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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 66 Documents
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Search results for , issue "Vol 34, No 2: May 2024" : 66 Documents clear
A comparative analysis of cervical cancer diagnosis using machine learning techniques Abdikadir Hussein Elmi; Abdijalil Abdullahi; Mohamed Ali Bare
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1010-1023

Abstract

This study undertakes a comprehensive analysis of cervical cancer diagnosis using machine learning (ML) techniques. We start by introducing the critical importance of early and accurate diagnosis of cervical cancer, a significant health issue globally. The objective of this research is to compare the effectiveness of three ML algorithms: K-nearest neighbors (KNN), linear support vector machine (SVM), and Naive Bayes classifier, in predicting biopsy results for cervical cancer. Our methodology involves utilizing a substantial dataset to train and test these algorithms, focusing on performance measures like accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC). The findings reveal that KNN demonstrates superior performance, with high precision, recall, accuracy, and F1 score, alongside a notable AUC. This suggests KNN's potential utility in clinical applications for cervical cancer prognosis. Meanwhile, linear SVM and Naive Bayes exhibit certain limitations, indicating a need for further optimization. This study highlights the promising role of ML in enhancing medical diagnostic processes, particularly in oncology.
Exploring open source and proprietary LoRa mesh technologies Mustapha Hammouti; Omar Moussaoui; Mohammed Hassine; Abdelkader Betari
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp960-969

Abstract

This paper explores low power wide area network (LPWAN) LoRa and its diverse variants, encompassing open-source and proprietary wireless mesh networks, operating over the physical LoRa or LoRaWAN layer. The primary challenge lies in defining an optimal LoRa mesh solution that balances cost-effectiveness, energy efficiency, low latency, long-range capability, and security. This study also comprehensively examines key LoRa mesh solutions from 2017 to 2024, as proposed by various authors. Furthermore, a detailed analysis is conducted to contrast open-source and commercial solutions, considering their applications, limitations, issues, characteristics, and pros and cons of mesh routing protocols. In the current landscape, the proliferation of open-source and proprietary LoRa mesh solutions has been instrumental in facilitating the connectivity of internet of things (IoT) devices. However, these solutions pose challenges related to energy consumption, latency, and suboptimal transmission throughput. These challenges are influenced by various LoRa characteristics such as spectrum factor, bandwidth, and transmission power, which directly impact the transmission range. Our research aims to perform a comparative analysis of existing LoRa mesh solutions by, systematically studying their advantages and disadvantages. This analysis offers valuable insights for making informed choices among these solutions in diverse domains for IoT applications.
A proposed model for enhancing e-bank transactions: an experimental comparative study Wael Alzyadat; Ameen Shaheen; Ala’a Al-Shaikh; Aysh Alhroob; Ziyad Al-Khasawneh
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1268-1279

Abstract

In this paper, we introduce a novel approach to address the dynamic prediction of customer activity in electronic payment transactions for individual clients. Our approach is founded on customer online payment transaction records from registered UK-based online retailers between 01/12/2009 and 09/12/2011. These retailers primarily specialize in unique gift items for various occasions, catering to a wide range of clients, including wholesalers. We used classification analysis based on the correlation coefficient to measure and describe a customer's electronic payment capability based on the quality of products they purchase. Furthermore, we trained multi-layered models (linear model, deep learning, random forest, and support vector machines (SVM)) to capture the dynamics of e-bank transaction reinforcement for retail customers using machine learning. Real transaction data from a UK online retailer was employed in our study. The experimental results consistently demonstrated the effectiveness of our proposed strategy.
Augmented reality for anatomy course for children with autism Misael Lazo-Amado; Laberiano Andrade-Arenas
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1246-1257

Abstract

Autism spectrum disorder (ASD) that mainly affects social interaction and effective communication, showing problems in their learning, reflected in the lack of attention in schools, such as in anatomy classes. The main objective of the project is to develop a mobile application for children with autism to improve their learning in Anatomy and social interaction through augmented reality (AR). The methodology to be used is ADDIE which is in charge of analyzing the experience of the work team to find the ease of software development, proposing the efficient development with continuous improvement according to the evaluation to the specialists. The results show the evaluation of the specialists on the mobile application with AR where they will indicate their satisfaction with the application. In conclusion, it will be shown a mobile application with AR that offers a technological and eye-catching novelty for users in order to improve the educational development of children with autism spectrum disorder.
Enhancing lung lesion localization in CT-scans: a novel approach using FE_CXY and statistical analysis Nurul Najiha Jafery; Siti Noraini Sulaiman; Muhammad Khusairi Osman; Noor Khairiah A. Karim; Mohd Firdaus Abdullah; Iza Sazanita Isa; Zainal Hisham Che Soh
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp913-925

Abstract

Intelligence algorithm systems rely on a large dataset to effectively extract significant features that can recognize patterns for classification purposes and extensively utilized to assist the physicians in diagnosis of lung cancer. Extracting valuable features from the available dataset is crucial, especially in cases where additional real data may not be readily accessible. In this context, we propose a novel method called feature extraction based on centroid (FE_CXY) for lesion localization, utilizing a statistical approach. The approach begins with a segmentation process that employs image processing techniques to extract features of interest which is data centroid. This extracted data is then used to compute statistical measurements, revealing hidden patterns that contribute to distinguishing between lesion and non-lesion locations. The method’s efficiency is reflected in the development of robust models with improved performance in localizing lung lesions. The study’s statistical findings strongly indicate that FE_CXY plays a crucial role as an important feature for detecting lesion localization supported by a student’s t-test, which identifies a statistically significant difference in the patterns between lesion and non-lesion localization (p<0.05). By incorporating this method into lung cancer detection systems, we anticipate improved accuracy and efficacy, thereby benefiting early diagnosis and treatment planning.
An improve unsupervised discretization using optimization algorithms for classification problems Rozlini Mohamed; Noor Azah Samsudin
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1344-1352

Abstract

This paper addresses the classification problem in machine learning, focusing on predicting class labels for datasets with continuous features. Recognizing the critical role of discretization in enhancing classification performance, the study integrates equal width binning (EWB) with two optimization algorithms: the bat algorithm (BA), referred to as EB, and the whale optimization algorithm (WOA), denoted as EW. The primary objective is to determine the optimal technique for predicting relevant class labels. The paper emphasizes the significance of discretization in data preprocessing, offering a comprehensive approach that combines discretization techniques with optimization algorithms. An investigative study was undertaken to assess the efficiency of EB and EW by evaluating their classification performance using Naive Bayes and K-nearest neighbor algorithms on four continuous datasets sourced from the UCI datasets. According to the experimental findings, the suggested EB has a major effect on the accuracy, recall, and F-measure of data classification. The classification performance using EB outperforms other existing approaches for all datasets.
Comparison of ARIMA boost, Prophet boost, and TSLM models in forecasting Davao City weather data Jamal Kay B. Rogers; Tamara Cher R. Mercado; Fredelino A. Galleto Jr.
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1092-1101

Abstract

The geography of the Philippines experiences climate variability thus, providing accurate and timely weather forecasts to the population is crucial. Climate forecasts, which are issued and disseminated by government agencies, serve as essential risk management tools. However, the country faces challenges in forecasting, further exacerbated by climate change. Thus, exploring the use of artificial intelligence has emerged as a strategy to enhance weather prediction accuracy. This research focuses on time series forecasting of rainfall, mean temperature, relative humidity, and wind speed weather data using a machine learning approach. Specifically, it aims to compare and identify the most beneficial forecasting models among autoregressive integrated moving average (ARIMA) boost, Prophet boost, and time series linear model (TSLM). It also seeks to evaluate the performance of these models using mean absolute error (MAE), mean absolute percentage error (MAPE), mean absolute scaled error (MASE), symmetric mean absolute percentage error (SMAPE), root mean squared error (RMSE), and R squared (RSQ) metrics. Results showed that the selection of the forecasting model varies based on the specific parameter under consideration, with no hyperparameter tuning in the analysis. For wind speed, ARIMA boost proves to be a favorable choice. At the same time, TSLM demonstrates effectiveness for relative humidity and mean temperature. Both ARIMA boost and TSLM exhibit strong performance for rainfall. Prophet boost consistently ranks as the least-performing model.
Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning Satyaprakash Swain; Binod Kumar Pattnayak; Mihir Narayan Mohanty; Suvendra Kumar Jayasingh; Kumar Janardan Patra; Chittaranjan Panda
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1192-1203

Abstract

Cattle diseases can significantly impact on livestock health and agricultural productivity is substantial. Timely detection and prognosis of these diseases are essential for prompt interventions and preventing their spread within the herd. This study delved into employing machine learning models to anticipate cattle diseases based on relevant parameters. These parameters encompass milk fever, milk clots, milk watery, milk flake, blisters, lameness, stomach pain, gaseous stomach, dehydration, diarrhea, vomiting, abdominal issues, and alkalosis. A dataset of 2,000 samples from diverse cattle populations was amassed, each tagged with the presence or absence of specific diseases. The primary goal was to compare the efficacy of five well-known machine learning models: Naïve Bayes multinomial (NBM), lazy-IBk, partial tree (PART), random forest (RF), and support vector machine (SVM). The findings underscored the consistent superiority of RF in comparison to the other models, boasting the highest accuracy in predicting cattle diseases. The RF model exhibited an accuracy rate of 88% on the test dataset. This achievement can be ascribed to its capacity to handle intricate interactions among input features and mitigate over fitting through ensemble learning. These insights can furnish valuable information about early indicators and risk factors associated with diverse cattle diseases.
Cross-layer multipath routing approach and link quality indicator for QoS provisioning in mobile WMSN Bharati S. Pochal; Jayashree Agarkhed; Siddarama R. Patil
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1288-1294

Abstract

With the recent advancement in mobile adhoc networks (MANET’s) and technology, applicability, and integration of wireless multimedia sensor networks (WMSN) in MANET’s has led to creation of smart distributed system for high-speed mobile multimedia streaming and real time multimedia traffic transmissions. In this paper, we propose cross-layer multipath routing approach with link quality indicator (CLMRLQI) to compute stable link between two nodes. CLMRLQI discovers stable multipath routes by considering cross-layer routing metrics such as energy and bandwidth to support quality of service (QoS). The simulation scenarios are carried on network simulation tool and QoS parameters such as throughput, PDR, delay, overhead and energy consumption are analysed.
Evaluating the efficacy of univariate LSTM approach for COVID-19 data prediction in Indonesia Tegar Arifin Prasetyo; Joshua Pratama Silitonga; Matthew Alfredo; Risky Saputra Siahaan; Roberd Saragih; Dewi Handayani; Rudy Chandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1353-1366

Abstract

The coronavirus disease 2019 (COVID-19) pandemic, originating in 2020, has emerged as a critical global issue due to its rapid and widespread transmission. Indonesia, among the affected nations, has taken measures to address the situation, including the development of a deep learning model for predicting future COVID-19 infection and spread. This predictive tool serves as a valuable reference for the government and stakeholders, aiding them in making informed decisions and implementing appropriate measures to contain the virus. The deep learning model employs the long short-term memory (LSTM) algorithm, chosen for its ability to recognize temporal patterns in the country’s COVID-19 data. The model creation process involves data collection, preprocessing, model architecture planning, modeling, training, and evaluation. Two LSTM models were developed: a univariate and a multivariate model. Following thorough training and evaluation, the univariate model emerged as the superior choice, boasting evaluation metrics of 16.72 for mean absolute percentage error (MAPE) and 66.36 for root mean squared error (RMSE). This model was then deployed on a publicly accessible website, presenting visualizations of past COVID-19 data and predictions of future cases through line graphs. This user-friendly platform enables the public to access and analyze the data easily.

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