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Journal of ICT Research and Applications
Published by Institut Teknologi Bandung
ISSN : 23375787     EISSN : 23385499     DOI : https://doi.org/10.5614/itbj.ict.res.appl.
Core Subject : Science,
Computer Science & IT
Journal of ICT Research and Applications welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless & Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 275 Documents
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Early Detection of Stroke for Ensuring Health and Well-Being Based on Categorical Gradient Boosting Machine Isaac Kofi Nti; Owusu Nyarko-Boateng; Justice Aning; Godfred Kusi Fosu; Henrietta Adjei Pokuaa; Frimpong Kyeremeh
Journal of ICT Research and Applications Vol. 16 No. 3 (2022)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2022.16.3.8

Abstract

Stroke is believed to be among the leading causes of adult disability worldwide. It is wreaking havoc on African people, families, and governments, with ramifications for the continent’s socio-economic development. On the other hand, stroke research output is insufficient, resulting in a dearth of evidence-based and context-driven guidelines and strategies to combat the region’s expanding stroke burden. Indeed, for African and other developing economies to meet the UN Sustainable Development Goals (SDGs), particularly SDG 3, which aims to guarantee healthy lifestyles and promote well-being for people of all ages, the issue of stroke must be addressed to reduce early death from non-communicable illnesses. This study sought to create a robust predictive model for early stroke diagnosis using an understandable machine learning (ML) technique. We implemented a categorical gradient boosting machine model for early stroke prediction to protect patients’ health and well-being. We compared the effectiveness of our proposed model to existing state-of-the-art machine learning models and previous studies by empirically testing it on a real-world public stroke dataset. The proposed model outperformed the others when compared to the other methods using the research data, achieving the maximum accuracy (96.56%), the area under the curve (AUC) (99.73%), F1-measure (96.68%), recall (99.24%), and precision (93.57%). Functional outcome prediction models based on machine learning for stroke were verified and shown to be adaptable and helpful.
Detection of Americans’ Behavior toward Islam on Facebook Qusai Abuein; Mohammed Q. Shatnawi; Lujain Ghazalat
Journal of ICT Research and Applications Vol. 16 No. 3 (2022)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2022.16.3.7

Abstract

Social network websites have become a rich place for detecting and analyzing people’s attitudes, perceptions, and feelings towards news, products,  and other real-world issues. Facebook is a popular platform among different age groups and countries and is generally used to convey ideas about certain topics based on likes, comments and sharing. In recent years, one of the most controversial topics were the idea behind Islamophobia and other ideas built in people’s minds about Islam around the world. This research studied the public opinion of American citizens about Islam during the presidency of Donald Trump, as that period was rich in diversity of opinion between his supporters and detractors. In this paper, sentiment analysis was used to analyze American citizens’ behavior towards posts about Islam during Trump’s presidency in various states across the United States. Sentiment analysis was performed on Facebook posts and comments extracted from American news channels from the year 2017. Several machine learning methods were used to detect the polarity in the dataset. The highest classification accuracy among the classifiers used in this research was achieved using a logistic regression classifier, reaching 84%.
Emergency Data Transmission Mechanism in VANETs using Improved Restricted Greedy Forwarding (IRGF) Scheme Kathirvelu Lakshmi; Manivasagam Soranamageswari
Journal of ICT Research and Applications Vol. 17 No. 1 (2023)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.17.1.3

Abstract

One of the most critical tasks in Vehicular Ad-hoc Networks (VANETs) is broadcasting Emergency Messages (EMs) at considerable data delivery rates (DDRs). The enhanced spider-web-like Transmission Mechanism for Emergency Data (TMED) is based on request spiders and authenticated spiders to create the shortest route path between the source vehicle and target vehicles. However, the adjacent allocation is based on the DDR only and it is not clear whether each adjacent vehicle is honest or not. Hence, in this article, the Improved Restricted Greedy Forwarding (IRGF) scheme is proposed for adjacent allocation with the help of trust computation in TMED. The trust and reputation score value of each adjacent vehicle is estimated based on successfully broadcast emergency data. The vehicles’ position, velocity, direction, density, and the reputation score, are fed to a fuzzy logic (FL) scheme, which selects the most trusted adjacent node as the forwarding node for broadcasting the EM to the destination vehicles. Finally, the simulation results illustrate the TMED-IRGF model’s efficiency compared to state-of-the-art models in terms of different network metrics.
Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons Parita Shah; Priya Swaminarayan; Maitri Patel
Journal of ICT Research and Applications Vol. 17 No. 1 (2023)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.17.1.1

Abstract

In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach.
Anomaly Detection for Human Home Activities Using Pattern Based Sequence Classification Rawan Mohammed Elhadad; Yi-Fei Tan
Journal of ICT Research and Applications Vol. 17 No. 1 (2023)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.17.1.4

Abstract

In most countries, the old-age people population continues to rise. Because young adults are busy with their work engagements, they have to let the elderly stay at home alone. This is quite dangerous, as accidents at home may happen anytime without anyone knowing. Although sending elderly relatives to an elderly care center or hiring a caregiver are good solutions, they may not be feasible since it may be too expensive over a long-term period. The behavior patterns of elderly people during daily activities can give hints about their health condition. If an abnormal behavior pattern can be detected in advance, then precautions can be taken at an early stage. Previous studies have suggested machine learning techniques for such anomaly detection but most of the techniques are complicated. In this paper, a simple model for detecting anomaly patterns in human activity sequences using Random forest (RF) and K-nearest neighbor (KNN) classifiers is presented. The model was implemented on a public dataset and it showed that the RF classifier performed better, with an accuracy of 85%, compared to the KNN classifier, which achieved 73%.
The Potential of a Low-Cost Thermal Camera for Early Detection of Temperature Changes in Virus-Infected Chili Plants Asmar Hasan; Widodo Widodo; Kikin Hamzah Mutaqin; Muhammad Taufik; Sri Hendrastuti Hidayat
Journal of ICT Research and Applications Vol. 17 No. 1 (2023)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.17.1.2

Abstract

One effect of viral infection on plant physiology is increased stomata closure so that the transpiration rate is low, which in turn causes an increase in leaf temperature. Changes in plant leaf temperature can be measured by thermography using high-resolution thermal cameras. The results can be used as an indicator of virus infection, even before the appearance of visible symptoms. However, the higher the sensor resolution of the thermal camera, the more expensive it is, which is an obstacle in developing the method more widely. This article describes the potential of thermography in detecting Tobacco mosaic virus infection in chili-pepper plants using a low-cost camera. A FLIR C2 camera was used to record images of plants in two treatment groups, non-inoculated (V0) and virus-inoculated plants (V1). Significantly, V1 had a lower temperature at 8 and 12 days after inoculation (dai) than those of V0, but their temperature was higher than V0 before symptoms were visible, i.e., at 17 dai. Thermography using low-cost thermal cameras has potency to detect early viral infection at 8 dai with accuracy levels (AUC) of 80.0% and 86.5% based on k-Nearest Neighbors and Naïve Bayes classifiers, respectively.
Sociable Robot ‘Lometh’: Exploring Interactive Regions of a Product-Promoting Robot in a Supermarket Nethmini Thilakshi Weerawarna; Udaka Manawadu; P. Ravindra S. De Silva
Journal of ICT Research and Applications Vol. 17 No. 1 (2023)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.17.1.5

Abstract

The robot ‘Lometh’ is an information-presenting robot that naturally interacts with people in a supermarket environment. In recent years, considerable effort has been devoted to the implementation of robotic interfaces to identify effective behaviors of communication robots focusing only on the social and physical factors of the addresser and the hearer. As attention focus and attention target shifting of people differs based on the human visual focus and the spatiality, this study considered four interactive regions, considering the visual focus of attention as well as the interpersonal space between robot and human. The collected primary data revealed that 56% attention shifts occurred in near peripheral field of view regions and 44% attention shifts in far peripheral field of view regions. Using correspondence analysis, we identified that the bodily behaviors of the robot showed the highest success rate in the left near peripheral field of view region. The verbal behaviors of the robot captured human attention best in the right near peripheral field of view region. In this experiment of finding a socially acceptable way to accomplish the attention attracting goals of a communication robot, we observed that the robots’ affective behaviors were successful in shifting human attention towards itself in both left and right far- peripheral field of view regions, so we concluded that for far field of view regions, designing similar interaction interventions can be expected to be successful.
The Utility of Decision Tree and Analytics Hierarchy Process in Prioritizing of Social Aid Distribution due to Covid-19 Pandemic in Indonesia Saucha Diwandari; Enny Itje Sela; Briyan Efflin Syahputra; Nathaniela Aptanta Parama; Anindita Septiarini
Journal of ICT Research and Applications Vol. 17 No. 1 (2023)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.17.1.6

Abstract

The Indonesian government provided various social assistance programs to local governments during Covid-19. One of the difficulties for the local governments in determining candidates for social aid is ensuring that the number of candidates is in balance with the available quota. Therefore, the local governments must select the most eligible candidates. This study proposes a priority model that can provide recommendations for candidates who meet the criteria for social assistance. The six parameters used in this study were: number of dependents, occupation, income, age, Covid status, and citizen status. The model operates in two stages, namely classification followed by ranking. The classification stage is conducted using a decision tree, while the ranking stage is performed conducted using the Analytical Hierarchy Process (AHP) algorithm. The decision tree separates two classes, namely, eligible and non-eligible. In addition, the classification process is also used to determine the dominant attributes and played a role in the modeling. The proposed model generates a list of the most eligible candidates based on our research. These are sorted by weight from greatest to most eligible using five dominant parameters: number of dependents, income, age, Covid status, and citizen status.
Machine Learning-based Early Detection and Prognosis of the Covid-19 Pandemic Ajitha Santhakumari; R. Shilpa; Hudhaifa Mohammed Abdulwahab
Journal of ICT Research and Applications Vol. 17 No. 2 (2023)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.17.2.6

Abstract

The outbreak of Covid-19 has caused a global health crisis, presenting numerous challenges to the healthcare system with its severe respiratory symptoms and variable presentation. Early and accurate diagnosis of the virus is critical in controlling its spread and reducing the burden on healthcare facilities. To address this issue and relieve the strain on the healthcare system, this paper proposes a machine learning-based approach for Covid-19 diagnosis. Four algorithms were used for analyzing early Covid-19 detection, i.e., logistic regression, random forest, decision tree, and naive Bayes, using a data set of basic symptoms such as fever, shortness of breath, etc. for predicting positive and negative Covid-19 cases. Furthermore, development of a web portal that provides information on global vaccine distribution and the most widely used vaccines by country along with Covid-19 predictions. Our evaluation results demonstrate that the decision tree model outperformed the other models, achieving an accuracy of 97.69%. This study provides a practical solution to the ongoing Covid-19 crisis through an improved diagnosis method and access to vaccination information.
Enhanced Relative Comparison of Traditional Sorting Approaches towards Optimization of New Hybrid Two-in-One (OHTO) Novel Sorting Technique Rajeshwari B S; C.B. Yogeesha; M. Vaishnavi; Yashita P. Jain; B.V. Ramyashree; Arpith Kumar
Journal of ICT Research and Applications Vol. 17 No. 2 (2023)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.17.2.2

Abstract

In the world of computer technology, sorting is an operation on a data set that involves ordering it in an increasing or decreasing fashion according to some linear relationship among the data items. With the rise in the generation of big data, the concept of big numbers has come into existence. When the number of records to be sorted is limited to thousands, traditional sorting approaches can be used; in such cases, complexities in their execution time can be ignored. However, in the case of big data, where processing times for billions or trillions of records are very long, time complexity is very significant. Therefore, an optimized sorting technique with efficient time complexity is very much required. Hence, in this paper an optimized sorting technique is proposed, named Optimized Hybrid Two-in-One Novel Sorting Technique (OHTO, a mixed approach of the Insertion Sort technique and the Bubble Sort technique. The proposed sorting technique uses the procedure of both Bubble Sort and Insertion Sort, resulting in fewer comparisons, fewer data movements, fewer data insertions, and less time complexity for any given input data set compared to existing sorting techniques.

Page 27 of 28 | Total Record : 275

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