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International Journal of Informatics and Computation
Published by Universitas Respati Yogyakarta
ISSN : 26858711     EISSN : 27145263     DOI : 10.35842/ijicom
Core Subject : Science,
Computer Science & IT
International Journal of Informatics and Computation (IJICOM) is an international, peer-reviewed, open-access journal, that publishes original theoretical and empirical work on the science of informatics and its application in multiple fields. Our concept of Informatics includes technologies of information and communication as well as the social, linguistic, and cultural changes that initiate, accompany, and complicate their development. IJICOM aims to be an international platform to exchange novel research results in simulation-based science across all scientific disciplines. It publishes advanced innovative, interdisciplinary research where complex multi-scale, multi-domain problems in science and engineering are solved, integrating sophisticated numerical methods, computation, data, networks, and novel devices. The scope of this journal includes IoT, 5G, Artificial Intelligence, sensor networks, and high-resolution imaging techniques. This new discipline in science combines computational thinking, modern computational methods, devices, and collateral technologies to address problems far beyond the scope of traditional numerical methods
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 61 Documents
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Mobile Application Development for Chili Disease Detection with Convolutional Neural Network Sri Winiarti; Itsnaini Irvina Khoirunnisa
International Journal of Informatics and Computation Vol. 6 No. 2 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i2.93

Abstract

Demand for chili peppers continues to increase with population growth and the industrial sector, but the supply could be more stable due to weather factors. This causes diseases in chili plants, such as fruit rot due to anthracnose, begomovirus yellow virus, and leaf spot. This research aims to develop a chili plant disease identification system and evaluate the accuracy of disease image classification. With this system, farmers are expected to recognize diseases earlier and improve the quality and quantity of crops. The method used is a Convolutional Neural Network (CNN). The research stages include data collection, preprocessing, model design, and system testing. The dataset of chili plant disease images was obtained from a garden in Sumowono District, Semarang Regency, Central Java, with 4,500 images, divided by 70% for training data and 30% for validation. The accuracy results obtained were 99% in the training process and 94% in validation. Evaluation of the model with a new dataset of 150 images showed 94% accuracy. Functional testing and user testing on the mobile system by ten farmers resulted in an average score of 90. Thus, this mobile system can identify
Virtual Reality and Augmented Reality in Sign Language Recognition: A Review of Current Approaches Aris Rakhmadi; Anton Yudhana; Sunardi Sunardi
International Journal of Informatics and Computation Vol. 6 No. 2 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i2.94

Abstract

This paper delivers a comprehensive review of the current approaches to Sign Language Recognition (SLR) using Virtual Reality (VR) and Augmented Reality (AR) technologies. Sign language is essential for communication within the deaf and hard-of-hearing community, and traditional SLR methods have faced several challenges, including limited gesture recognition, lack of context awareness, and scalability. VR and AR, with their immersive and interactive environments, offer promising solutions to overcome these limitations. This review explores how VR and AR can enhance SLR by providing real-time feedback, personalized learning experiences, and more dynamic and engaging systems. It also examines the integration of VR and AR with advanced technologies such as machine learning and computer vision, which have significantly enhanced the accuracy and efficiency of sign language recognition. Despite progress, challenges related to hardware limitations, cultural diversity, and user experience remain. The paper concludes by highlighting future directions, including advancements in AI, increased affordability, and the need for interdisciplinary collaboration to ensure the development of inclusive, scalable, and accessible SLR systems
Development of a Web-Based Administration System for the Bantu Sodara Community Using the Waterfall Method Bima Randy Gumilang; Agung Nugroho; Ahmad Turmudi Zy
International Journal of Informatics and Computation Vol. 6 No. 2 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i2.95

Abstract

In this research, the aim is to develop a web-based administrative system for the Bantu Sodara Community to overcome manual administration constraints. Through a web-based system development approach, needs analysis, system design, implementation, and evaluation were conducted. Data were obtained through interviews, observations, and literature reviews. The results indicate that the development of a web-based administrative system can improve the efficiency and accuracy of community administration data management. The implementation of this system provides ease of access, centralized data management, and transparency of information for all community members. Thus, the use of a web-based administrative system is expected to provide an effective solution to the manual administration constraints experienced by the Bantu Sodara Community.This research aims to develop a web-based administrative system for the Bantu Sodara Community to address the limitations of manual administration. The system development process included needs analysis, system design, implementation, and evaluation. Data were collected through interviews, observations, and literature reviews. The results indicate that the web-based administrative system significantly improves the efficiency and accuracy of community data management. The system enhances accessibility, centralizes data management, and ensures transparency of information for all community members. Therefore, the implementation of this web-based system is an effective solution to the administrative challenges faced by the Bantu Sodara Community.
Enhancing Web Security Using AES and Twofish Algorithms Ahmad Sahal; Farida Nur Aini; Zaidir Zaidir; Indra Listiawan
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.98

Abstract

The advancement of information technology has driven the massive adoption of web-based information systems across various sectors. However, this surge in usage has been accompanied by increasingly complex data security threats, such as SQL injection attacks and sensitive information theft. This article proposes a security enhancement strategy through the implementation of encryption at the database and source code levels, focusing on the AES and Twofish algorithms. Web security, particularly through the use of the Advanced Encryption Standard (AES), plays a crucial role in safeguarding sensitive data across various applications. AES, especially in its 256-bit key variant (AES-256), is widely recognized for its robust security features, making it a preferred choice for encrypting data in cloud environments and web applications. The following sections highlight key aspects of AES in web security. The research findings that AES dan Twofish algorithms can provide an optimal balance between security and efficiency, making it a relevant solution for addressing information security challenges in the real application.
Honeypot Integration with Software-Defined Networking (SDN) for DDoS Attack Mitigation Sugiyatno
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.101

Abstract

Distributed Denial of Service (DDoS) attacks are one of the serious threats in cybersecurity that can disrupt the availability of network services. Traditional approaches to DDoS mitigation often have limitations in detecting complex attack patterns and responding dynamically. This research aims to develop a framework that integrates Honeypot with Software-Defined Networking (SDN) to improve the ability to adaptively mitigate DDoS attacks. The SDN approach was chosen due to its unique ability to provide centralized network control and high flexibility in real-time traffic management. The research method involves developing a prototype of Honeypot-SDN integration, testing through simulation using Mininet, as well as analyzing network traffic data using machine learning algorithms. Data obtained from simulated DDoS attacks with various scenarios, including variations in attack intensity and type, were analyzed to test the effectiveness of the system.
Impact of Digital Technology on Improving Women's Reproductive Health: Literature Review Mestika Rija Helti; Juita Sari; Rayandra Asyhar
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.102

Abstract

Low knowledge about reproductive health among mothers and adolescent girls leads to a lack of understanding of its importance. This limited knowledge results in women's inability to care for their reproductive organs, often exacerbated by conflicting or insufficient information. The stigma surrounding discussions on reproductive health, particularly in rural areas, further restricts access to accurate knowledge. Social media and the internet have emerged as vital tools in enhancing reproductive health education by offering accessible, interactive, and engaging information. This literature review employs a grey literature search through Google Scholar to analyze 12 articles that meet inclusion criteria. Three key themes are identified: social media, knowledge, and reproductive health. The findings highlight the significant role of social media platforms in improving women's reproductive health education, especially among adolescents. Online interactions are preferred over traditional classroom-based health education, as they provide a more practical and enjoyable learning experience. This review underscores the potential for leveraging digital platforms to support nurses and healthcare providers in delivering effective health education and promotion strategies tailored to women's needs.
Building Employee Health Monitoring Tool Using IoT Oximeter in Palm Industry Helmi Wardah Nasution; Dwi Ris Hasanah; Rayandra Asyhar
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.103

Abstract

Ensuring employee health and safety is a critical concern in the palm oil industry, where physically demanding environments can pose significant risks. Traditional health monitoring methods often lack real-time data collection and prompt intervention capabilities, creating gaps in maintaining occupational health standards. This paper addresses these challenges by presenting the design and implementation of an Internet of Things (IoT)--based oximeter prototype tailored to the needs of the palm oil industry. The system leverages the Blynk IoT platform to enable continuous monitoring of employees' oxygen saturation levels (SpO2) and heart rates in real time. By integrating sensor-based data collection with a user-friendly interface, the prototype facilitates immediate access to vital health metrics and sends alerts for abnormalities. A simulated palm oil plantation environment was used to evaluate the system, demonstrating its effectiveness in delivering accurate health measurements and timely notifications. Results indicate that the IoT-based oximeter significantly enhances employee health monitoring by addressing the limitations of traditional methods, promoting proactive health management, and enabling timely interventions. This innovation not only improves workplace safety but also fosters a culture of health awareness among employees and is a valuable tool for occupational health monitoring in the palm oil industry.
Sentiment Analysis of a 271 Trillion Rupiahs Corruption Case Using LSTM Selamet Riadi; Rudi Muslim; Emi Suryadi; Karina Nurwijayanti; M. Zulpahmi; Muhamad Masjun Efendi; Bahtiar Imran
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.104

Abstract

Corruption is one of the most pressing issues in Indonesia, significantly affecting public trust in governance and the nation’s development. Among the many corruption cases that have surfaced, the recent 271 trillion rupiah corruption case has drawn widespread attention and public discourse. Understanding the public's perception and sentiment regarding such cases can provide valuable insights into how these issues impact society. Researchers identified an opportunity to leverage sentiment analysis as a method to capture and analyze public sentiment in this context. The dataset for this study was collected from the social media platform Twitter (X) using a data crawling technique. Prior to analysis, preprocessing was performed to clean and prepare the data. After preprocessing, the data was categorized into three sentiment labels: negative, positive, and neutral. To perform sentiment classification, this study utilized the LSTM (Long Short-Term Memory) algorithm, a deep learning method particularly suited for sequential data analysis. The model was trained over a total of 10 epochs. The classification results demonstrated that the LSTM algorithm achieved an accuracy of 0.9365 at the 10th epoch, showcasing its effectiveness in analyzing public sentiment regarding 271 trillion rupiah corruption issues.
Vehicle Theft Detection Using YOLO Based on License Plates and Vehicle Ownership Bradika Almandin Wisesa; M. Hizbul Wathan; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Silvia Agustin; Better Swengky
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.105

Abstract

Detection of vehicle theft requires innovative approaches to address an increasing number of cases in Indonesia. This study presents a YOLOv11-based system for detecting vehicle theft by combining real-time object detection with a vehicle ownership database. The proposed system identifies license plates, detects vehicle owners using facial recognition, and analyzes suspicious activity to determine theft occurrences. The proposed method can produce model effectiveness with an accuracy = 70%. Key improvements in architecture, including enhanced feature fusion and dynamic anchor assignment, contribute to the object’s detection in complex environments. This research can be a potential technique to provide efficient, scalable, and real-time security solutions in dynamic surveillance applications.
Sentiment Analysis on Canva Reviews Using Naive Bayes Method Faiza Muhammad Julianto; Ahmad Turmudi Zy; Elkin Rilvani
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.107

Abstract

Sentiment analysis for user review is a growing research topic in digital applications. In this study, we analyze user reviews from the Google Play Store to classify sentiments as positive or negative. The primary objective of this research is to evaluate the performance of the Naive Bayes classifier in sentiment classification. The methodology involves comprehensive data preprocessing, model training, and evaluation using performance metrics such as accuracy, precision, recall, and F1-score. The results indicate that the proposed model achieves an accuracy = 92%, precision = 85%, recall = 88%, and F1-score= 86%, respectively. These findings show the effectiveness of the proposed method that can extract valuable insights from user reviews to increase user satisfaction.

Page 6 of 7 | Total Record : 61

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