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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
Summarizing twitter posts regarding COVID-19 based on n-grams Noralhuda N. Alabid; Zahraa Naseer
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.pp1008-1015

Abstract

The COVID-19 pandemic announced by the World Health Organization has disrupted human lives at different scales, including the economy, public health, and people's emotions. Social media databases record huge accumulated information concern this pandemic. Twitter platform is considered one of the most active social media that enable users to tweet in different conversations they are concerned about. The problem arises when tweeters want to search about a specific topic. They can only sort tweets by its recency to understand conversation and not by relevancy. This makes tweeters read through the most tweets to understand what was firstly discussed about the related topic. Some strategies were developed for summarizing tweets but summarizing topics of COVID-19 are still at the beginning. The current research aims to introduce a technique to present a short summary related COVID-19 topics with consuming little time and effort. Thus, summarization task started by clustering topics based on latent dirichlet allocation (LDA) method and K-means clustering and then selected the important sentences to format summarization. The study also compares bigram-based and unigram-based summarization. Different metrics were used to evaluate results and experiments at each stage, and the output of the proposal system was evaluated using ROUGE metrics.
IoT framework of telerehabilitation system with wearable sensors for diabetes mellitus patients Muhammad Zakwan Abdul Karim; Rozita Jailani; Ruhizan Liza Ahmad Shauri; Norashikin M. Thamrin
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.pp1023-1031

Abstract

Physical activity is commonly used as a treatment for diabetes patients, although its effectiveness in improving cognitive functions such as learning, thinking, remembering, and decision making is not clear. Regular exercise can gradually improve metabolic abnormalities associated with pre-diabetes and assist patients with type-2 diabetes (T2D) in managing their pharmacological treatment. The usage of mobile health (mHealth) as a tool to help diabetes patients with their diabetes self-management have been demonstrated in previous studies and it can lead to reductions in glycosylated hemoglobin (HbA1c) levels. Heart rate readings during physical activity is beneficial for healthcare professionals (HCP) to ensure appropriate intensity levels for their patients is achieved. Additionally, the list of the tailored physical activities is long, and it is quite challenging for the T2D patients to remember. Therefore, Tele-DM is proposed, consisting of a smartwatch and mobile application that enable remote physiotherapy sessions for T2D patients. The smartwatch transfers the heart rate data to Tele-DM through Google Fit database. The system provides tailored exercise programs to help patients reduce their weight and HbA1c levels. With the ability to facilitate two-way communication between HCP and T2D patients, the Tele-DM system is designed to enable an effective remote rehabilitation process.
Efficient automated car parking system based modified internet of spatial things in smart cities Noor Alsaedi; Ali Sadeq Abdulhadi Jalal
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.pp1164-1170

Abstract

The technological advances of smart cities have been progressively increasing to improve the quality of life to humans, especially in urban mobility. Parking appears to be a major issue, with residents needing to find a suitable parking space among many parking areas, resulting in time and fuel waste as well as environmental pollution. We propose in this paper a new automated system model that integrates reinforcement learning (RL), Q-learning, and image processing algorithms based on modified Internet of Spatial Things (IoST) architecture to optimize automated parking in smart cities. For demonstrating the efficiency of the proposed model, iFogSim simulation is used to reduce network usage and latency. Moreover, it deploys heterogeneous devices in multi layers and different scenarios. The experimental results show that the suggested system for automated car parking in fog-based placement-IoST network is feasible and effective. it minimizes latency and the total network usage compared to the cloud-based placement of the implemented system.
Time series prediction of personalized insulin dosage for type 2 diabetics Jisha G.; Nikhila T. Bhuvan; Ritta Jerrard
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.pp1080-1087

Abstract

Careful blood glucose monitoring and consistent insulin administration are necessary for managing diabetes. People with demanding schedules or little access to medical personnel may find this difficult. Fortunately, without having to visit a doctor every day, daily insulin dosage may now be customized to a person’s unique needs using technology and customised algorithms based on their food intake, exercise routines, and blood glucose levels. This information can be entered into a diabetes management app or device, where an algorithm will determine the proper insulin dosage and offer real-time feedback to assist maintain ideal blood glucose levels. A patient's dietary preferences, degree of physical activity, and blood sugar are taken into account for determining the proper bolus and basal insulin dosages in this study. According to the tracked body data, a patient’s appropriate insulin dosage is predicted using artificial neural network (ANN)-based models. Based on patient activity, food intake, exercise, and past insulin administration, insulin projections are created. To forecast an individual’s basal and bolus insulin requirements, long short-term memory (LSTM) and random forest regression models are employed. Accuracy of both models are tested and random forest regression shows better accuracy which is used in the prediction system.
Parameterized SDRAM-based content-addressable memory on field programmable gate array Binh Dang; Minh Bui; Nguyen Trinh Vu Dang; Linh Tran
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.pp669-680

Abstract

Contents-addressable memory (CAM) is a special memory that searches the input data with the entire pre-loaded database and generates corresponding address information. CAMs are advancing to be a core technology in computer networking systems. As field programmable gate array (FPGA) is recently being used for network acceleration applications, the demand to integrate CAM on FPGA is increasing. FPGA-based CAMs are divided into three categories of implementation: register-based, block RAM (BRAM)-based, and distributed RAM-based CAM. However, they come with a cost of excessive resource usage. Besides, the collision ratio is high in FPGA-based CAMs, leading to data loss and failure to produce accurate addresses. Synchronous dynamic random-access memory (SDRAM)-based CAMs, benefiting from the features of high density and low price of SDRAM, solve the limitations of FPGA’s on-chip resources. This paper proposes a data collision CAM hardware implementation using modern FPGA’s off-chip SDRAM for data storage. The hardware architecture is customized for massive lookup tables and resource-saving. Furthermore, the architecture is parameterized, which is better for integration. The synthesis results and comparisons show significant advancement compared to other FPGA-based CAM implementations by total reduction of on-chip RAM. The novel architecture shows remarkable improvement in the memory depth and width with the capacity of 128 Mbyte lookup table.
An improved clustering based on K-means for hotspots data Rani Rotul Muhima; Muchamad Kurniawan; Septiyawan Rosetya Wardhana; Anton Yudhana; Sunardi Sunardi; Mitra Adhimukti
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.pp1109-1117

Abstract

Riau province is one of the provinces in Indonesia where forest fires frequently occur every year. Hotspot data is geothermal points and they can be utilized as an indicator of forest fires. Clustering’s method can be used to analyze potential forest fires from hotspot data’s cluster pattern. In this study, hybrid genetic algorithm polygamy with K-means (GAP K-means) was used for hotspot data clustering. GA polygamy was used to determine the initial centroid of K-means. It was used to solve the sensitivity of K-means to the initial centroid, and to find the optimal solution faster. Experimentally compared the performance of GAP K-means, GA K-means, and K-means on the hotspots data, two artificial datasets, and three real-life datasets. Sum square error (SSE), davies bouldin index (DBI), silhouette coefficient (SC) and F-measure are used to evaluation clustering. Based this experiment, GAP K-means outperforms than K-means but GAP K-means still not fast to achieve convergent than GA K-means.
Towards a new healthy food decision-making system Ahmad Outfarouin; Nourane Laaffat
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.pp1088-1098

Abstract

Latterly, food recommendation systems have received high attention due to their importance to healthy living. On the recommendation domain, most studies focus on recommendations that suggest healthy products for each user based on their profiles. These types of recommender systems offer additional functionality to persuade users to change their buying behavior profitably. However, these systems must highlight the health preferences of the users and their health problems must be adequately taken into account. In this work, healthy food products recommender systems (RS) are our interest study and more specifically using content-based filtering. We represented this content by the food product composition. Our goal was to provide a healthy recommendation to consumers or citizens around the world, especially at this time when disease abounds. Thus, we developed our new healthy recommendation system (HRS). In this paper, we present a new recommendation process for individuals in the area of healthy eating. Furthermore, we analyze the existing state of the art in recommender system techniques and implement an algorithm that responds to this new process with very satisfactory results from the beginning, to conclude we discuss the research challenges related to the development of this kind of HRS.
Citrus leaves disease diagnosis Emad A. Mohammed; Ghasaq Hashim Mohammed
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.pp925-932

Abstract

Agriculture is the most important sector in developing countries, so the main source of concern for farmers is plant diseases that lead to a lack of production and a waste of money and crops. In this paper, a system using computer-assisted convolutional neural networks (CNN) with camera is developed to characterize diseases of citrus trees. This proposed system can help farmers to increase and improve the quality of their agricultural productivity. In addition to reducing the spread of the disease through early detection. Citrus leaf dataset was created to train and test the model because citrus is one of the main crops in Iraq. The results of the experiment shown that the implemented CNN achieved high classification accuracy of (92%) with fewer parameters, making it flawless and promising outcomes.
FunAR-furniture augmented reality application to support practical laboratory experiments in interior design education Fairuz Iqbal Maulana; Baskoro Azis; Tiara Ika Widia Primadani; Pangeran Rasyach Artha Hasibuan
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.pp845-855

Abstract

Practical learning in the laboratory has been limited due to the COVID-19 pandemic. Conventional education must adapt to increasingly digital technological developments. The design of interactive learning media with augmented reality technology can be a solution and support the previous conventional learning. FunAR is a Furniture Augmented Reality Application developed using the analysis, design, development, implementation, and evaluation (ADDIE) method consisting of ADDIE. In its implementation, the FunAR application will provide basic information about existing equipment in Lab Furniture, accompanied by 2D images, and augmented reality technology that creates 3D objects from each piece of equipment in Lab Furniture. From 15 student respondents, the results reveal that distance, angle, and device specifications significantly impact camera marker reading. A distance of 20 cm to 80 cm and an angle of 25° to 100° can display 3D objects. Likewise, the camera’s ability to process reading markers is better on smartphones with better hardware specifications. The results of the respondents showed that 80% of respondents were satisfied with the FunAR application.
An improved features selection approach for control chart patterns recognition Waseem Alwan; Nor Hasrul Akhmal Ngadiman; Adnan Hassan; Mohd Syahril Ramadhan Mohd Saufi; Azanizawati Ma’aram; Ibrahim Masood
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.pp734-746

Abstract

Control chart patterns (CCPs) are an essential diagnostic tool for process monitoring using statistical process control (SPC). CCPs are widely used to improve production quality in many engineering applications. The principle is to recognize the state of a process, either a stable process or a deterioration to an unstable process. It is used to significantly narrow the set of possible assignable causes by shortening the diagnostic process to improve the quality. Machine learning techniques have been widely used in CCPs. Artificial neural networks with multilayer perceptron (ANN-MLP) are one of the standard tools used for this purpose. This paper proposes an improved features selection method to select the best features as input representation for control chart patterns recognition. The results demonstrate that the proposed approach can effectively recognize CCPs even for small patterns with a mean shift of less than 1.5 sigma. The dimensional reduction was achieved by employing Relief, correlation, and Fisher algorithms (RCF) for feature selection and (ANN-MLP) as a classifier (RCF-ANN). This study provides an experimental result that compares the performance before and after dimensional reduction.

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