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Muhammad Nur Faiz
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Journal of Innovation Information Technology and Application (JINITA)
Published by Politeknik Negeri Cilacap
ISSN : 27160858     EISSN : 27159248     DOI : https://doi.org/10.35970/jinita.v2i01.119
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Software Engineering, Mobile Technology and Applications, Robotics, Database System, Information Engineering, Interactive Multimedia, Computer Networking, Information System, Computer Architecture, Embedded System, Computer Security, Digital Forensic Human-Computer Interaction, Virtual/Augmented Reality, Intelligent System, IT Governance, Computer Vision, Distributed Computing System, Mobile Processing, Next Network Generation, Natural Language Processing, Business Process, Cognitive Systems, Networking Technology, and Pattern Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 160 Documents
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Mitigating the Risks of Enterprise Software Upgrades: A Change Management Perspective: A Change Management Perspective Hewa Majeed Zangana; Natheer Yaseen Ali; Marwan Oma
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2404

Abstract

Enterprise software upgrades are crucial for maintaining competitive advantage, ensuring security, and enhancing operational efficiency. However, these upgrades often pose significant risks, including system disruptions, data loss, and user resistance. The problem lies in effectively managing these risks to avoid operational setbacks and ensure successful adoption. This paper explores the role of change management in mitigating these risks by offering solutions through strategic planning, stakeholder engagement, and comprehensive training programs. The research employs a mixed-methods approach, integrating quantitative survey results from 185 participants and qualitative insights from 20 in-depth interviews. Results indicate that organizations prioritizing stakeholder engagement, tailored training, and proactive communication achieve higher user satisfaction, improved system performance, and enhanced operational efficiency. These findings provide a framework for best practices in change management that minimize risks and promote successful software upgrades.
Application of Machine Learning in Fault Detection And Classification in Power Transmission Lines Michel Evariste Tshodi; Nathanael Kasoro; Freddy Keredjim; ALbert Ntumba Nkongolo; Jean-Jacques Katshitshi Matondo; Paul Mbuyi Balowe; Laurent Kitoko
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2424

Abstract

Electrical faults have been identified as a significant contributing factor to electrical equipment damage. Such incidents can potentially result in a range of adverse consequences, including bushfires, electrical outages, and power shortages. The detection and classification of faults facilitates the delivery of superior quality of service, the preservation of the environment, the prevention of equipment damage, and the satisfaction of electricity line subscribers. In this study, we analyze the data from an electrical network comprising four generators of 11 kV, which have been modeled in Matlab. The generators are situated in pairs at either end of the transmission line. Subsequently, machine learning techniques are employed to detect faults in the transmission between lines, and machine learning models are utilized to classify the faults. Four distinct supervised machine learning classifiers are employed for comparison purposes, with the results presented in a confusion matrix. The results demonstrated that decision trees are particularly well-suited to this task, with an 88.6205% detection rate and a slightly higher accuracy than the random forest algorithm (87.9212% detection rate). The K-nearest neighbor's approach yielded a lower result (80.4196% of faults detected), while logistic regression demonstrated the lowest performance, with 34.5836% of faults detected. Six fault categories were found in the dataset: No-Fault (2365 occurrences), Line A Line B to Ground Fault (1134 occurrences), Three-Phase with Ground (1133 occurrences), Line-to-Line AB (1129 occurrences), Three-Phase (1096 occurrences) and finally Line-to-Line with Ground BC (1004 occurrences).
Development of Android-Based Interactive Learning Media on Computer Assembly Material with the ADDIE Model Approach: case study : SMK Negeri 1 Lolayan Jemmy Pakaja; Hermila A.; Alfito Paputungan
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2436

Abstract

This study raises the title of the development of android-based interactive learning media on computer assembly material at SMK Negeri 1 Lolayan. Based on the observations and interviews, several problems were found in learning computer assembly material, including teachers still using PowerPoint learning media that is only text-based. During the practicum, one of the computers used was damaged. This is due to limited knowledge and inadequate facilities, resulting in teachers' lack of innovation and creativity in developing learning media, which makes it difficult for students to understand the material. This study aims to develop android-based interactive learning media on Computer Assembly material for class X TKJ students at SMK Negeri 1 Lolayan and test the feasibility of interactive learning media through material and media expert feasibility tests and determine the practicality of learning media through respondent trials (students/users). The research method used is Research and Development (R&D) with the ADDIE development model (Analyze, Design, Development, Implementation, Evaluation). The results of this study were obtained from feasibility testing by material experts, who obtained an average value of ‘138’ with the criteria ‘Very Feasible.’ The results of feasibility testing by media experts obtained an average value of ‘120’ with the criteria ‘very Feasible,’ and the results of testing student response responses obtained an average value of ‘101’ with the criteria ‘Very Feasible’. The results showed that Android-based interactive learning media is feasible to use as an alternative to learning computer assembly
An Intelligent System for Light and Air Conditioner Control Using YOLOv8 Ikharochman Tri Utomo; Muhammad Nauval Firdaus; Sisdarmanto Adinandra; Suatmi Murnani
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2446

Abstract

High energy consumption in classrooms is a significant concern, often resulting from inefficient lighting and air conditioning systems. Specifically, the problem lies in the lack of automated control mechanisms that adjust energy use based on real-time occupancy data. This study aims to develop and evaluate a system that employs a camera integrated with the YOLOv8 algorithm to detect human presence and optimize energy usage by controlling lights and air conditioning. The system's performance was assessed in three different classroom environments: two large and one small. The system's accuracy for occupancy detection varied from 13.64% to 100%, depending on lighting conditions and room size. Light control accuracy was highest in the classrooms with consistent lighting, reaching 99.77%. Air conditioning control achieved perfect accuracy of 100% in the classroom with a SHARP brand AC, with a maximum remote-control range of 7 meters. These findings indicate that the system's performance is influenced by lighting conditions and room size, with smaller rooms showing better results. The system demonstrates promising potential for reducing energy consumption in classroom settings, thereby contributing to more sustainable energy practices.
Programming Languages Prediction from Stack Overflow Questions Using Deep Learning Razowana Khan Mim; Tapu Biswas
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2453

Abstract

Understanding programming languages is vital in the ever-evolving world of software development. With constant updates and the emergence of new languages, staying informed is essential for any programmer. Additionally, utilizing a tagging system for data storage is a widely accepted practice. In our study, queries were selected from a Stack Overflow dataset using random sampling. Then the tags were cleaned and separated the data into title, title + body, and body. After preprocessing, tokenizing, and padding the data, randomly split it into training and testing datasets. Then various deep learning models were applied such as Long Short-Term Memory, Bidirectional Long Short-Term Memory, Multilayer Perceptron, Convolutional Neural Network, Feedforward Neural Network, Gated Recurrent Unit, Recurrent Neural Network, Artificial Neural Network algorithms to the dataset in order to identify the programming languages from the tags. This study aims to assist in identifying the programming language from the question tags, which can help programmers better understand the problem or make it easier to understand other programming languages.
Mobile-Based Event Decoration Ordering System Using UAT Method with PIECES Framework Hadi Jayusman; Fajar Mahardika; Ratih
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2472

Abstract

The Mobile Event Decoration Booking System is an innovative solution designed to facilitate users in ordering event decorations. By implementing the User Acceptance Testing (UAT) method and the PIECES framework, this system ensures that the developed application meets the needs and expectations of users. This research aims to identify and analyze key features in the ordering process and evaluate user satisfaction with the application. Respondents provide valuable feedback regarding the interface, functionality, and overall user experience through UAT. The research results indicate that this application can enhance the efficiency of bookings, reduce communication errors between service providers and customers, and offer a better experience. With the application of the UAT method, users feel that this system effectively meets their needs, resulting in an improved experience in event planning. These findings suggest that the factors influencing user satisfaction and interest are adequate and should be maintained. The Mobile Event Decoration Booking System has successfully improved the efficiency and effectiveness of the booking service, with an average user satisfaction rate of 95%.
Website Security Analysis Using Vulnerability Assessment Method : Case Study: Universitas Internasional Batam Haeruddin; Gautama Wijaya; Hendra Winata; Sukma Aji; Muhammad Nur Faiz
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2476

Abstract

In today’s digital era, ensuring website security is crucial, especially in the education sector which is frequently targeted by cyber attacks. This research aims to test security of the Universitas Internasional Batam (UIB) website using OWASP ZAP and Nessus. The method will be used in this research was vulnerability assessment. It will involve gathering information with the tools such as, Nmap, whois and nslookup. OWASP ZAP detected 11 vulnerabilities, categorized into 6 medium level and 5 low level, including Content Security Policies (CSP) and anti-clickjacking headers. Otherwise, Nessus only detected one medium level vulnerability, the absence of HTTP Strict Transport Security (HSTS). The difference in detection results from the tools that OWASP ZAP is better at finding web application weakness that are consistent with the OWASP Top Ten 2021, while Nessus specifically targets server and network configuration. For educational institutions, these results emphasize the importance of conducting regular vulnerability assessment to protect sensitive data. Recommended action include implementing CSP to prevent Cross-site scripting (XSS) and other injection attacks, enforcing HSTS to secure communication, and its recommend to updating software to mitigate the unknown vulnerabilities. By adopting these measures, institutions can reduce their exposure to cyber attacks, its also can maintain user trust, and strengthen overall security. This research provides a pratical framework for stregthening the security of educational websites against evolving threats. These findings highlight that the importance of using multiple tools can provide a more comprehensive view of security gaps.
The Influence of the Tiktok Application on Cyberbullying Behavior : Case Study: Students of SMP Negeri 5 Depok Nur Maulidia Wati; Leliyanah; Sri Hardani
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2491

Abstract

Cyberbullying is threatening, insulting, or intimidating behavior carried out through online media. This cyberbullying behavior is vulnerable to being carried out or felt by teenagers who are still easily instigated by bad actions around them. Therefore, this study aims to determine what effects the TikTok application has on cyberbullying behavior in adolescents and to find out the causes and handling solutions for cyberbullying behavior. The research was conducted using the Technology Acceptance Model (TAM) method and the descriptive quantitative method. The research was conducted from June 11 to June 21, 2024, with a sample size of 91 students determined using the proportionate stratified random sampling method. The results of hypothesis testing with the t-test state that perceived usefulness has no effect on real conditions of use, then perceived ease of use and behavior to continue using positively affect real conditions of use. Meanwhile, attitude towards use harms the real conditions of use. The f-test states that all variables have a simultaneous effect. Meanwhile, the R-Square test states that perceived usefulness, perceived ease of use, attitude towards use, and behavior to continue using contribute 62.4% to the real conditions of use
Parallel Computing Applied to A Three-Modality Biometric Recognition System using Task Parallel Library Bopatriciat Boluma Mangata; Pierre Tshibanda wa Tshibanda; Vince Muladi Tsitabi; Jean Pepe Buanga Mapetu; Rostin Mabela Matendo Makengo; Eugène Mbuyi Mukendi
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2288

Abstract

This study focuses on optimizing the performance of a three-modality biometric recognition system (fingerprints, facial and voice recognition) with global decision fusion, designed for access control to secure areas. When the biometric database contains a large volume of information, the verification module's processing time increases considerably due to the complexity of template comparisons. To address this issue, we implemented an optimization strategy based on parallel programming, specifically targeting the intensive processing loops within the verification module. Using Microsoft's Task Parallel Library, we parallelized all critical loops associated with the three biometric modalities. By effectively exploiting for and foreach statements, our parallelized implementation enables optimal distribution of tasks across available processor cores. We validated our approach by conducting repeated experiments on data sets of varying sizes (50 to 600 individuals), with a rigorous analysis of temporal performance. The results show a significant reduction in execution times: for 600 entries, the processing time goes from 1.68 ms in sequential mode to 0.77 ms in parallel mode. These performances were evaluated over several iterations to ensure the statistical reliability of the results, in particular by calculating averages and standard deviations and including error bars in the comparative graphs. The practical implications of this work are significant: the module can be deployed in corporate security systems, airports or banks, while respecting ethical considerations and privacy constraints. Finally, this work paves the way for future extensions, including the integration of other biometric modalities, deployment on distributed clusters or the adoption of more advanced parallelization frameworks.
Implementation of CNN Algorithm for Baby Blues Detectionin Postpartum Mothers Through Facial Image Analysis Muhammad Fikri Hidayattullah; Yustia Hapsari; Movida Tantra Putra Malani; Laela Diyah Puspita; Syeli Mutiatul Hilmy; Zielda Okkya Lorosae
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2596

Abstract

The use of computer technology in the healthcare sector is growing, especially in supporting recommendation systems and early detection of various health conditions. Artificial intelligence, particularly deep learning, has made significant contributions in analysing complex data such as medical imaging. One of the leading deep learning methods is Convolutional Neural Network (CNN), which is able to extract visual features hierarchically and accurately. Baby blues is a psychological disorder often experienced by mothers after childbirth and can have a serious impact on the mother's mental health and relationship with the baby. Early detection of baby blues is crucial to provide appropriate interventions and prevent worse outcomes. This research aims to implement CNN algorithm to detect baby blues through facial image analysis. Using a dataset of postpartum mothers, a CNN model was developed to recognise visual patterns related to baby blues symptoms. The results showed that the CNN model was able to identify baby blues conditions with an accuracy of 53% on the dataset used. This research proves the effectiveness of CNN in detecting visual patterns related to babyblues disorder, and is expected to be a solution in supporting early diagnosis and appropriate treatment for postpartum mothers.

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