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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
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Alzheimer’s disease diagnosis using convolutional neural networks model Samanvi, Potnuru; Agrawal, Shruti; Mallick, Soubhagya Ranjan; Lenka, Rakesh Kumar; Palei, Shantilata; Mishra, Debani Prasad; Salkuti, Surender Reddy
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.pp206-213

Abstract

The global healthcare system and related fields are experiencing extensive transformations, taking inspiration from past trends to plan for a technologically advanced society. Neurodegenerative diseases are among the illnesses that are hardest to treat. Alzheimer’s disease is one of these conditions and is one of the leading causes of dementia. Due to the lack of permanent treatment and the complexity of managing symptoms as the severity grows, it is crucial to catch Alzheimer’s disease early. The objective of this study was to develop a convolutional neural network (CNN)-based model to diagnose early-stage Alzheimer’s disease more accurately and with less data loss than methods previously discovered. CNN, is adept at processing and recognising images and has been employed in various diagnostic tools and research in the healthcare sector, showing limitless potential. Convolutional, pooling and fully linked layers are the common layers that make up a CNN. In this paper, five CNN modelswere randomly chosen (ResNet, DenseNet, MobileNet, Inception, and Xception) and were trained. ResNet performed the best and was chosen to undergo additional modifications to improve accuracy to 95.5%. This was a remarkable achievement that made us hopeful for the performance of this model in larger datasets as well as other disease detection.
Improved inception-V3 model for apple leaf disease classification Sirait, Dheo Ronaldo; Sutikno, Sutikno; Sasongko, Priyo Sidik
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.pp161-167

Abstract

Apple, a nutrient-rich fruit belonging to the genus Malus, is recognized for its fiber, vitamins, and antioxidants, giving health benefits such as improved digestion and reduced cardiovascular disease risk. In Indonesia, the soil and climate create favorable conditions for apple cultivation. However, it is essential to prioritize the health of the plant. Biotic factors, such as fungal infections like apple scabs and pests, alongside abiotic factors like temperature and soil moisture, impact the health of apple plants. Computer vision, specifically convolution neural network (CNN) inception-V3, proves effective in aiding farmers in identifying these diseases. The output layer in inception-V3 is essential, generating predictions based on input data. For this reason, in this paper, we add an output layer in inception-V3 architecture to increase the accuracy of apple leaf disease classification. The added output layers are dense, dropout, and batch normalization. Adding a dense layer after flattening typically consolidates the extracted features into a more compact representation. Dropout can help prevent overfitting by randomly deactivating some units during training. Batch normalization helps normalize activations across batches, speeding up training and providing stability to the model. Test results show that the proposed method produced an accuracy of 99.27% and can increase accuracy by 1.85% compared to inception-V3. These enhancements showcase the potential of leveraging computer vision for precise disease diagnosis in apple crops.
Traffic accident classification using IndoBERT Naufal, Muhammad Alwan; Girsang, Abba Suganda
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.pp42-49

Abstract

Traffic accidents are a widespread concern globally, causing loss of life, injuries, and economic burdens. Efficiently classifying accident types is crucial for effective accident management and prevention. This study proposes a practical approach for traffic accident classification using IndoBERT, a language model specifically trained for Indonesian. The classification task involves sorting accidents into four classes: car accidents, motorcycle accidents, bus accidents, and others. The proposed model achieves a 94% accuracy in categorizing these accidents. To assess its performance, we compared IndoBERT with traditional methods, random forest (RF) and support vector machine (SVM), which achieved accuracy scores of 85% and 87%, respectively. The IndoBERT-based model demonstrates its effectiveness in handling the complexities of the Indonesian language, providing a useful tool for traffic accident classification and contributing to improved accident management and prevention strategies.
Mobile forensics tools and techniques for digital crime investigation: a comprehensive review Sutikno, Tole
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.pp321-332

Abstract

Extracting and analyzing data from smartphones, IoT devices, and drones is crucial for conducting digital crime investigations. Effective cyberattack mitigation necessitates the use of advanced Android mobile forensics techniques. The investigation necessitates proficiency in manual, logical, hex dump, chip-off, and microread methodologies. This paper provides a comprehensive overview of Android mobile forensics tools and techniques for digital crime investigation, as well as their use in gathering and analyzing evidence. Forensic software tools like Cellebrite UFED, Oxygen Forensic Detective, XRY by MSAB, Magnet AXIOM, SPF Pro by SalvationDATA, MOBILedit Forensic Express, and EnCase Forensic employ both physical and logical techniques to retrieve data from mobile devices. These advanced tools offer a structured approach to tackling digital crimes effectively. We compare dependability, speed, compatibility, data recovery accuracy, and reporting. Mobile-network forensics ensures data acquisition, decryption, and analysis success. Conclusions show that Android mobile forensics tools for digital crime investigations are diverse and have different capabilities. Mobile forensics software offers complete solutions, but new data storage and encryption methods require constant development. The continuous evolution of forensic software tools and a comprehensive tool classification system could further enhance digital crime investigation capabilities.
BloFoPASS: A blockchain food palliatives tracer support system for resolving welfare distribution crisis in Nigeria Aghware, Fidelis Obukohwo; Adigwe, Wilfred; Okpor, Margareth Dumebi; Odiakaose, Christopher Chukwufunaya; Ojugo, Arnold Adimabua; Eboka, Andrew Okonji; Ejeh, Patrick Ogholorunwalomi; Taylor, Onate Egerton; Ako, Rita Erhovwo; Geteloma, Victor Ochuko
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.pp178-187

Abstract

With population rising to approximately 200 million Nigerians – fast-paced, urbanization has continued to advent food insecurity with maladministration, corruption, internal rife, and starvation. These, threatened the nation's unity with the lockdown of 2020; and consequently, have now become the trend. Nigeria must as a nation, re-examine her methods in the administration of palliatives (in lieu of food and relief) distribution – as the above-listed issues have become of critical need in the equitable distribution of reliefs, both from the humanitarian agency view, and the Government (State and Federal). They have noticed non-transparency, corruption, and data inadequacies, as major drawbacks in its management. Our study presents a blockchain ensemble for the administration of food palliatives distribution in Nigeria that first ensures, that all beneficiaries be registered, and the food palliatives are sensor-tagged and recorded on the blockchain. Results show the number of transactions per second and page retrieval abilities for the proposed chain were quite low with 30-TPS and 0.38seconds respectively – as compared to public blockchain. Proposed ensemble eliminates fraud that is herein rippled across the existing system, minimizes corrupt practices via sensor-based model, provides insight for stakeholders, and minimize the error in reported data on the supply chain.
Quasi linear and stone geary utility functions based-internet service financing scheme with marginal costs and monitoring costs Indrawati, Indrawati; Puspita, Fitri Maya; Yuliza, Evi; Dwipurwani, Oki; Octarina, Sisca; Helmayanti, Rizky
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.pp143-151

Abstract

The use of computer network technology is currently increasing, especially on the internet network. To connect to the actual internet, it is a task for internet service provider (ISP). Providing advantages to ISPs, it requires a financing scheme. This study's goal is to present a modified model for internet service financing schemes, within the customer choices and consumer satisfaction levels to maintain the schemes. To achieve the best outcomes, this updated model is built through marginal costs and cost monitoring while taking into account service quality based on stone-geary utility functions and quasi-linear utility functions. This research provides a solution regarding the differences in increasing consumer interest with payment options on model modification that will be provided. Traffic Digilib in a local server in Palembang. According to this study, a usage-based financing strategy and a two-part pricing of IDR 2727.8 per kbps will yield the highest revenues.
A custom-built deep learning approach for text extraction from identity card images Suddul, Geerish; Seguin, Jean Fabrice Laurent
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.pp34-41

Abstract

Information found on an identity card is needed for different essential tasks and manually extracting this information is time consuming, resource exhaustive and may be prone to human error. In this study, an optical character recognition (OCR) approach using deep learning techniques is proposed to automatically extract text related information from the image of an identity card in view of developing an automated client onboarding system. The OCR problem is divided into two main parts. Firstly, a custom-built image segmentation model, based on the U-net architecture, is used to detect the location of the text to be extracted. Secondly, using the location of the identified text fields, a (CRNN) based on long short-term memory (LSTM) cells is trained to recognise the characters and build words. Experimental results, based on the national identity card of the Republic of Mauritius, demonstrate that our approach achieves higher accuracy compared to other studies. Our text detection module has an intersection over union (IOU) measure of 0.70 with a pixel accuracy of 98% for text detection and the text recognition module achieved a mean word recognition accuracy of around 97% on main fields of the identity card.
Misconceptions of metaverse: from etymology to technology Putawa, Rilliandi Arindra; Izumi, Calvina; Sugianto, Dwi; Ghaffar, Soeltan Abdul
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.pp314-320

Abstract

The emergence of the metaverse in society is followed by certain confusions, whereas the line between virtual reality and the metaverse remains unclear. Ironically, this has affected the development of the metaverse itself, focusing more on virtual reality while being one of its side components. This has led to the concept losing popularity compared to artificial intelligence technology. This research is a qualitative study that aims to explore the issues at the root of misconceptions and reconstruct the true meaning of the metaverse itself. This research indicates that the misconception already existed when the term was first used alongside virtual reality technology. The term "meta" refers to a higher reality, whereas the terms "digiverse" or "virtuverse" can be used, considering that the terms "digital" and "virtual" can refer to realities lower than the universe.
Face recognition using haar cascade classifier and FaceNet (A case study: Student attendance system) Maryuni Susanto, Bekti; Surateno, Surateno; Jullev Atmadji, Ery Setiyawan; Pramulintang, Ardian Hilmi; Apriliano, Galuh; Wulansari, Tanti; Angga Gumilang, Mukhamad
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.pp272-284

Abstract

Face recognition is increasingly widely utilised, and there are numerous face recognition systems. Face recognition is typically utilised for attendance on e-learning platforms in the field of education. The haar cascade classifier is one method for face identification; it is used to identify facial areas. Faces are classified using an alternative model, FaceNet. In this research, we purposefully designed an e-learning platform that authenticates students based on face recognition. Based on the findings of this investigation, the system can accurately recognise faces. Ten students were evaluated based on their participation in two attendance trials. Successful presence has an achievement success value of 19, and 1 failed out of a total of 20 attempts. Several variables, such as illumination, and the use of marks on hats, that could have influenced attendance caused the experiment to fail.
Quantum drones and the future of military warfare Sutikno, Tole
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v12i3.pp293-299

Abstract

The advent of drones has significantly impacted military warfare, providing improved reconnaissance, surveillance, and target acquisition (RSTA), cost savings, increased convenience, safety, and flexibility. A layered network control architecture, known as the internet of drones (IoD), coordinates drone access to controlled airspace and offers navigation services. Various systems, including wireless sensor networks (WSN) and drones directed to an expanded controlling zone, integrate with IoD to improve connection performance. This paper provides an overview of the IoD and the internet of quantum drones (IoQD), highlighting key issues and potential solutions in applications and deployment. The IoQD provides primary features such as secure message exchange, fast communication processes, the viability of creating and deploying private IoQD, and enabling a new field of application, quantum well (QW). In conclusion, the advent of drone technology has significantly improved various aspects of military operations, including reconnaissance, surveillance, and target acquisition. The IoQD offers a promising solution for military networks, facilitating the safe and expedited transfer of data, ultimately benefiting the entire military network.

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