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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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Analyzing radicalism sentiments in Indonesian da’wah content on website da’wah through text mining techniques Aziza, Aulia; Hasanah, Risqiatul; Juairiah, Juairiah; Munsyi, Munsyi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp575-585

Abstract

This study investigates the classification of radical content in Indonesian Da’wah websites using text mining techniques. A content search engine application, developed with PHP, processes queries by comparing results against a database of keywords, classifying content into four categories: red, yellow, green, and white. Manual labeling based on data from the Ministry of Communication and Informatics yielded 126 labeled articles, forming the dataset for classification. The K-nearest neighbors (K-NN) algorithm, with an optimal k value of 7, achieved a classification accuracy of 66.37%, demonstrating its reliability compared to manual methods. The “White” class showed the highest precision and recall. System testing revealed efficient performance, with 0.704 seconds per classification task and 884,656 bytes of memory usage. Future enhancements include incorporating synonym identification for Indonesian keywords and exploring machine learning algorithms such as Naive Bayes and neural networks to improve accuracy. This research highlights the potential for text mining in identifying online radical content while emphasizing the need for system adaptability.
Deep learning for grape leaf disease detection Patil, Pragati; Jadhav, Priyanka; Chaudhari, Nandini; Sureja, Nitesh; Pawar, Umesh
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp653-662

Abstract

Agriculture is crucial to India's economy. Agriculture supports almost 75% of the world's population and much of its gross domestic product (GDP). Climate and environmental changes pose a threat to agriculture. India is recognized for its grapes, a commercially important fruit. Diseases reduce grape yields by 10-30%. If not recognized and treated early, grape diseases can cost farmers a lot. The main grape diseases include downy and powdery mildew, leaf blight, esca, and black rot. This work creates an Android grape disease detection app which uses machine learning. When a farmer submits a snapshot of a diseased grape leaf, the smartphone app identifies the ailment and offers grape plant disease prevention tips. In this research, an android app that detects grape plant illnesses use convolutional neural network (CNN) and AlexNet machine learning architectures. We investigated and compared CNN and AlexNet architecture's efficacy for grape disease detection using accuracy and other metrics. The dataset used comes from Kaggle. CNN and AlexNet architectures yielded 98.04% and 99.03% accuracy. AlexNet was more accurate than CNN in the final result.
Enhancing logo security: VGG19, autoencoder, and sequential fusion for fake logo detection Mishra, Debani Prasad; Ojha, Prajna Jeet; Dash, Arul Kumar; Sethy, Sai Kanha; Behera, Sandip Ranjan; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp506-515

Abstract

This paper deals with a way of detecting fake logos through the integration of visual geometry group-19 (VGG19), an autoencoder, and a sequential model. The approach consists of applying the method to a variety of datasets that have gone through resizing and augmentation, using VGG19 for extracting features effectively and autoencoder for abstracting them in a subtle manner. The combination of these elements in a sequential model account for the improved performance levels as far as accuracy, precision, recall, and F1-score are concerned when compared to existing approaches. This article assesses the strengths and limitations of the method and its adapted comprehension of brand identity symbols. Comparative analysis of these competing approaches reveals the benefits resulting from such fusion. To sum up, this paper is not only a major contribution to the domain of counterfeit logo detection but also suggests prospects for enhancing brand security in the digital world.
Machine learning in detecting and interpreting business incubator success data and datasets Widianto, Mochammad Haldi; Prabowo, Puji
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp446-456

Abstract

This research contributes to creating a proposed architectural model by utilizing several machine learning (ML) algorithms, heatmap correlation, and ML interpretation. Several algorithms are used, such as K-nearest neighbors (KNN) to the adaptive boosting (AdaBoost) algorithm, and heatmap correlation is used to see the relationship between variables. Finally, select K-best is used in the results, showing that several proposed model ML algorithms such as AdaBoost, CatBoost, and XGBoost have accuracy, precision, and recall of 94% and an F1-score of 93%. However, the computing time the best ML is AdaBoost with 0.081s. Then, finally, the proposed model results of the interpretation of AdaBoost using select K-best are the best features “last revenue” and “first revenue” with k feature values of 0.58 and 0.196, these features influence the success of the business. The results show that the proposed model successfully utilized model classification, correlation, and interpretation. The proposed model still has weaknesses, such as the ML model being outdated and not having too many interpretation features. The future research might maximize with ML models and the latest interpretations. These improvements could be in the form of ML algorithms that are more immune to data uncertainty, and interpretation of results with wider data.
Incremental prioritization using an iterative model for smallscale systems Shaheen, Ameen; Alzyadat, Wael; Alhroob, Aysh; Asfour, A. Nasser
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp565-574

Abstract

To improve customer satisfaction during the requirement engineering process and create higher consistency in the developed software, there is a growing trend toward the development and delivery of software in an incremental manner. This paper introduces a novel approach to prioritizing the initial development of core subsystems. This prioritization ensures that the most critical subsystems, which contribute significantly to the project’s overall success, are addressed first. Our method involves employing an incremental model with iterative modeling, where each subsystem is assigned a profitability score ranging from 1 to 10. The iterative model is then utilized to identify the most suitable subsystem for the next development stage. The results of our study indicate that utilizing the total profit weight in conjunction with the iterative model effectively identifies the central subsystem of the entire project. This approach proves to be the optimal starting point for development, helping streamline the process and contribute to a more efficient software delivery strategy.
Creating a smart bedroom for children by connecting PIR and LDR sensors to a microcontroller Arduino UNO ATmega328P Mustafa, Ragmi M.; Mustafa, Kujtim R.; Ramadani, Refik
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp540-554

Abstract

Intelligent electronic systems are increasingly prevalent in modern society. The development of smart bedrooms for young children, especially those with developmental disabilities, it is based on the responses of passive infrared (PIR) and light dependent resistor (LDR) sensors. The PIR sensor detects children’s movement during the night, triggering the microcontroller to send a bit of 1 to the microcontroller pin connected to an electromagnetic relay, which then switches on a 220 VAC light to illuminate the bedroom. This only occurs if the LDR sensor has high resistance, indicating that the environment is completely dark. The functionality of this intelligent system mainly depends on the program code (sketch) uploaded to the Arduino UNO microcontroller module. The microcontroller is programmed to perform specific functions based on the sensors data. It is based on the responses of PIR and LDR sensors. The PIR sensor detects children’s movement during the night, triggering the microcontroller to send a bit of 1 to the microcontroller pin connected to an electromagnetic relay, which then switches on a 220 VAC light to illuminate the bedroom. This only occurs if the LDR sensor has high resistance, indicating that the environment is completely dark.
Performance analysis of LDPC codes in MIMO-OFDM for next generation wireless systems Kumari, P. Aruna; Pyla, Srinu; Rajendranath, U. N. V. P.; Maheswara Rao, Nirujogi Venkata
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp636-644

Abstract

Fifth Generation communication systems overcome the limitations of the fourth-generation systems and ensure improved data rates, lower latency, and higher connection density. 5G technology has the potential to unlock new internet of things (IoT) applications by utilizing the technologies such as multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), and Li-Fi. Low density parity check (LDPC) and polar codes are being preferred for data and control channels respectively in 5G systems as these coding techniques offer good error-detection and correction along with reduced latency. Morever, LDPC codes are power efficient. This paper aims to analyze the bit error rate (BER) performance of LDPC codes in MIMO-OFDM System for different modulation schemes. LDPC codes improve the BER performance of OFDM and MIMO-OFDM systems. MIMO-OFDM systems deliver better BER performance over OFDM system.
Smart hybrid power management system in electric vehicle for efficient resource utilization with ANN Ponnusamy, Vinoth Kumar; Devarajan, Gunapriya; Easwaram, Gomathi; Murugesan, Geetha; Sekar, Rathinam Marimuthu; Howsalya Devi, R. Delshi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp488-496

Abstract

The novel hybrid power system integrating energy storage, electric vehicle (EV) charging infrastructure and renewable energy sources improve sustainability and resilience. This work proposes a power management system controlled by artificial intelligence for EV charging infrastructure. The battery’s state of charge (SoC) is continuously monitored by artificial neural network (ANN) algorithm improves the health of the battery and efficient operation of the system. The buck boost DC-DC converter performs dynamic switching mechanism, which adjusts to changing solar conditions and energy demands, guarantees a steady power supply. Depending on the situation, the ANN algorithm used in the battery’s SoC control mechanism decides whether to priorities the EV charging or the inverter to supply the grid. The work is evaluated with the MATLAB simulation for different conditions and results are compared with different controllers such as PI, PID, and ANN. The experiment performed uses internet of things (IoT) for transferring the data from the EV motor, acts as an input for the controller to perform the novel hybrid power management operation with cloud technology. The experimental prototype evaluates the results connected to the photovoltaic (PV) system and battery management system (BMS) which lowers reliance on non-renewable resources, increases overall energy efficiency, and ensures a steady supply of power under a various condition.
Efficient design of approximate carry-based sum calculating full adders for error-tolerant applications Shiva Kumar, Badiganchela; Reddy, Galiveeti Umamaheswara
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1189-1198

Abstract

Approximate computing is an innovative circuit design approach which can be applied in error-tolerant applications. This strategy introduces errors in computation to reduce an area and delay. The major power-consuming elements of full adder are XOR, AND, and OR operations. The sum computation in a conventional full adder is modified to produce an approximate sum which is calculated based on carry term. The major advantage of a proposed adder is the approximation error does not propagate to the next stages due to the error only in the sum term. The proposed adder was coded in verilog HDL and verified for different bit sizes. Results show that the proposed adder reduces hardware complexity with delay requirements.
Performance analysis of D2D network in heterogeneous multitier interference scenarios Santhakumar, Dhilipkumar; Chellaperumal, Arunachalaperumal; Lazer Jessie, Jenifer Suriya; Arulpragasam, Jerlin
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp811-821

Abstract

The trade-off between boosting network throughput and minimizing interference is a critical issue in fifth generation (5G) networks. Diverting the data traffic around the network access point in device-to-device (D2D) communication is an important step in realizing the vision of 5G. Though the D2D network improves the network performance, they complicate the interference management process. Interference is an invisible physical phenomenon occurring in wireless communication which happens when multiple transmissions happen simultaneously over a general wireless medium. Enormous growth in usage of mobile phone and other wireless gadgets in recent years has paved the way for significant role in Interference analysis over multi-tier network. Interference could affect communication systems performance and it might even prevent systems functioning properly. 3G and 4G wireless devices coexisted with reverse compatibility in a coverage area. Also, after their widespread adoption, 5G devices have become prevalent across the globe and this reaffirms interference coexistence as a significant field of research. Multiple systems operating in a region will cause severe interference and ultimately reduce the quality of received signal. A simulation environment for cellular standards coexistence considering real-time parameters is created and experimented. Various research works earlier addresses the interference mitigation techniques in multi-tier networks but none of them present the analysis of scenarios and interfering signal power levels in the respective contexts. In this paper various scenarios with different network interference coexistence were studied, simulated, and analyzed quantitatively.

Page 42 of 47 | Total Record : 462

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