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International Journal of Information Engineering and Science
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30481902     EISSN : 30481953     DOI : 10.62951
Core Subject : Engineering,
Engineering
The scope of the this Journal covers the fields of Information Engineering and Science. This journal is a means of publication and a place to share research and development work in the field of technology
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 37 Documents
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Literature Review on Histogram-Based Image Forensics for Recaptured Image Detection Nathanael David Christian Barus; Nayem Kibriya; Natasha Fedora Barus
International Journal of Information Engineering and Science Vol. 1 No. 3 (2024): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i3.35

Abstract

This qualitative literature review explores the realm of histogram-based image forensics for recaptured image detection, addressing the challenges posed by advancements in display technology and the subsequent need for robust forensic techniques. The research methodology involves a systematic approach, including defined research objectives, thorough literature search, data extraction, thematic analysis, and ethical considerations. The focal point is the proposed method utilizing Local Ternary Count (LTC) histograms normalized from residue maps, demonstrating exceptional performance across various databases. The methodology involves residue map calculation, LTC histogram extraction, and experiments showcasing the method's efficiency in both single and mixed databases. The discussion emphasizes emerging frontiers in recaptured image forensics, presenting innovative algorithms categorized by the medium used during the recapture process. The shift towards deep learning methods is noted, with a focus on a proposed algorithm for detecting images recaptured from LCD screens based on quality-aware features and histogram features. The RID field has witnessed advancements, with a detailed overview of methods categorically addressing recapture from LCD screens. Ethical considerations are integrated into the discussion, and the conclusion emphasizes the need for constant adaptation, innovation, and collaboration in the fight against evolving manipulation techniques. Looking ahead, the fusion of features, standardized datasets, and advanced deep learning architectures are identified as key elements for future research in ensuring image authenticity
Optimizing the Transmission of Church Information Through the Design Thinking-Based Church News Application Gunawan Prayitno; Jenny Tandi
International Journal of Information Engineering and Science Vol. 1 No. 3 (2024): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i3.44

Abstract

The church plays a crucial role in spreading Christian values and nurturing the growth of faith within its community. However, in the context of GPSI EFATA, there are several challenges in effectively conveying church information. These challenges include the unappealing presentation of physical congregation news, the accumulation of paper, and limited distribution of information. In order to address these issues, this research project utilizes the Design Thinking method to develop a smartphone-based application for congregational news. The Design Thinking method consists of five stages: empathy, definition, ideation, prototype, and testing. During the empathy stage, the needs of the congregation are identified, particularly the need for more accessible and engaging information. The ideation stage then generates a solution in the form of a smartphone application. This application includes various features such as notifications, prayer schedules, financial reports, photo galleries, and management profiles. A prototype of the application was developed to meet these needs and was subsequently tested using a questionnaire. The results of the testing indicated that the application had an easy-to-understand interface, adequate features, and received suggestions for improvement. Overall, this smartphone application offers a modern and efficient solution to the challenges of delivering information within churches. It enhances accessibility and improves the quality of communication with the congregation.
Processing Student Comments on Understanding of Lecture Materials Using Rule Based Automata Finite State Model Febri Febri; Suharmanto Suharmanto
International Journal of Information Engineering and Science Vol. 1 No. 3 (2024): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i3.45

Abstract

Serang Raya University, is a private tertiary institution in the province of Banten, There are 4 faculties, Vocational D3 and 17 Study Programs, in the process of teaching and learning activities each lecturer has a different way of teaching students. Faculty of Information Technology, Computer Science Engineering study program with 30 lecturers, 9 classes, 59 courses and 126 students. This does not require the possibility that with the number of courses taken on campus there are sti ll many students who do not understand what the lecturer is delivering. Management of student comments on the understanding of lecture material is designed to make it easy for students to comment on the lecturer's presentation of the material. This is also used as evaluation material for lecturers regarding the delivery of material. Currently, Serang Raya University does not have a website-based information system. From this discussion, comments are made with the Rule-based Finite State Automata model. In reading the comments, this produces a system that can read comments word by word until the end of the word with a space separator so that it finds keywords, namely the keywords understand and don't understand.
Detection of Attacks in Computer Networks Using C4.5 Decision Tree Algorithm: An Approach to Network Security Wahyu Wijaya Widiyanto; Rizka Licia
International Journal of Information Engineering and Science Vol. 1 No. 4 (2024): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i4.48

Abstract

The detection of computer network attacks is becoming increasingly important as the complexity of cyber-attacks threatening information systems and infrastructure continues to rise. To address these threats, artificial intelligence techniques have become a primary focus in the development of more effective attack detection systems. One algorithm that has proven reliable in this context is the C4.5 decision tree. This study aims to apply the C4.5 algorithm in network attack detection using a dataset that includes various types of attacks and network activities. The process includes data preprocessing, decision tree model building, and model performance evaluation. The results show that the C4.5 decision tree algorithm is effective in classifying network activities into attacks and normal activities with a satisfactory level of accuracy. The model successfully recognizes attack-related patterns, and further analysis identifies key factors influencing attack detection. This research provides a significant contribution to the development of reliable and efficient attack detection systems in computer networks. By applying the C4.5 decision tree algorithm, it is expected to help enhance information security and protect network infrastructure from increasingly complex cyber threats
IoT, Anomaly Detection, Machine Learning, K-Nearest Neighbors, Random Forest, Real-Time Detection James Anderson; Emily Johnson; Michael Brown
International Journal of Information Engineering and Science Vol. 1 No. 1 (2024): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i1.50

Abstract

The increase in connected IoT devices causes increased vulnerability to cyber attacks. This research develops a hybrid machine learning model to detect real-time anomalies in IoT networks. This model combines the K-Nearest Neighbors (KNN) and Random Forest (RF) algorithms to increase accuracy and efficiency. Evaluation was carried out using the UNSW-NB15 dataset to test model performance. The results show that this hybrid approach is able to detect anomalies with high accuracy and a low false positive rate.
Optimizing Energy Consumption in Data Centers Using Machine Learning-Based Predictive Analytics James Wilson; Patricia Taylor; Elizabeth Thomas
International Journal of Information Engineering and Science Vol. 1 No. 1 (2024): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i1.51

Abstract

Data centers are major contributors to global energy consumption, with significant implications for operational costs and environmental sustainability. As energy demand increases, optimizing energy usage within these facilities has become essential. This study investigates the application of machine learning-based predictive analytics to enhance energy efficiency in data centers. By leveraging models such as Random Forest, Neural Networks, and Deep Learning, predictive analytics forecasts energy demands based on variables like temperature, workload, and time of day. Results from multiple case studies reveal that machine learning models can reduce energy consumption by up to 20%, offering a sustainable solution without compromising data center performance.
A Comparative Study of Edge and Cloud Computing Architectures for Industrial IoT Applications Muhammad Arifin; Ali Ramadhan; Hendra Wijaya
International Journal of Information Engineering and Science Vol. 1 No. 1 (2024): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i1.52

Abstract

The proliferation of the Industrial Internet of Things (IIoT) has transformed manufacturing, energy, and logistics, generating vast amounts of data that demand efficient processing solutions. Edge and cloud computing have emerged as two main architectures that can support IIoT applications by addressing latency, bandwidth, and computational challenges. This study presents a comparative analysis of edge and cloud computing architectures in the context of industrial IoT, focusing on performance, scalability, security, and cost. By analyzing case studies from manufacturing, logistics, and energy sectors, we identify the strengths and limitations of each approach, providing recommendations for selecting optimal architectures based on application needs.
Natural Language Processing For Automatic Sentiment Analysis In Social Media Data Siti Rahmawati; Dewi Anggraini; Rizki Kurniawan
International Journal of Information Engineering and Science Vol. 1 No. 1 (2024): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i1.54

Abstract

With the exponential growth of social media platforms, vast amounts of data are generated daily, capturing public opinions, sentiments, and trends in real time. Automatic sentiment analysis using Natural Language Processing (NLP) has emerged as an essential tool to process this data, helping industries, researchers, and policymakers understand social sentiment more effectively. This study explores various NLP techniques for sentiment analysis, including machine learning-based, lexicon-based, and deep learning models. By examining advancements in NLP algorithms and challenges related to language diversity, slang, and context in social media data, this paper highlights the strengths and limitations of current methodologies and discusses potential future directions.
Blockchain-Based Secure Data Sharing Framework For Healthcare Information Systems David Williams; Jessica Taylor; William Thompson
International Journal of Information Engineering and Science Vol. 1 No. 1 (2024): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i1.55

Abstract

Health information systems face major challenges in terms of data security and integrity. This article proposes a blockchain-based framework that enables secure data sharing among various entities in a health information system. The framework ensures the confidentiality, integrity, and transparency of patient data through the use of smart contract technology and hash-based encryption. Case studies on several hospitals demonstrate improved data security without sacrificing system efficiency.
Enhancing Cybersecurity Posture: A Framework for Anomaly Detection in Cloud Computing Environments David Jackson; Barbara Harris; Richard Clark
International Journal of Information Engineering and Science Vol. 1 No. 3 (2024): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i3.66

Abstract

The rapid adoption of cloud computing has transformed the way organizations manage and store their data. However, this shift has also increased vulnerabilities to cyber threats. Anomaly detection is a critical component of cybersecurity frameworks, allowing for the identification of unusual patterns that may indicate security breaches. This paper presents a comprehensive framework for anomaly detection in cloud computing environments. It reviews existing methodologies, explores the integration of machine learning techniques, and discusses the challenges associated with implementing these systems. The proposed framework aims to enhance the cybersecurity posture of organizations by providing proactive detection of anomalies.

Page 1 of 4 | Total Record : 37

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