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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 65 Documents
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Search results for , issue "Vol 31, No 2: August 2023" : 65 Documents clear
Rectangular and radial region of interests on the edge of cylindrical phantom for spatial resolution measurement Choirul Anam; Nazil Ainurrofik; Heri Sutanto; Ariij Naufal; Mohammad Haekal
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp747-754

Abstract

The purpose of this study was to evaluate the effect of rectangular region of interest (ROI) size on modulation transfer function (MTF), to develop the radial ROI, and to compare both ROIs performances for MTF measurement using a cylindrical polymethyl methacrylate (PMMA) phantom. The PMMA phantom used in this study was rotated 45°. Four rectangular ROIs and a radial ROI were created to measure the MTF value. The rectangular ROI sizes were 3×41, 21×41, 41×41, and 61×41 pixels; each was placed at upper phantom edge. The radial ROI’s length was 41 pixels and placed at several points in phantom edge. The MTF calculation was automatically conducted using MATLAB. The MTFs from rectangular ROIs and radial ROI were then compared. The comparison of the MTF measurement was also conducted using three different filters. The MTF which used radial ROI was smoother than those of rectangular ROI for all filters. This indicated that radial ROI was more resistant to noise than rectangular ROI. Rectangular ROI with the 41×41 pixels had similar 50% and 10% MTF values with the radial ROI. The MTF value which was obtained using radial ROI is more accurate and robust than those obtained using rectangular ROI.
Comparative evaluation of data mining algorithms in breast cancer Mustafa Qahtan Alsudani; Hassan Falah Fakhruldeen; Israa Fayez Yousif
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp777-784

Abstract

Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. The authors of this review studied numerous data mining algorithms and implemented them such that clinicians might use them to accurately detect cancer cells early on. This article introduces several techniques, including support vector machine (SVM), K star (K*) classifier, additive regression (AR), back propagation (BP) neural network, and Bagging. These algorithms are trained using a set of data that contains tumor parameters from breast cancer patients. Comparing the results, the authors found that SVM and Bagging had the highest precision and accuracy, respectively. Also assess the number of studies that provide machine learning techniques for breast cancer detection.
Anomaly detection for software defined datacenter networks using X-Pack Sneha Mahabaleshwar; Shobha Gangadhar; Sharath Krishnamurthy
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp995-1007

Abstract

The global data center market is growing as more and more enterprises are increasingly adopting cloud computing services and applications. Data centers are evolving towards highly virtualized architectures where transformation to software defined network (SDN) based solutions provides benefits in terms of network programmability, automation, and flow visibility. With the benefits, the need for securing network becomes essential as many critical applications are hosted on to such networking platforms. Anomaly detection is a continuous process of monitoring the traffic pattern and alerting the user about the anomalies if detected. For such real time analysis NoSQL and relational databases are less efficient. This paper proposes a framework for anomaly detection and alerting system using Elasticsearch database for SDN. Traffic patterns generated from SDN devices are continuously monitored and predefined actions are taken immediately if an anomaly is detected. The proof of concept is implemented in NOKIAs Nuage Networks Laboratory and the results showed a real time anomaly detection and took relevant actions within minimum time.
Grouping of Twitter users according to contents of their tweets Farha Naznin; Anjana Kakoti Mahanta
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp876-884

Abstract

In today’s world most of the people use social networking sites such as Twitter. They share their opinions and their views. through these media. Grouping these users will help us in different ways such as product recommendation, opinion mining, characterization of users based on their way of expressing their feelings. In this work, we present a technique to group the users based on the textual contents of the tweets. This technique is based on an unsupervised approach of machine learning that is clustering. A method is presented for representing the users using vector space model and TF-IDF weight scheme. K-means algorithm is employed for grouping the users using cosine distance as a distance measure. For the evaluation of this method, we construct a Twitter user dataset by using the Twitter application programming interface (API). A new technique is also proposed for characterization of the clusters formed. The experimental results are promising and from the study, it is found that the users in the clusters formed could be well defined by using the proposed cluster characterization technique.
A literature review for measuring maintainability of code clone Shahbaa I. Khaleel; Ghassan Khaleel Al-Khatouni
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1118-1127

Abstract

Software organizations face constant pressure due to stakeholder requirements and the increasing complexity of software systems. This complexity, combined with defects in code quality and failures, can pose risks to software systems. To ensure code is understood before maintenance, developers must spend over 60% of their time modifying and improving code quality, which is costly. This study examines the impact of code refactoring activities on software maintainability and quality by reviewing relevant research and explaining key terms. The research finds that refactoring activities can enhance specific quality characteristics, including maintainability, understandability, and testability. The study also identifies important factors that should be considered when developing refactoring tools. Refactoring enables code improvement without altering program behavior and can be applied multiple times to source code.
Selection of autofocus algorithms for printed circuit board automated optical inspection system Rizki Putra Prastio; Rodik Wahyu Indrawan
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp856-865

Abstract

This paper presents an examination study of 11 autofocus algorithms for printed circuit board (PCB) automated optical inspection (AOI). A selection of an optimal algorithm for that application based on some criteria was carried out. Unlike microscopy, PCB optical inspection does not require very high magnification. The object in this work was also different from that of microscopy and thus influenced the image features. We analyzed 47 PCB images, size of 640×480, sequentially captured every 1 mm in the z-direction. This work utilized USB digital microscope, and the magnification was set at ten times. Each algorithm calculated the sharpness values of the image sequences, and the plot of the sharpness profile was created. Moreover, the research also carried out experiments in several strategies, including image resizing and applying the non-local means (NLM) denoising filter to assess the algorithm performance in different situations. The algorithms were examined and ranked based on five criteria, i.e., computation time, full width at half maximum (FWHM), accuracy, number of half maxima, and range. The experimentation results showed that the Brenner gradient worked best for analyzing images both in their original dimension or resized images.
Machine learning based approach for detection of fake banknotes using support vector machine Haider Khalil Easa; Ali Ali Saber; Noor Kaylan Hamid; Hindren Ali Saber
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1016-1022

Abstract

Currency counterfeiting is a significant offense that has an impact on a nation's finances. Due to the enormous progress in printing technology, it is now quite simple to create fake currency that resembles real currency in both appearance and texture, making it nearly difficult to manually tell them apart. The suggested approach will be helpful in identifying fake currency in financial systems. Because of the rise of fake currency in the market, numerous false note detecting techniques are available globally to address this issue, however the most of them rely on expensive technology. In this paper, we'll introduce a revolutionary way for separating fake banknotes from real ones using the support vector machine (SVM) approach. To categorize bank notes as authentic or counterfeit utilizing the data retrieved from the photos of the bank notes, SVM performs better overall and is more effective, particularly when it comes to pattern categorization. Finally, the results of our experiment will demonstrate that the suggested algorithm does really yield extremely good performance.
Machine learning in predicting whistle-blowing intention of academic dishonesty with theory of planned behaviour Suraya Masrom; Nor Hafiza Abdul Samad; Rahayu Abdul Rahman; Farah Husna Mohd Fatzel; Siti Marlia Shamsudin
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp909-916

Abstract

The COVID-19 pandemic and its aftermath have caused most higher educations to choose to implement remote learning as a new method of instruction and assessment. Nevertheless, remote learning has been criticized by having adverse impact on academic integrity. Whistle-blowing has been regarded as an effective mechanism in limiting such unethical behavior. Thus, the main objective of this study is to identify the influence attributes of whistle-blowing intention among university students. The effectiveness of the whistle-blowing attributes was observed in prediction models based on machine learning technique. This paper presents the fundamental knowledge on evaluations of tree-based machine learning algorithms namely decision tree, random forest, to be compared with logistics regression and gradient linear model. A rigorous evaluation reports are provided that includes the area under curve (AUC) as a supplementary metric to measure the model accuracy. Additionally, to provide a clearer insight on the whistle-blowing prediction models, the pattern of influences from the whistle-blowing attributes based on the adoption of theory of planned behavior (TPB) and demography are presented. The findings revealed that both TPB and demography attributes contain some degree of impressive knowledge for the machine learning to generate a good prediction result.
Impact of cell temperature on the performance of a rooftop photovoltaic system of 2.56 kWp at Universitas Pamulang Ojak Abdul Rozak; Mohd Zamri Ibrahim; Muhamad Zalani Daud; Syaiful Bakhri; Rifqi Muwaffiq
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp599-608

Abstract

The performance of solar panels greatly determines the electrical energy production of a solar power generation system. The decrease in performance has an impact on efficiency, output power, output voltage and current. Currently, at Universitas Pamulang a solar photovoltaic system (PV) is installed with a capacity of 2.56 kWp since 2018. However, no performance test and analysis have been conducted to determine its level of efficiency and reliability. This paper presents an experimental method used for performance testing of a 320 W mono-crystalline solar panel, measuring from 08.00 AM to 4.00 PM, using the solar survey 200R to measure solar irradiation, ambient and cell temperature. A digital multimeter CD800a was used to measure Voc and a PV200 tester used to measure voltage and current output. The results revealed that at an ambient temperature of 38°C and cell temperature 50.9°C, the intensity of solar radiation was 702.7 W/m2 and output voltage of 42.9 V with a performance of 78.37% and an efficiency of 27.73% was due to an increase cell temperature. Low-efficiency values with high cell temperatures indicate that this system requires an external solar panel cooling system.
A multi-instance learning based approach for whitefly pest detection Lal Chand; Amardeep Singh Dhiman; Sikander Singh
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1050-1060

Abstract

Agriculture constantly faces various challenges including attacks from new pests and insects. With large farm sizes and plummeting manpower in the agricultural sector, it becomes challenging to continuously monitor crops for pest infestation. In this research paper, a specific type of pest attack known as the white fly attack has been investigated which affects a variety of crops. This paper presents four different approaches for automated classification of whiteflies which are the Bayesian network, convolution neural network (CNN), ResNet and multi-instance learning-CNN. A comparative analysis with conventional machine learning and deep learning techniques has also been presented. The performance of the proposed technique has been evaluated in terms of the classification accuracy. The experimental results obtained show that the proposed technique attains a classification accuracy of 95.53%, 96.9%, 97.6% and 98.13% for the four models respectively. A comparative analysis in terms of accuracy of classificaiton, with existing techniques shows that the proposed technique outperforms baseline deep learning models identifying whitefly infestation.

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