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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 27, No 1: July 2022" : 64 Documents clear
Internet of things based wireless sensor network: a review Shayma Wail Nourildean; Mustafa Dhia Hassib; Yousra Abd Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp246-261

Abstract

Recently, Internet of Things (IoT) technologies are developing technology with a variety of applications. The Internet of Things (IoTs) is defined as a network of ordinary objects such as Internet TVs, smartphones, actuators and sensors that are smartly connected together to enable new types of communication between people and things as well as between things themselves. Wireless sensor networks (WSNs) play an important part in Internet of Things (IoT) technology. A contribution to wireless sensor networks and IoT applications is wireless sensor nodes’ construction with high-speed CPUs and low-power radio links. The IoT-based wireless Sensor network (WSN) is a game-changing smart monitoring solution. ZigBee standard is an important wireless sensor network (WSN) and Internet of Things (IoT) communication protocol in order to facilitate low-power, low-cost IoT applications and to handle numerous network topologies. This paper presented a review on the energy efficient and routing topologies of ZigBee WSN, applications of IoT enabled Wireless Sensor Network as well IoT WSN security challenges.
A semi-automated hybrid approach to identify radicalization on social digital platform Vandna Batra; Suresh Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp563-572

Abstract

The digital social platform is an important medium for sharing or communicating a message from one person to another or one to many. The growth of internet users and social media use has also led to many adverse consequences. Such a platform is also used for radical activity by spreading the radical message in public. The detection of such a message is impossible by human monitoring. Many researchers are continually working on automatic detection of such activity to find a way to stop it. Automatic identification is also not possible due to the massive amount of data present and ambiguity in messages. The proposed work presents a framework for detecting the radical message and taking action by automatically blocking it. A dataset of 33k tweets has been fetched from twitter based on radical words. Two machine learning models, first countervectorizer and Logistic regression-based and second convolutional neural networks (CNN) have been applied yielding 96.97% accuracy. The provision of human intervention is also given in doubt cases which helps further to improve the accuracy of overall model. The framework gives very good results in a simulated environment.
Design and modeling of solar water pumping system in Diyala region Mohammed Hasan Ali; Raghad Ali Mejeed
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp79-85

Abstract

In recent years, solar panels have become increasingly popular for converting solar energy is converted into electrical energy. The solar panel can be utilized as part of a larger solar system that is connected to the power grid or as a stand-alone system. Every day, the world receives 84 Terawatts of energy, yet we only use about 12 Terawatts. For optimal energy conversion, the tracking mechanism will keep the solar panel perpendicular to the sun at all times. In this setup, photo resistors will be employed as sensors. A light detection system, a microprocessor, a gear motor system, and a solar panel will make up the system. When compared to solar panels without tracking equipment, our system will produce up to 40% more electricity. Improvements to the board's efficiency include the addition of a dust sensor. The dust on the board is also detected by the sensor, which activates a pump inside the tank. It uses the Arduino to pump water onto the board to clean it of dust and maintain its efficiency. There is also a water sensor. When the tank's water level falls below a certain level, the attached pump activates.
Blockchain associated machine learning and IoT based hypoglycemia detection system with auto-injection feature Rahnuma Mahzabin; Fahim Hossain Sifat; Sadia Anjum; Al-Akhir Nayan; Muhammad Golam Kibria
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp447-455

Abstract

Hypoglycemia is an unpleasant phenomenon caused by low blood glucose. The disease can lead a person to death or a high level of body damage. To avoid significant damage, patients need sugar. The research aims at implementing an automatic system to detect hypoglycemia and perform automatic sugar injections to save a life. Receiving the benefits of the internet of things (IoT), the sensor’s data was transferred using the hypertext transfer protocol (HTTP) protocol. To ensure the safety of health-related data, blockchain technology was utilized. The glucose sensor and smartwatch data were processed via Fog and sent to the cloud. A Random Forest algorithm was proposed and utilized to decide hypoglycemic events. When the hypoglycemic event was detected, the system sent a notification to the mobile application and auto-injection device to push the condensed sugar into the victim’s body. XGBoost, k-nearest neighbors (KNN), support vector machine (SVM), and decision tree were implemented to compare the proposed model's performance. The random forest performed 0.942 testing accuracy, better than other models in detecting hypoglycemic events. The system’s performance was measured in several conditions, and satisfactory results were achieved. The system can benefit hypoglycemia patients to survive this disease.
Intelligent water flow monitoring system based on internet of things for residential pipeline Siti Sufiah Abd Wahid; Shakira Azeehan Azli; Mohd Sufian Ramli; Khairul Kamarudin Hasan
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp20-27

Abstract

Providing sustainable water supply is a huge challenge for Malaysia whereby the residential areas are still equipped with the conventional water meter with lack of monitoring options. In order to detect the locations of internal leakage, the process requires costly plumber service while manual comparison may be inaccurate and time-consuming. Therefore, digitalization transformation aligned with the industrial revolution IR 5.0 is crucial especially with the recent occurrences of high water bills reports during the movement control order (MCO). The objectives of this project is to develop an intelligent water flow monitoring system using Arduino as a microcontroller and to construct a system that can monitor the water usage behaviour at any distant with internet of thing (IoT). It can be installed anywhere in a pipeline whereby the water flow sensor measures the real-time water parameters. The data transferred to the cloud are sent to the homeowner to display the accuracy and availability of their water system via Blynk, a mobile-compatible and user-friendly application that generates clear data visualization. The key goal of this project is to provide a wireless, mobile, economical and systematic solution for residents to self-monitor their water consumption as compared to the conventional manual monitoring.
An enhanced hybridized approach for group recommendation via reliable ratings Rachna Behl; Indu Kashyap
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp413-421

Abstract

A group recommender system aim's to provide relevant information to all members of the group. To determine group preferences, the majority of existing studies use aggregation approaches. An aggregation method is a strategy for recommending products to a group by combining the individual preferences of group members. So far, a slew of different types of aggregation algorithms has been discovered. However, they only aggregate one component of the offered ratings (e.g., counts, rankings, high averages), which limits their ability to capture group members' proclivities. This study proposes a novel aggregation method called weighted count that aggregates ratings by providing weights equal to the number of users who provide ratings to an item (location). In addition, the study proposes combining additive utilitarian and weighted count approaches to highlight popular locations on which group members agreed. Experiments on a benchmark check-in dataset demonstrated that the proposed blended technique surpasses the existing methods significantly.
A comparative study to predict breast cancer using machine learning techniques Shiva Shankar Reddy; Neelima Pilli; Priyadarshini Voosala; Swaroop Ravi Chigurupati
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp171-180

Abstract

Detection of disease at the starting stage is a very crucial problem. As the population growth increases, the risk of death incurred by breast cancer rises exponentially. Breast cancer is the most common cancer in women, and it is also the most dangerous of all cancers. Deaths because of breast cancer have b een increasing in recent times. Earlier detection of the disease followed by treatment can reduce the risk and increase survival chances. There will be cases where even medical professionals can make mistakes in identifying the disease. This project deals with the detection of Breast cancer using the cell data of the tumor present in the breast. So, with the help of technologies in machine learning and artificial intelligence can substantially improve the diagnosis accuracy. The development of this project is beneficial in medical decision support systems. Several machine learning techniques, namely Adaboost, multi-layer perceptron (MLP) and stacking classifier; were used, and among all the algorithms, the stacking classifier results in the best accuracy. The accuracies 95.6%, 97.1%, and 99.2% respectively.
News classification using light gradient boosted machine algorithm Muhammad Hatta Rahmatul Kholiq; Wiranto Wiranto; Sari Widya Sihwi
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp206-213

Abstract

News classification is a complex issue as people are easily convinced of misleading information and lack control over the spread of fake news. However, we ca n break the problem of spreading fake news with artificial intelligence (AI), which has developed rapidly. This study proposes a news classification model using a light gradient boosted machine (LightGBM) algorithm. The model is analyzed using two feature extraction techniques, count vectorizer and Tfidf vectorize r and compared with a deep learning model using long - short term memory (LSTM). The experimental evaluation showed that all LightGBM models outperform LSTM. The best model is the count vectorizer Li ghtGBM, which achieves an accuracy value of 0.9933 and an area under curve (AUC) score of 0.9999.
An efficient authentication and key-distribution protocol for wireless multimedia sensor network Basavaraj Patil; Sangappa Ramachandra Biradar
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp347-354

Abstract

To provide security and privacy for multimedia data transmission, efficient techniques for authorizing and authenticating network users and nodes are required. These challenges have made it a vital and significant area of research in the present decade. Du e to resource constraints, existing systems are unable to provide adequate protection against vulnerable behaviors and security assaults such as black-hole, Sybil, man-in-the-middle, and other similar attacks. In this paper, an effective enhanced engineere d cementitious composites (ECC) and crypto-based authentication with a key exchange mechanism is proposed. The method boosts the effective authentication mechanism and reduces the number of vulnerable activities in the network. The simulation results demon strate that the suggested technique is robust to malicious assaults and performs mutual authentication efficiently. A cost-benefit analysis validates that the processing, communication, and storage requirements are much reduced when compared to existing ap proaches. Furthermore, an informal security analysis demonstrates that the suggested protocol is secure and adaptable to real-time scenarios.
Digital platform based on geomarketing as an improvement in micro and small enterprises Teófilo Crisóstomo-Berrocal; Fernando Sierra-Liñan; Cabanillas Carbonell-Michael
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp395-403

Abstract

After the situation generated by the pandemic caused by COVID-19, micro and small enterprises (MSEs) faced a complex reality, having to cope with business uncertainty. This research proposes a digital platform based on geomarketing as a growth and support strategy for MSEs, with the objective of improving their labor and capital productivity, through the incorporation of the technological factor, which will have a great impact on them, helping them to continue operating and not having to close their businesses. The platform was developed under the agile Scrum methodology because it is adaptable to the constant changes in the mobile application development process, having as indicators labor productivity and capital productivity. Finally, the results revealed that labor productivity increased by 30.86 percent, meaning that, for every hour worked per person, more sales were made. As for capital productivity, it decreased by 1.47 percent, meaning that investment decreased for each value added of each product sold.

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