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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 36, No 2: November 2024" : 64 Documents clear
Enhancing diagonal comprehension with advanced topic modeling technique: DIAG-LDA Sifi, Fatima-Zahrae; Sabbar, Wafae; El Mzabi, Amal
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1261-1272

Abstract

With the speed increase of reviews or other forms of text, natural language has the ability to convey large and complex amounts of information in relatively small communications. This capability is being leveraged by the machine-learning algorithm known as latent dirichlet allocation (LDA), which can be utilized to discover latent topics within documents. LDA can be also used to generate summaries or abstracts from a given set of documents. However, LDA can struggle to identify topics in short documents or in data with high levels of noise. This article will introduce a new method for topic modeling with LDA based on diagonal reading for sentences (DIAG-LDA). Primarily, the features are selected using the TF-IDF algorithm, and the highest relevant features are extracted using the confidence value. Besides, the classification step is executed utilizing the LDA classifier. Ultimately, we evaluate our model using the convolutional neural network algorithm. The experiment results show that DIAG-LDA performs well in identifying features from text data, achieving a 94.4%, and 89.5% in accuracy for the datasets on international economics and the political economy.
Mutual information-MOORA based feature weighting on naive bayes classifier for stunting data Prabiantissa, Citra Nurina; Hakimah, Maftahatul; Rozi, Nanang Fakhrur; Puspitasari, Ira; Yamani, Laura Navika; Mahendra, Victoria Lucky
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp972-982

Abstract

One effort to reduce stunting rates is to predict stunting status early in toddlers. This study applies Naive Bayes (NB) to build a stunting prediction model because it is simple and easy to use. This study proposes a filter-based feature weighting technique to overcome the NB assumption, which states that each feature has the same contribution to the target. The frequency of an event in a dataset influences the feature weighting using mutual information criteria. This is the gap in the filter-based ranking highlighted in this study. Therefore, this study proposes a feature-weighting method that combines mutual information with the MOORA (MI-MOORA) decision-making method. This technique makes it possible to include external factors as criteria for ranking important features. For stunting cases, the external consideration for ranking purposes is the assessment of nutrition experts based on their experience in dealing with stunted toddlers. The MI-MOORA technique makes the availability of clean water the most influential feature that contributes to the stunting status. In the ten best features, the MI-MOORA ranking results are dominated by family factors. Based on the performance evaluation results of NB and other classifiers, MI-MOORA can improve the performance of stunt prediction models.
Ultra-miniaturized dual-band implantable antenna for retinal prosthesis Bousrout, Abdelmouttalib; Khabba, Asma; Ibnyaich, Saida; Mazri, Tomader; Habibi, Mohamed; Sutikno, Tole
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp760-776

Abstract

This article presents two miniaturized antennas designed for retinal prosthesis devices, aimed at enhancing vision for blind individuals with functional optic nerves. The implantable antenna is 2.2 mm wide, 2.15 mm tall, and 0.78 mm thick. It works in the ISM bands but is small because it uses slot incorporation and high-permittivity substrates. High-frequency structure simulator (HFSS) electromagnetic simulations show great performance, with a 16.66% impedance bandwidth at 2.4 GHz and a 10.34% bandwidth at 5.8 GHz. The peak gain values are -27.76 dB at 2.4 GHz and -16.40 dB at 5.8 GHz. We have also developed an extraocular antenna for telemetry and energy transfer, with dimensions of 36×36×1.6 mm3 . Validation through CST calculation software confirms the efficacy of both antenna designs. Implantable antennas hold significant promise in biomedical antenna research, demonstrating capabilities conducive to retinal implantation and offering potential advancements in vision restoration technology.
Electroencephalography biometric authentication using eye blink artifacts Madile, Thamang Teddy; Hlomani, Hlomani B.; Zlotnikova, Irina
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp872-881

Abstract

This study presents a novel approach to electroencephalography (EEG) biometric authentication using eye blink artifacts. Unlike traditional methods that rely on imagination and mental tasks, which are susceptible to emotional and physical variations, this approach leverages the consistent effects of eye blinks on brainwaves for authentication. Brainwaves were recorded using the NeuroSky Mindwave Mobile 2 headset, and eye blinks were extracted via NeuroSky’s blink detection algorithm. An authentication algorithm was developed based on blink strength, time, and frequency. The proposed method demonstrated high performance with an accuracy (ACC) of 97%, a false acceptance rate (FAR) of 5%, and a false rejection rate (FRR) of 1%. This study also explored the impact of emotions and physical exercise on the authentication process, confirming the method's robustness under varying conditions. These findings suggest that eye blink artifacts offer a reliable and practical biometric trait for EEG-based authentication systems, providing a secure alternative to traditional biometric methods. The substantial contribution of this research lies in demonstrating the superior stability and usability of eye blink-based EEG authentication under diverse conditions, compared to existing EEG authentication methods that often require mental tasks or multi-channel recordings.
Comparative analysis on liver benchmark datasets and prediction using supervised learning techniques Balakrishna, Tilakachuri; Annam, Jagadeeswara Rao; Haritha, Dasari
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1043-1051

Abstract

Disease diagnosis is most challenging task today. Different datasets are available in web source that contains important features to diagnose the diseases. This paper explores different classification algorithms on medical liver bench mark datasets like BUPA and Indian Liver patient dataset (ILPD). The ILPD is best fit for the model and also gives high classifier accuracy. In proposed model the following classifiers like Naïve Bayes (NB), support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) classification, multi-layer perceptron (MLP), artificial neural network (ANN), deep belief network (DBN) and probabilistic neural network (PNN) are used. The results shown that ILPD is best dataset for all classifiers and RF classification in particular is best classifier.
Utilizing association rule mining for enhancing sales performance in web-based dashboard application Teja Nursasongka, Raden Mas; Fahrurrozi, Imam; Oktiawati, Unan Yusmaniar; Taufiq, Umar; Farooq, Umar; Alfian, Ganjar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1105-1113

Abstract

Data is increasingly recognized as a valuable asset for generating new insights and information. Given the importance of data, businesses must always look for ways to get more value from data generated from sales transactions. In data mining, association rule mining is a good standard technique and is widely used to find interesting relationships in databases. Association rule is closely related to market basket analysis to find items that often appear together in one transaction. This study proposes the frequent pattern growth (FP-Growth) algorithm in finding association rules on sales transaction data. Our methodology includes dataset preparation for modeling, evaluation of model performance, and subsequent integration into a web-based platform. We conducted a comparative analysis of the FP-Growth algorithm against the Apriori algorithm, finding that FP-Growth outperformed Apriori in efficiency. Using the same dataset and constraint level, both algorithms produce the same number of frequent itemsets. However, in terms of computation time, FP-Growth excels by taking 2.89 seconds while Apriori takes 5.29 seconds. We integrated trained FP-Growth algorithm into a web-based dashboard application using the streamlit framework. This system is anticipated to simplify the process for businesses to identify customer purchasing patterns and improve sales.
Parameter tuning for enhancing performance of a variant of particle swarm optimization algorithm Kumar, Ashok; Kumar, Sheo; Tiwari, Rajesh; Saxena, Shalya; Singh, Anurag
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1253-1260

Abstract

There is dependably an extraordinary requirement for new types of algorithms in the population-based improvement algorithm. These algorithms improve the execution of the current algorithm. Parameter change approach assumes an essential job in improving the execution of the PSO algorithm. A new algorithm called particle acceleration-based particle swarm optimization (PA-PSO) has been proposed. In this algorithm a particle acceleration parameter is tuned. This algorithm significantly improves the performance of the PSO–time varying acceleration coefficients (PSO-TVAC) algorithm. This algorithm reduces the time varying weight of inertia and the nonlinear acceleration coefficients in the equation of the PSO-TVAC velocity vector in each iteration. Particle movements in the n-dimensional search space are governed by the kinetics of the second motion equation. Experiments demonstrate that the proposed PA-PSO algorithm outperforms the existing PSO-TVAC algorithm on five well-known reference test functions. The algorithm possesses adequate control over the local as well as global optimums.
Improving quality of life through brain-computer interfaces: an integrated stress prediction method using machine learning Perur, Shrivatsa D.; Kenchannavar, Harish H.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1030-1042

Abstract

In recent days, people must deal with stress brought on by the demands of modern living, which constantly presents new obstacles. Stress, a state of mental tension triggered by challenging circumstances, has become a global risk factor impacting individual well-being. Understanding variations in stress resilience is crucial for tailoring treatment strategies. Previous studies have explored stress prediction using measures like electroencephalography (EEG), blood pressure (BP), heart rate (HR), and interventions such as Kriya Yoga and mindfulness meditation. The experimentation is done on the data collected from people who practice heartfulness meditation regularly. The research employs machine learning (ML) algorithms alongside physiological parameters such as EEG, BP, HR, and psychological parameters, perceived stress scale (PSS), to precisely classify, measure, and predict stress levels. The investigations are done using K-nearest neighbor (KNN), random forest (RF), and kernel-support vector machine (k-SVM). An accuracy of 98.27% accuracy was achieved with the RF algorithm in classifying stressed and non-stressed individuals.
Utilization of learning media based on augmented reality on design material network topology Rohandi, Manda; Pakaja, Jemmy A.; Mulyanto, Arip; Novian, Dian; A., Hermila; Ashari, Sri Ayu; Nugraha, Bariq
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1083-1091

Abstract

Augmented reality (AR) is a growing technology that has great potential in the field of education. This article explores using AR as an interactive learning medium in a secondary education environment. The study involves the implementation of AR in network topology material to enhance student engagement and understanding. This research consists of the design and development of AR applications following the curriculum of the network topology subject at SMK Negeri 1 South Bulango using the waterfall model. The results showed that using AR-based learning media can increase student engagement in the learning process. Three-dimensional visualization of network topology design can improve students’ interest and motivation to understand the material better. AR allows students to interact directly with the network topology design model.
Development of an IoT-based sleep pattern monitoring system for sleep disorder detection Md Shahrum, Muhammad Nur Ikhwan; Md Isa, Ida Syafiza; Mohd Shaari Azyze, Nur Latif Azyze; Nasir, Haslinah Mohd; Sutikno, Tole
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp777-784

Abstract

Inadequate sleep can cause various health problems including heart disease and obesity. In this work, a sleep monitoring system that monitors human sleep patterns is developed using the internet of things (IoT) and Raspberry Pi. The system is designed to record any detected movements and process the data using machine learning to provide valuable insight into a person’s sleep patterns including sleep duration, the time taken to fall asleep, and the frequency of waking up. This information is very useful to provide the sleep disorder diagnostics of an individual including restless leg, parasomnia and insomnia syndrome besides giving recommendations to improve their sleep quality. Also, the system allows the processed data to be stored in the cloud database which can be accessed through a mobile application or web interface. The performance of the system is evaluated in terms of its accuracy and reliability in detecting sleep order diagnostics. Based on the confusion matrix, the results show the accuracy of the system is 90.32%, 91.80%, and 91.80% in detecting the restless leg, parasomnia and insomnia syndrome, respectively. Meanwhile, the system showed high reliability in monitoring 10 participants for 8 hours and updated the recorded data and its analysis in the cloud.

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