cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
International Journal of Informatics and Communication Technology (IJ-ICT)
Published by Institute of Advanced Engineering and Science
ISSN : 22528776     EISSN : 27222616     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal of Informatics and Communication Technology (IJ-ICT) is a common platform for publishing quality research paper as well as other intellectual outputs. This Journal is published by Institute of Advanced Engineering and Science (IAES) whose aims is to promote the dissemination of scientific knowledge and technology on the Information and Communication Technology areas, in front of international audience of scientific community, to encourage the progress and innovation of the technology for human life and also to be a best platform for proliferation of ideas and thought for all scientists, regardless of their locations or nationalities. The journal covers all areas of Informatics and Communication Technology (ICT) focuses on integrating hardware and software solutions for the storage, retrieval, sharing and manipulation management, analysis, visualization, interpretation and it applications for human services programs and practices, publishing refereed original research articles and technical notes. It is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in ICT.
Arjuna Subject : -
Articles 462 Documents
 30   31   32   33   34 
Predicting rainfall runoff in Southern Nigeria using a fused hybrid deep learning ensemble Ojugo, Arnold Adimabua; Ejeh, Patrick Ogholuwarami; Odiakaose, Christopher Chukwufunaya; Eboka, Andrew Okonji; Emordi, Frances Uchechukwu
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i1.pp108-115

Abstract

Rainfall as an environmental feat can change fast and yield significant influence in downstream hydrology known as runoff with a variety of implications such as erosion, water quality, and infrastructures. These, in turn impact the quality of life, sewage systems, agriculture, and tourism of a nation to mention a few. It chaotic, complex, and dynamic nature has necessitated studies in the quest for future direction of such runoff via prediction models. With little successes in use of knowledge driven models, many studies have now turned to data-driven models. Dataset is retrieved from Metrological Center in Lagos, Nigeria for the period 1999-2019 for the Benin-Owena River Basin. Data is split: 70% for train and 30% for test. Our study adapts a spatial-temporal profile hidden Markov trained deep neural network. Result yields a sensitivity of 0.9, specificity 0.19, accuracy of 0.74, and improvement rate of classification of 0.12. Other ensembles underperformed when compared to proposed model. The study reveals annual rainfall is an effect of variation cycle. Models will help simulate future floods and provide lead time warnings in flood management.
Autism detection based on autism spectrum quotient using weighted average ensemble method Lawysen, Lawysen; Anggara, Nelsen; Girsang, Abba Suganda
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp188-196

Abstract

Autism spectrum disorder (ASD) is a condition that occurs in an individual, wherein it is accompanied by various symptoms such as difficulties in socializing with others. Early detection of ASD patients can assist in preventing various symptoms caused by ASD. The focus of this research is to automate the diagnosis of ASD in an individual based on the results of the autism spectrum quotient (AQ) using weighted average ensemble method. Initially, preprocessing is carried out on the dataset to ensure optimal performance of the resulting model. In the preprocessing step, the filling of missing values and feature selection occurs, where the feature selection method being utilized is p-value. The model in this research uses the weighted average ensemble method, which is the model that combines three machine learning classification algorithms. Eight classification algorithms are tested to identify the three algorithms with the best performance, namely gaussian Naïve Bayes (NB), logistic regression (LR), and random forest (RF). Following the testing, the model constructed using the weighted average ensemble method exhibits the highest performance compared to the model built using a single classification algorithm. The performance matrix used to measure the model’s performance is area under the curve (AUC)/receiver operating characteristic (ROC), with the developed model achieving an AUC/ROC value of 0.912.
Medical X-ray images enhancement based on super resolution convolution neural network Rani, Sharda; Kaur, Navdeep
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp257-263

Abstract

Pneumonia is a severe lung infection, chest X-ray (CXR) image preferred to find infection. Real images lost its quality, resolution and other feature due to transmission. So good qualitative datasets are very limited. Quality enhancement in medical images is challenging task for researchers. And quality in clinical diagnosis of any disease in deep learning play a very important role. So, this paper presents an aspect with importance of quality in medical images CXR of a particular dataset and how to enhance and create new images with high quality resolution, that is re-used for classification in deep learning. Super resolution convolutional neural netwok (SRCNN) is deep learning based method, which is used for improving resolution in image. Super resolution means low resolution (LR) images from dataset is to be reconstructed or magnified into high resolution (HR). The objective behind this study is to measure the effect of super resolution with quality index, peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index measure (SSIM). This experinment performed on 200 images with 10 batches, each batch has 20 images from Kermany dataset, select LR images and converted into HR with SRCNN. Then we find PSNR value of image is increase upto 2 to 5 DB, and MSE of qood quality images is near to zero and MSE decrease up to 20-25, SSIM value have little variation due to same pattern is found in input and output images. Enhancement means highlight or improve the region of interest of pneumonic images. Main goal of this study is to preapare a modified dataset which is further used for classification.
Influence live streaming TikTok to purchase intention of skincare products in Indonesia Rajagukguk, Nova Winda; Suwarno, Wulan; Anggraeni, Adilla
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i1.pp80-90

Abstract

TikTok Live Streaming accommodates the needs of sellers to be able to communicate two-way between sellers and buyers. A new type of online business called live streaming allows users to watch and make purchases. The host is the person who sells the goods during the live-streaming event, and the live streaming platform is the location where the live-streaming takes place. The purpose of this study is to further understand the factors that determine customer purchase intention through tiktok live streaming using the IAM model. In previous research, several variables in the IAM model have a positive correlation with purchase intention. This study aims to see the impact of adding one variable, namely perceived persuasiveness within the framework of the IAM model on purchase intention. This research aims to see the impact of adding one variable, perceived persuasiveness in IAM model framework on purchase intention. This study using the quantitative method was employed using partial least square structural equation modeling (PLSSEM). The SmartPLS 4.0 software was applied to examine the proposed model.
IoT based MPPT techniques for photovoltaic frameworks management under different environmental conditions: a review Khan, Mohammad Junaid; Akhtar, Md. Naqui; Alam, Afroj; Afthanorhan, Asyraf
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp306-313

Abstract

Solar energy (SE) is the most attractive form of renewable energy (RE) source for electrification. To harness SE, the photovoltaic (PV) system is required towards converting sunlight into direct electricity. The PV frameworks can be placed in areas with high energy potential. The performance of PV frameworks is complex work which depends on various parameters of the frameworks and their operations. The performance of PV frameworks can be evaluated using MATLAB/Simulink platform and real-time implementation. In this research article, the internet of things (IoT) is investigated to regulate and monitor PV system performance in various environments. IoT-based maximum power point tracking (MPPT) technology improves the response of real-time operating characteristics which makes it possible to control remote PV systems management, quickly diagnose problems and maintain them effectively. Additionally, it allows for recording production and performance data for analysis.
Efficient traffic signal detection with tiny YOLOv4: enhancing road safety through computer vision Santhiya, Santhiya; Johnraja Jebadurai, Immanuel; Leelipushpam Paulraj, Getzi Jeba; Veemaraj, Ebenezer; Sharance, Randlin Paul; Keren, Rubee; Karan, Kiruba
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp285-296

Abstract

As decades go by, technology advances and everything around us becomes smarter, such as televisions, mobile phones, robots, and so on. Artificial intelligence (AI) is applied in these technologies where AI assists the computer in making judgments like humans, and this intelligence is artificially fed to the model. The self-driving technique is a developing technology. Autonomous driving has been a broad and fast-expanding technology over the last decade. This model is carried out using the tiny you only look once (YOLO) algorithm. YOLO is mainly used for object detection classification. Tiny YOLO model is explored for the traffic signal detection. ROBI FLOW dataset is used for object detection which contains 2000+ image data to train the tiny YOLO model for traffic signal detection in real time. This model gives an improved accuracy and lightweight implementation compared to other models. Tiny YOLO is fast and accurate model for real-time traffic signal detection.
Building detection based on searching of the optimal kernel shapes pruning method on Res2-Unet Arul Reji, Arulappan Amala; Muruganantham, Sathiyamoorthy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp131-142

Abstract

In recent years, advances in remote sensing technology have made it feasible to use satellite data for large-scale building detection. Moreover, the building detection from multispectral satellite photography data is necessary; however, it is difficult to recovery the accurate building footprint from the high-resolution pictures. Because the deep learning networks contains high computational cost and over-parameterized. Therefore, network pruning has been used to reduce the storage and computations of convolutional neural network (CNN) models. In this article, we proposed the pruning technique to prune the CNN network from Res2-Unet model for accurately detecting the buildings. Initially, the CNN network is pruned by utilizing the searching of the optimal kernel shapes technique. It is employed to carry out stripe-wise pruning and automatically find the ideal kernel shapes. Then the data quantification is applied to enhance the proposed model and also reduce the complexity. Finally, the enhanced Res2-Unet model is used for the building detection. Moreover, WHU East Asia Satellite and the Massachusetts building dataset are the two available datasets used to access the suggested framework. Compare to the existing models, the proposed model gives better performance.
Navigating the cyber forensics landscape a review of recent innovations Panigrahi, Gyana Ranjana; Barpanda, Nalini Kanta; Sethy, Prabira Kumar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i1.pp27-33

Abstract

The extensive relevance of digital forensics in today's data-driven environment has been emphasized in this article. The free software and the commercial software community are debatable, despite users and developers often differing views on important topics like software safety and usability. This article primarily uses pre-defined criteria and a platform-oriented approach to examine promising freeware (Magnet Forensics and Sleuth Kit) vs. profitable (ProDiscover and Oxygen Forensic Suite) mobile forensics tools. Under diverse settings, the tools' capacity to develop and analyze forensically sound digital forensic media sources is validated. After erasing data, each media type was tested again after formatting. The study concludes with a comparison matrix that may aid in determining the best-fit option for the investigation's requirements among the tools. The findings indicate the potential for freeware to supplant numerous proprietary applications, as users can opt for freeware instead of incurring costs associated with proprietary software. Furthermore, this perception can be put into practice.
A comprehensive analysis of dynamic PAPR reduction schemes in MIMO-OFDM systems Dubala, Ramadevi; Rao, P. Trinatha
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp248-256

Abstract

In this paper, an attempt develops three different methods, namely, Hybrid Maximal-Minimum (Max-Min) model with Decomposed Selective Mapping (D-SLM) in a UFMC, Modified Enhancement Asymmetric Arithmetic Coding Scheme (M-EAAC) and Dynamic Threshold-based Logarithmic Companding (DTLC) is carried out in Multiple-Input, Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) technology to enhance the PAPR reduction. These methods allow increased data rate request through threshold limit adjustment in a desired out-of-band (OOB) range, allows data transmission for the selected for the candidate sequences for maximizing the channel utility, data capacity and computational demands and varying threshold limit to analyse the nonlinear companding effect, respectively on D-UFMC-SLM, M-EAAC SCS-TI and DTLC. The extensive analysis shows that the proposed M-EAAC SCS-TI achieves a reduced CCDF PAPR, increased average spectral efficiency and redued Bit Error Rate (BER) than the other proposed DTLC and D-UFMC-SLM methods.
Folk art classification using support vector machine Bhatt, Malay; Mehta, Apurva
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp152-160

Abstract

Tremendous amounts of effort have been carried out every year by the governments of all the countries to preserve art and culture. Art in the form of paintings, artifacts, music, dance, and cuisines of every country has the utmost importance. The study of Tribal arts provides deep insight into our history and acts as a milestone in the roadmap of our future. This paper focuses on three popular folk arts namely: Gond, Manjusha, and Warli. 300 images of each artwork have been collected from various online repositories. To generate a robust system, data augmentation is applied which results in 7510 images. A feature vector based on a generalized co-occurrence matrix, local binary pattern, HSV histogram, and canny edge detector is constructed and classification is performed using a linear support vector machine. 10- fold cross-validation produces 99.8% accuracy.

Page 32 of 47 | Total Record : 462

 30   31   32   33   34 

Filter by Year

2012 2025


Reset
Filter By Issues
All Issue Vol 14, No 3: December 2025 Vol 14, No 2: August 2025 Vol 14, No 1: April 2025 Vol 13, No 3: December 2024 Vol 13, No 2: August 2024 Vol 13, No 1: April 2024 Vol 12, No 3: December 2023 Vol 12, No 2: August 2023 Vol 12, No 1: April 2023 Vol 11, No 3: December 2022 Vol 11, No 2: August 2022 Vol 11, No 1: April 2022 Vol 10, No 3: December 2021 Vol 10, No 2: August 2021 Vol 10, No 1: April 2021 Vol 9, No 3: December 2020 Vol 9, No 2: August 2020 Vol 9, No 1: April 2020 Vol 8, No 3: December 2019 Vol 8, No 2: August 2019 Vol 8, No 1: April 2019 Vol 7, No 3: December 2018 Vol 7, No 2: August 2018 Vol 7, No 1: April 2018 Vol 6, No 3: December 2017 Vol 6, No 2: August 2017 Vol 6, No 1: April 2017 Vol 5, No 3: December 2016 Vol 5, No 2: August 2016 Vol 5, No 1: April 2016 Vol 4, No 3: December 2015 Vol 4, No 2: August 2015 Vol 4, No 1: April 2015 Vol 3, No 3: December 2014 Vol 3, No 2: August 2014 Vol 3, No 1: April 2014 Vol 2, No 3: December 2013 Vol 2, No 2: August 2013 Vol 2, No 1: April 2013 Vol 1, No 2: December 2012 Vol 1, No 1: June 2012 More Issue