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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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Blockchain Based Donation Management in Disaster Response Ankit K C
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 1 (2024): JINITA, June 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i1.2257

Abstract

Both natural and man-made disaster leave thousands of people in vulnerable and in need for essential aid. While individuals generously donate resources, traditional donation management systems suffer from limitations. Centralized control, opaque transactions, and potential corruption often hinder aid delivery and leave victims in despair. This paper proposes a novel Ethereum blockchain-based system for transparent and secure disaster donation management. Utilizing smart contracts, the system ensures traceability, accountability, and immutability of donations, empowering donors and fostering trust. This paper presents the system's architecture, detailing its components and interactions through sequence diagrams and algorithms. Additionally, successful testing on the Sepolia Ethereum testnet validates its functionality. To assess its effectiveness, a cost and security analysis is conducted. This blockchain-based framework offers a promising solution for transparent and efficient disaster response, potentially revolutionizing donation management. Further research, particularly on donation allocation optimization within the system holds immense potential for future development.
Implementation of Payment Gateway in the Mobile-Based Pawon Mbok`E Eating House Ordering System Fajar Mahardika; Ratih; R. Bagus Bambang Sumantri
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 1 (2024): JINITA, June 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i1.2289

Abstract

This paper discusses the implementation of a payment gateway in the mobile-based Pawon Mbok'E eating house ordering system. The integration of a payment gateway into mobile applications is crucial for facilitating secure and convenient transactions. Pawon Mbok'E aims to enhance customer satisfaction by enabling users to order food and make payments seamlessly through their mobile devices. Method research used is development system order mobile based with payment gateway integration. This implementation involves selecting an appropriate payment gateway, integrating it with the existing ordering system, ensuring security measures are in place, and testing for reliability and user-friendliness. The success of this implementation will provide Pawon Mbok'E customers with a streamlined ordering and payment process, thereby improving overall service efficiency and customer experience. Obtained testing reliability with amount respondents there are 65 as well percentage prove 100%, subject This show if 65 respondents That breast milk as well as No there is incoming respondents​ to type Excluded
Performance Optimization in Three-Modality Biometric Verification using Heterogeneous CPU-GPU Computation Bopatriciat Boluma Mangata; Pierre Tshibanda wa Tshibanda; Guy-Patient Mbiya Mpoyi; Jean Pepe Buanga Mapetu; Rostin Mabela Matendo Makengo; Eugène Mbuyi Mukendi
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.v6i2.2286

Abstract

This paper proposes a method to improve the performance of tri-modal biometric verification using a heterogeneous computing system exploiting the synergy between CPU and GPU. The main objective is to reduce the time required for verification while maintaining the system's accuracy. The design of this system is based on a decision fusion algorithm based on the logical OR connector, enabling the results of the three modalities to be combined. The implementation is being carried out in C# with Visual Studio 2019, using the Task Parallel Library to parallelize tasks on the CPU, and OpenCL.NET to manage processing on the GPU. The tests carried out on a representative sample of 1,000 individuals, show a clear improvement in performance compared with a sequential system. Execution times were significantly reduced, ranging from 0.03 ms to 0.67 ms for data sizes between 50 and 1000. Analysis of the performance gains, based on Amdahl's law, reveals that the proportion of tasks that can be parallelized remains higher in heterogeneous systems than in parallel and sequential systems, even though part of processing remains sequential for large data sizes. This study highlights the ability of heterogeneous computing systems to effectively reduce the verification time of biometric systems while maintaining an optimal balance between processing speed and overall efficiency. The results demonstrate the potential of this approach for advanced biometric applications, particularly in distributed environments.
Comparative Analysis of Keypoint Detection Performance in SIFT Implementations on Small-Scale Image Datasets Arif Rahman; Suprihatin; Imam Riadi; Tawar; Furizal
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.v6i2.2399

Abstract

Scale-invariant feature transform (SIFT) is widely used as an image local feature extraction method because of its invariance to rotation, scale, and illumination change. SIFT has been implemented in different program libraries. However, studies that analyze the performance of SIFT implementations have not been conducted. This study examines the keypoint extraction of three well-known SIFT libraries, i.e., David Lowe's implementation, OpenSIFT, and vlSIFT in vlfeat. Performance analysis was conducted on multiclass small-scale image datasets to capture the sensitivity of keypoint detection. Although libraries are based on the same algorithm, their performance differs slightly. Regarding execution time and the average number of keypoints detected in each image, vlSIFT outperforms David Lowe’s library and OpenSIFT.
Sentiment Analysis of Universitas Jember’s Sister for Student Application Using Gaussian Naive Bayes and N-Gram Mochamad Bagoes Alfarazi; Muhammad 'Ariful Furqon; Harry Soepandi
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.v6i2.2400

Abstract

This research aims to classify sentiment in reviews of the Universitas Jember Sister for Student application on Google Play Store, a vital student platform. The primary challenge tackled is the automated identification of positive and negative user sentiments. The study employs the Gaussian Naive Bayes method for classification and uses N-Gram techniques for sentiment analysis. The dataset consists of 1097 reviews, with 673 negative and 424 positive reviews, after removing 86 neutral spam reviews. The data is divided into 80% training data (877 reviews) and 20% test data (220 reviews). Gaussian Naive Bayes is used for modeling and combined with TF-IDF vectorization. The findings reveal that the Gaussian Naive Bayes model achieves an accuracy of 68%, precision of 68%, and recall of 71% on the test data. N-Gram analysis shows frequent occurrences of words like "bisa", "bagus", and "aplikasi" in positive sentiments, while "bisa", "hp", and "absen" are prevalent in negative sentiments. The study concludes that the Gaussian Naive Bayes model effectively classifies sentiment in application reviews, with the potential for further performance improvements.
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.

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