International Journal of Informatics and Communication Technology (IJ-ICT)
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.
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<![CDATA[A cluster and association analysis visualization using Moodle activity log data ]]>
<![CDATA[Andri Reimondo Tamba]]>;
<![CDATA[Krista Lumbantoruan]]>;
<![CDATA[Aulia Pakpahan]]>;
<![CDATA[Samuel Situmeang]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v12i2.pp150-161
<![CDATA[The course activity log is where a learning management system (LMS) like Moodle keeps track of the various learning activities. In order to conduct a quicker and more in-depth examination of the students' behaviors, the instructor may either directly examine the log or make use of more complex methodologies such as data mining. The majority of the proposed methods for analyzing this log data center mostly on predictive analysis. In this research, cluster analysis and association analysis, two separate data mining functions, are investigated in order to analyze the log. The students' activities are used in the cluster analysis performed with K-Means++, and the association analysis performed with Apriori is used to investigate the connections between the students' various activities. A dashboard presentation of the findings is provided in order to facilitate clearer comprehension. Based on the findings of the analysis, it can be concluded that the structure of the student cluster is medium, whereas the association between the activities undertaken by students is positively correlated and well-balanced. The subjective review of the dashboard reveals that the visualization is already sufficient, but there are some recommendations for making it even better.]]>
<![CDATA[Comparison of three common software-defined network controllers ]]>
<![CDATA[Rikie Kartadie]]>;
<![CDATA[Edy Prayitno]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v12i2.pp85-91
<![CDATA[The software-defined network (SDN) controller adds and removes the contents of the flow table through secure channels to determine how packets are processed and how the flow table is managed. The controller pays attention to network intelligence and becomes the middle part, where the network manages the transfer data of the aircraft delivered via the OpenFlow (OF) switch. To this end, the controller provides an interface for managing, controlling, and managing this switch flow table. Run tests to calculate controller throughput and latency levels and test using the cbance tool, which can test transmission control protocol (TCP) and user datagram protocol (UDP) protocols. The tests are run by forcing the controller to run at maximum without any additional settings (default settings) in order to use the correct information about the controller’s capabilities. Because of this need, you need to test the performance of your controller. In this study, the tests were run on three popular controllers. Test results show that flowed controllers are more stable than open network operating dystem (ONOS) and open daylight (ODL) controllers in managing switch and host loads.]]>
<![CDATA[Novel DV-hop algorithm-based machines learning technics for node localization in rang-free wireless sensor networks ]]>
<![CDATA[Oumaima Liouane]]>;
<![CDATA[Smain Femmam]]>;
<![CDATA[Toufik Bakir]]>;
<![CDATA[Abdessalem Ben Abdelali]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v12i2.pp140-149
<![CDATA[Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an Extreme Learning Machine (ELM) technique for minimizing localization error in range-free WSN. In this paper, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to increase localization accuracy in Range-Free WSNs. We tested the proposed approaches in a variety of multi-hop WSN scenarios. Our research focused on an isotropic and irregular environment. The simulation results show that the proposed Cascade-ELM algorithm considerably improves localization accuracy when compared to previous algorithms derived from smart computing approaches. When compared to previous work, isotropic environments show improved localization results.]]>
<![CDATA[Evaluating the level of inteference in UMTS/LTE heterogeneous network system ]]>
<![CDATA[Nnebe Scholastica Ukamaka]]>;
<![CDATA[Odeh Isaac Ochim]]>;
<![CDATA[Okafor Chinenye Sunday]]>;
<![CDATA[Ugbe Oluchi Christiana]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v12i2.pp92-102
<![CDATA[The study evaluated interference in a dense heterogeneous network using third-generation universal mobile telecommunication systems (UMTS) and fourth-generation long term evolution (LTE) networks LTE. The UMTs/LTE heterogeneous network determines the level of interference when the two communication systems coexist and how to improve the network by migrating from UMTs to LTE, which has a faster download speed and larger capacity. Techno lite 8 on third generation (3G) and Infinix Pro 6 on fourth generation (4G) were used to measure network the received signal strength (RSS) during site investigation. UE interference was detected and traced using a spectrum analyzer. UMTS and LTE path loss exponents are 2.6 and 3.2. Shannon's capacity theorem calculated LTE and UMTS capacity. When signal to interference and noise ratio (SINR) was used as a quality of service (QoS) indicator, MATLAB channel capacity plots did not match Shannon's due to neighboring interference. UMTS had an R2 of 0.54 and LTE 0.57 for the Shannon channel capacity equation. Adjacent channel interference (ACI) user devices reduce network capacity, lowering QoS for other customers.]]>
<![CDATA[Text-based emotion recognition in online social networks using adaboost classification method ]]>
<![CDATA[Paria Soheilifar]]>;
<![CDATA[Samad Nejatian]]>;
<![CDATA[Karmollah Bagherifard]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v12i2.pp%p
<![CDATA[Data mining and natural language processing are used in Emotion Mining to retrieve and extract knowledge from text. Data is always changing as a result of upgrades and the inclusion of new information at any given time. The feature set can be reduced to a feature selection method, which can be reduced to a subset with considerably smaller volume and higher detection capabilities, using the new feature combination. As you may be aware, synonymous words are treated differently in text classification. The composition method's primary assumption is that combining synonyms results in better characteristics. Given the complexity of the search problem, the use of meta-meta-methodologies can be beneficial in identifying better combinations of features and, as a result, enhancing classification efficiency. The multipurpose method of learning and optimization (TLBO) algorithm will be employed for this project because to its simplicity and quickness in identifying individuals' perspectives. In the field of cognitive science, the proposed strategy has a considerable effect on data reduction and, as a result, classification efficiency. In this study, we used Adaboos, a hybrid classification approach, to classify comments. Adequacy should improve classification accuracy by an average of 8 and 11 points over regression and SVM, respectively.]]>
<![CDATA[Designing a Book Recommender Engine: A New Perspective with Deep Learning Techniques ]]>
<![CDATA[Radha Guha]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v12i2.pp%p
<![CDATA[From the last decade, deep learning technology is showing amazing performance improvement in the field of computer vision and natural language processing (NLP). NLP’s big leap forward recently has enabled computers to understand ambiguous human languages decently. In this paper benefit of deep learning techniques in Book Recommender system design is explored and validated. As every book is huge in content, content-based filtering used for recommendation system design can benefit from NLP’s breakthrough word embedding technique which captures word context, semantics, and word dependency better and helps in dimensionality reduction as well. Subsequent advancement in language model with attention-based transformer architecture deciphers word and sentence meaning better considering a larger context. Content based filtering computes nearest neighbor recommendation and this technique will benefit as cosine similarity of one book to another can be computed more efficiently now. A second method used for recommender design is collaborative filtering which analyzes users’ past item preference, and user to user and item to item similarity computation. Deep learning techniques captures non-trivial, non-linear, user-item interaction better than traditional matrix factorization algorithms. Deep learning trains its model with huge amount of data in its parallel processing architecture. Multi-core CPU, GPU and TPU will support deep learning’s parallel processing architecture to handle bigdata to capture complex user-item interaction hierarchy. The contribution of this paper is to explain recommendation system design aspects, deep learning technology and comparison of deep learning with traditional machine learning techniques by solving a book recommendation system design.]]>
<![CDATA[Design of a 175 GHz SiGe-based voltage-controlled oscillator with greater than 7.6 dBm power ]]>
<![CDATA[Oluseun Damilola Oyeleke]]>;
<![CDATA[Aliyu Danjuma Usman]]>;
<![CDATA[Kabir Ahmad Abubilal]]>;
<![CDATA[Habeeb Bello]]>;
<![CDATA[Olabode Idowu-Bismark]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v12i2.pp103-114
<![CDATA[In this research, we present a low phase noise (PN) and wide tuning range 175 GHz inductors and capacitors (LC) voltage-controlled oscillator (VCO) based on a differential Colpitts oscillator that was designed using a 0.13 μm bipolar complementary metal oxide semiconductor (BiCMOS) and simulated. The square of the tank Q-factor and the square of the oscillation amplitude were both maximized to reduce PN. With an extensive examination of parasitic, mathematical analysis of load impedances, and implementation of differential design, the PN was reduced, and the output power was enhanced. Using a supply voltage of 1.6 V, the VCO consumes 41.9 mA, resulting in a total power usage of 67 mW to prevent undesirable PN deterioration, an inter-stage LC filter at the VCO-buffer interface increases the swing at the buffer input. To make a better output, a buffer is used to isolate the load from the VCO core. In addition, the VCO has a high linearity and the overall, the VCO presented in this study demonstrates excellent performance and has the potential to be used in a wide range of applications that require a high-performance, low-power VCO.]]>
<![CDATA[E-government implementation's impact on Saudi vision 2030 to become an international logistic center ]]>
<![CDATA[Razan Alhujaili]]>;
<![CDATA[Malak Albaqami]]>;
<![CDATA[Omar Aboulola]]>;
<![CDATA[Mashael Khayyat]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v12i2.pp180-187
<![CDATA[This study examined the influence of electronic government (e-government) implementation for the Ministry of Transport on fulfilling Saudi vision 2030 by transforming the Kingdom of Saudi Arabia (KSA) into a logistics center linking three continents. Saudi vision 2030 aims to cut transportation costs by improving infrastructure, shorten importing and exporting times by streamlining and automating operations, and increase supply chain transparency through sector reform. Implementing e-government would improve government services and engagement through information and communication technology (ICT). This article focuses on four primary areas: i) making KSA a logistics center; ii) increasing the chance of living throughout the Kingdom; and iii) promoting long-term financial sustainability. The study is founded on the idea that logistics is a crucial component for competitive advantage and transportation (by land, sea, or air) is a logistical sub-process for Saudi enterprises that benefit from transport networks similar to the best in the world. The Kingdom's strategic location at the junction of three continents gives its transport sector a geographical competitive advantage that provides access to important emerging markets and critical sea lanes. Despite the optimistic future of the transport and logistics industries in KSA, some important hurdles must be overcome.]]>
<![CDATA[Smart door access control system based on QR code ]]>
<![CDATA[Agrim Jain]]>;
<![CDATA[Abhinav Panwar]]>;
<![CDATA[Mohd. Azam]]>;
<![CDATA[Ruqaiya Khanam]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v12i2.pp171-179
<![CDATA[Wirelessly based security applications have exploded as a result of modern technology. To build and/or implement security access control systems, many types of wireless communication technologies have been deployed. quick response (QR code) is a contactless technology that is extensively utilised in a variety of sectors, including access control, library book tracking, supply chains, and tollgate systems, among others. This paper combines QR code technology with Arduino and Python to construct an automated QR code-based access management system. After detecting a QR code, the QR scanner at the entry collects and compares the user's unique identifier (UID) with the UID recorded in the system. The results show that this system is capable of granting or denying access to a protected environment in a timely, effective, and reliable way. Security systems can protect physical and intellectual property by preventing unauthorized persons from entering the area. Many door locks, such as mechanical and electrical locks, were created to meet basic security needs but it also helps to create a data files structure of the authorized persons.]]>
<![CDATA[Machine learning techniques for plant disease detection: an evaluation with a customized dataset ]]>
<![CDATA[Amatullah Fatwimah Humairaa Mahomodally]]>;
<![CDATA[Geerish Suddul]]>;
<![CDATA[Sandhya Armoogum]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v12i2.pp127-139
<![CDATA[Diseases in edible and industrial plants remains a major concern, affecting producers and consumers. The problem is further exacerbated as there are different species of plants with a wide variety of diseases that reduce the effectiveness of certain pesticides while increasing our risk of illness. A timely, accurate and automated detection of diseases can be beneficial. Our work focuses on evaluating deep learning (DL) approaches using transfer learning to automatically detect diseases in plants. To enhance the capabilities of our approach, we compiled a novel image dataset containing 87,570 records encompassing 32 different plants and 74 types of diseases. The dataset consists of leaf images from both laboratory setups and cultivation fields, making it more representative. To the best of our knowledge, no such datasets have been used for DL models. Four pre[1]trained computer vision models, namely VGG-16, VGG-19, ResNet-50, and ResNet-101 were evaluated on our dataset. Our experiments demonstrate that both VGG-16 and VGG-19 models proved more efficient, yielding an accuracy of approximately 86% and a f1-score of 87%, as compared to ResNet-50 and ResNet-101. ResNet-50 attains an accuracy and a f1-score of 46.9% and 45.6%, respectively, while ResNet-101 reaches an accuracy of 40.7% and a f1-score of 26.9%.]]>
