic silicon substrates has gained importance in recent years thanks to manufacturing technologies in the microelectronics industry. For this reason, this paper aims to carry out the electrical characterization of an optical device manufactured with commercial austria micro syste m technology of complementary metal oxide semiconductors of 0.35 μm. The methodology consists of implementing an optical device, with an incandescent optical source called a microlamp, a waveguide and a photodiode. The microlamp was projected between two m etal layers connected by tungsten vias that act as filaments covered by SiO 2 dielectric to prevent oxidation. The results of the electrical characterization of the optical device show that the microlamp reaches a maximum current of 48 mA and stops working at higher currents. The waveguide was designed with a SiO 2 core and it was discovered that the TiN layers were found to be part of the waveguide causing it to behave as an emitter in the 2.5 - 5 µm region.
Design and development of frameworks for CPU verification efficiency improvement Sheetal Singrihalli Hemaraj; Shylashree Nagaraja; Sunitha Yariyur Narasimhaiah; Madhu Patil
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.pp1361-1369

Abstract

Bug finding is a critical component of the verification flow and is resource intensive.In a typical week, a debug engineer writes triages, which take up significant amount of time that could be spent debugging another unique issue, and the lack of standardization in scripting causes maintainability issues in functional verification bug triage. A framework that allows customizable triage script generation is developed based on inputs from the engineer deploying YAML isn’t another markup language (YAML) files and practical extraction and report language (PERL) scripting, and this methodology is made automated and is standardized across projects to ensure maximum benefit going forward. The use of auto-triage in the project of functional verification bug triage has contributed to a 18% increase in triaged signatures on average, from 40% before its use to 58% after. A similar earlier project vs. current project comparison shows a 20% uplift. The triaged inputs that are parsed are currently being fed to a machine learning algorithm, which will help further improve the debug efficiency. As part of future work, the information from input YAML files can be used to analyze simulation failure attributes, hence improving the overall efficiency of debugging.

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