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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 30, No 2: May 2023" : 64 Documents clear
Machine learning to improve the performance of anomaly-based network intrusion detection in big data Siriporn Chimphlee; Witcha Chimphlee
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1106-1119

Abstract

With the rapid growth of digital technology communications are overwhelmed by network data traffic. The demand for the internet is growing every day in today's cyber world, raising concerns about network security. Big Data are a term that describes a vast volume of complicated data that is critical for evaluating network patterns and determining what has occurred in the network. Therefore, detecting attacks in a large network is challenging. Intrusion detection system (IDS) is a promising cybersecurity research field. In this paper, we proposed an efficient classification scheme for IDS, which is divided into two procedures, on the CSE-CIC-IDS-2018 dataset, data pre-processing techniques including under-sampling, feature selection, and classifier algorithms were used to assess and decide the best performing model to classify invaders. We have implemented and compared seven classifier machine learning algorithms with various criteria. This work explored the application of the random forest (RF) for feature selection in conjunction with machine learning (ML) techniques including linear regression (LR), k-Nearest Neighbor (k-NN), classification and regression trees (CART), Bayes, RF, multi layer perceptron (MLP), and XGBoost in order to implement IDSS. The experimental results show that the MLP algorithm in the most successful with best performance with evaluation matrix.
Supply chain strategy during the COVID-19 terms: sentiment analysis and knowledge discovery through text mining Muhammad Khahfi Zuhanda; Yuan Anisa; Desniarti Desniarti; Muhammad Hafiz; Anil Hakim Syofra; Rezzy Eko Caraka; Maengseok Noh
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1120-1127

Abstract

The coronavirus pandemic has affected not only health but also the economy. The use of big data in finding information can be used to gain profits that logistics companies can utilize to survive during the pandemic. This study conducted text-mining research on service consultant sites in the logistics sector. This study aims to present frequency diagrams, analyze sentiment using the National Research Council (NRC) lexicon, present bigrams, and seek knowledge about strategies to minimize shipping costs and maintain inventories of manufactured goods. The words "supply", "chain", and "COVID-19" are words that are used frequently throughout the article. The results of this study showed that the words that often appear from word excavation are the words "supply", "chain", "logistics", "kpis," and "inventory". Then emotion trust becomes an emotional word that often appears in articles. The words "Supply" and "pandemic" are the words that seem the most positive and negative words, respectively. The words "COVID-19", "safety stock", and "inventory management" are words that often appear together. The result of discovery knowledge is that logistics consultants offer emotions of trust and provide many insights on minimizing shipping costs and maintaining inventory during a pandemic.
Rainfall prediction model in Semarang City using machine learning Carissa Devina Usman; Aris Puji Widodo; Kusworo Adi; Rahmat Gernowo
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1224-1231

Abstract

The erratic distribution of rainfall greatly affects people's daily activities, especially in Semarang City, so it is necessary to predict rainfall. Correct prediction of rainfall can improve community preparedness in dealing with natural disasters. Algorithms for machine learning and data mining have been extensively utilized in research involving rainfall data from various regions. The primary objectives of this study are to find the best regression algorithm and use machine learning algorithms to predict rainfall in Semarang. The dataset used is daily rainfall data for the City of Semarang from the meteorological, climatological, and geophysical agency (BMKG). Machine learning algorithms such as multiple linear regression, random forest regression, and replicated neural networks will be used to conduct regression analysis on this dataset. The mean absolute error and Root mean squared error techniques are utilized to evaluate the performance of machine learning algorithms. With an error rate of 13.055 for root mean squared error (RMSE) and 6.621 for mean absolute error (MAE), the results of the research indicate that the performance of the neural network algorithm is superior to that of other algorithms.
Effects of (Ba,Ca)ScO2F:Bi3+,K+ phosphor particle size on color uniformity white light-emitting diodes Ha Thanh Tung; Huu Phuc Dang
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp707-713

Abstract

Phosphors that offer considerable performance as well as heat consistency has been a high priority of recent studies concerning light-emitting diodes (LED) devices. This study employs the perovskite phosphors BCSOF (short for Ba1-xCaxScO2F:0.001Bi3+,0.001K+ with x value from 0 to 0.12 and one chip at 415 nm generating thin green illumination via cation-replacement method. The study examines the aftermath when Ca2+ replaces Ba2+ within the crystal formations of BCSOF as well as the luminescent features of the phosphors, detecting a formation of cube-like perovskite within the space group of Pm3m in the employed phosphors. In addition, the study also assesses the development concerning the magnitude of cells as well as the binding extent of Ba/Ca/K/Bi-O. When the inner quantum performance reaches 77.4% in BCSOF, a potent green discharge is manifested, reaching 510 nm when excited by a chip at 415 nm. Greater luminescent performance as well as heat consistency correlating with changes in inner formation were reported. Via the method of replacing cations, it is possible to control spectrum by manipulating the latticework’s surroundings, leading to desirable performance in LED products.
Transmission line characterization and modeling for electronic circuits and systems design Oluwole John Famoriji; Thokozani Shongwe
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp730-738

Abstract

Channel bandwidth-limited high-speed links or interfaces make circuit solutions not efficient. Both recent and subsequent links (SerDes-Serializer/Deserializer) design demand efficient and effective coupling between future circuit design, communication, and optimization. The challenges vary and new solutions are needed. In this article, an analytical wireline model is presented to predict electronic path loss towards adequate designs of electronic circuits and systems. An open loop system analysis is adapted in this paper. Our model was tested against different channels: a legacy channel with via stub discontinuity and FR4 dielectric, and a more recent microwave-engineered channel without stub and NELCO 6,000 dielectric, a very good matching attained. Good agreement was observed between our model and electromagnetic full-wave simulation data, as a result showed high level of applicability to thin-film microstrip line for adequate circuit design. The model is recommended for electronic engineers for adequate and faster interfaces and high-speed links designs.
The impacts from SrS:Cu+,Na and LaOF:Eu3+ phosphors on color and luminous performances at 5600 K–8000 K WLEDs Ha Thanh Tung; Huu Phuc Dang
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp714-720

Abstract

In terms of lumen performance, the remote phosphor structure can yield better results than the conformal phosphor and in-cup phosphor packages. The application of such a package in LEDs might make the manipulation of the device’s chromatic performance challenging. Two remote phosphor packages are available for raising chromatic performance: one-layer and triple-layer phosphor adjustments. Using software simulation and phosphors created through specific procedures, our study was carried out to select the best adjustment that provides the best results in white LEDs (WLEDs) implemented with many chips: color rendering index (CRI), color quality scale (CQS), lumen output (LO), along with chromatic uniformity. We utilized the WLEDs at five temperatures of color between 5,600 K and 8,500 K. From the outcome, we can consider the three-layer phosphor package to have greater CRI, CQS, and lumen efficiency (LE). Notably, CQS and LE receive a roughly 30% boost compared to singular-layer package. The package can also reduce the chromatic deviation by roughly 30% to 50%, and therefore, grants a boost in chromatic homogeneity. To authenticate these outcomes, the dispersion attribute underwent examination in the layers of phosphor based on Mie-dispersion hypothesis. The outcome may prove useful for creating WLED devices with greater standards.
Raga classification using enhanced spatial bound whale optimization algorithm Bettadamadahally Shivakumaraswamy Gowrishankar; Nagappa U. Bhajantri
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp825-837

Abstract

A raga is a unique set of notes with certain rules that carefully followed, retain and protect its purity and produce amazing musical effects. An automated raga transcription and identification is important for computational musicology, which is an important step for musicology for indexing, classifying, and recommending tunes. In the present research, the audio features such as mel frequency cepstrum coefficients (MFCCs), spectral flux, short time energy, audio feature extractor, and spectral centroid features are used for the prediction of a raga. The model showed more complexity which means it required lots of training data. The proposed enhanced spatial bound whale optimization algorithm (ESBWOA) is used that overcome the feature selection problem of high dimensional features. In addition to this, a weighted salp swarm algorithm (SSA) is used for selecting the tone-based features from the ragas based on amplitude or each raga sample. The features were fed for bidirectional long short-term memory (Bi-LSTM) network, which enhanced the success rate for raga identification and classification. The present research uses CompMusic dataset in the research work where 9 classes for Carnatic music and 7 classes in Hindustani music are considered for the classification of ragas.
Simulation model of ANN and PID controller for TCP/AQM wireless networks by using MATLAB/Simulink Manal Hadi Jaber; Manal Kadhim Oudah; Salam Waley Shneen
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp739-747

Abstract

The wireless network transmission control protocol/active queue management (TCP/AQM) is a network that was chosen as a topic for research a basis is laid to simulate the proposed network and to conduct the simulation under certain conditions. To manage the queue control protocol (TCP/AQM) was chosen. The solution for many modern systems depends on placing additional units called controllers, which work to improve the performance of the work of the systems. The current simulation system can be described according to the test cases that were conducted, where four test cases were identified with sequential steps. In this work there are two control methods by simulation and mathematical model of wireless network TCP/AQM with proportional, integral and derivative (PID) controller and neural network. Simulation is conducted for cases in order to determine the performance of each case through comparison according to appropriate criteria to determine the best. The first case is a wireless communication network system with with a traditional controller PID type. The second is a wireless communication network system, a large neural network controller. Simulations were conducted to choose the best methods among those suggested for nonlinear systems and to enhance and achieve the possibility of adopting MATLAB to perform the required simulation.
Application of smartphone in recognition of human activities with machine learning Sabah Mohammed Fayadh; Elham Mohammed Thabit A. Alsaadi; Huda Hallawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp860-869

Abstract

The aim of activity recognition is to determine the physical action being performed by one or more users based on a series of observations made during the user's actions in the relevant environment. Significant advancements in the field of human activity have resulted in the creation of novel ways for supporting elderly persons in doing their tasks independently. Using ambient computing, this type of service will be manageable. Many of services are provided by ambient technology, involving home automation tools, monitoring the behaviour of diseased individuals, and utility management. Numerous academics are focusing their efforts on computer software architectures, system infrastructure, and distributed applications utilising sensor devices. Aim of this project is to develop an algorithm that can perform human activity recognition (HAR) better than the existing state-of-the-art approach. Several tasks must be done to achieve this goal. To compete with an existing HAR system, this study will rely on secondary data from the cutting-edge experiment; no new data will be collected. The central experiment will be used to quantitatively identify the best classifier based on prediction accuracy. The current study entails monitoring and assessing existing literature in order to generate hypotheses that may be tested via experiment.
Prediction of heart disease outcomes using machine learning classifier Kehinde Marvelous Adeniyi; Olasunkanmi James Oladapo; Timothy Oluwaseun Araoye; Taiwo Felix Adebayo; Sochima Vincent Egoigwe; Mathew Chinedu Odo
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp917-926

Abstract

The responsibility of heart organ is to supply blood to every part of the human body. The method of diagnose heart disease in medical hospital is extremely costly and also consume doctors time of operations. This research work applied forward, backward, and enter method for selection of variables in the logistic regression model, sensitivity, specificity, accuracy, and area under characteristic curve (AUC). The logistic regression model, at 5% level of significance with the enter method is used which denotes that the risk variables associated with heart disease gives accuracy of 87.9%. The preferred model of variable selection method used was the model from forward which has 88.6%. Also using the forward method of variables selection, the process produces 10 models with the best accuracy of 88.6%. The specificity and sensitivity of the analysis model was 91.4% and 85.6%. Also, the misclassification rate was also 11.4%, Positive predicted value is 87% and negative predicted value is 90.5%. Finally, the suitable model to predict the heart disease is from the forward method of variables selection and the positive likelihood ratio is 6 i.e the patients are 6 times likely to have the heart disease and the model has AUC value of 1.

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