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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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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
Performance Evaluation of A Three-Modality Biometric System using Multinomial Regression Bopatriciat Boluma Mangata; Trésor Mazambi Kilongo; Pierre Tshibanda wa Tshibanda; Remy Mutapay Tshimona; Jean Pepe Buanga Mapetu; Eugène Mbuyi Mukendi
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2287

Abstract

In this article, we explored key concepts related to technology and system efficiency. We have created an innovative biometric system that combines three modalities: fingerprint, facial recognition and voice recognition. This approach guarantees enhanced security and a seamless user experience for access control. We tested our application to obtain the false rejection rate and the false acceptance rate, which gave us the confusion matrix. We then used the multinomial regression method to obtain the various parameter values, which are: FN=0.124, VPP=0.88, Sp=0.88, VPN=0.87, Se=0.87 and F-measure = 0.87 for voice recognition, FN=0.104, VPP=0.90, Sp=0.90, VPN=0.89, Se=0.89 and F-measure = 0.89 for face recognition, FN=0.08, VPP=0. 92, Sp=0.92, VPN=0.91, Se=0.91 and F-measure = 0.91 for fingerprints and FN=0.004, VPP=0.99, Sp=0.99, VPN=0.99, Se=0.99 and F-measure = 0.99 for the global system resulting from the fusion of these three modalities. From this result, we can say that using the global fusion of these three modalities, our system is very efficient compared to separate systems which give an advantage to the fingerprint recognition system followed by facial recognition and finally voice recognition. We recommend further studies to evaluate the performance of our system in real scenarios, using methods such as multinomial regression. This work paves the way for significant advances in the field of biometric systems and methods such as multinomial regression. We hope that these results will inspire further research and practical applications for a connected and secure world.
Natural Language Processing-Based Financial Time Series Forecasting: Utilizing Sentiment Analysis for Improved Stock Price Prediction Albert Ntumba Nkongolo; Yae Olatoundji Gaba; Kafunda Katalay Pierre; Esther Matendo Mabela; Ben Mbuyi Mpumbu
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2290

Abstract

This study explores the application of natural language processing (NLP) techniques in financial time series forecasting, specifically in predicting stock prices. Historical stock price data and textual data from financial news articles and social media sources were collected, and TextBlob was used to obtain sentiment indices from the textual data. A hybrid model combining NLP techniques with LSTM (Long Short-Term Memory) neural networks was developed, and the methodology involved preprocessing and analyzing textual data using sentiment analysis with TextBlob and integrating the sentiment indices with historical stock price data for forecasting with LSTM. The LSTM model achieved a performance of 89.6 percent precision and outperformed traditional time series forecasting models in terms of accuracy and reliability. The results demonstrate that incorporating sentiment indices obtained through NLP significantly enhances the predictive performance of stock price forecasting models, and the study highlights the potential of NLP techniques, particularly sentiment analysis with TextBlob, in conjunction with LSTM neural networks, to improve the accuracy of financial time series forecasting, specifically in predicting stock prices.   Studi ini mengeksplorasi penerapan teknik pemrosesan bahasa alami (Natural Language Processing/NLP) dalam peramalan deret waktu keuangan, khususnya untuk memprediksi harga saham. Data harga saham historis dan data tekstual dari artikel berita keuangan serta sumber media sosial dikumpulkan, dan TextBlob digunakan untuk memperoleh indeks sentimen dari data tekstual tersebut. Sebuah model hibrida yang menggabungkan teknik NLP dengan jaringan saraf LSTM (Long Short-Term Memory) dikembangkan, dan metodologinya melibatkan praproses dan analisis data tekstual menggunakan analisis sentimen dengan TextBlob, serta integrasi indeks sentimen dengan data harga saham historis untuk peramalan menggunakan LSTM. Model LSTM ini mencapai kinerja dengan tingkat ketepatan (precision) sebesar 89,6 persen dan mengungguli model peramalan deret waktu tradisional dalam hal akurasi dan keandalan. Hasilnya menunjukkan bahwa penggabungan indeks sentimen yang diperoleh melalui NLP secara signifikan meningkatkan kinerja prediktif model peramalan harga saham, dan studi ini menekankan potensi teknik NLP, khususnya analisis sentimen dengan TextBlob, dalam kombinasi dengan jaringan saraf LSTM, untuk meningkatkan akurasi peramalan deret waktu keuangan, khususnya dalam memprediksi harga saham.
Fuzzy Expert System for Decission Support to Diagnosis Leukemia Linda Perdana Wanti; Nur Wachid Adi Prasetya; Zahrun Nafisa; Rahmat Mulyadi; Muhammad Ramadani
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2349

Abstract

Leukemia is a cancer of the blood and bone marrow. In leukemia, the bone marrow produces too many abnormal white blood cells. These abnormal cells cannot fight infections well and can displace healthy blood cells, which can cause anemia and bleeding. In this study, a fuzzy method will be implemented to diagnose leukemia and the results will later be compared with expert diagnoses. Fuzzy logic was chosen because it allows for degrees of truth between 0 (completely false) and 1 (completely true) and it is suitable for situations where human expertise relies on experience and judgment rather than fixed rules. Fuzzy systems can analyze large amounts of data quickly, thereby accelerating the diagnosis and decision-making process, especially when used in medical decision support systems. This study produced a leukemia diagnosis accuracy of 88.83% when compared with the results of expert diagnoses using the same symptom and sample data.
The Barriers of Knowledge Acquisition and Knowledge Transfer in Software Companies: A Systematic Literature Review Ersha Aisyah Elfaiz
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2522

Abstract

Nowadays, organizations need computer-based information systems to improve their business process. Software company provide service to accommodate that needs. In the development process, knowledge acquisition and knowledge transfer are the most important process to understand the technology used. However, knowledge gap is a problem that may occur. To solve that, this research provides the barriers or problems that may occur in two process and the solutions. From the systematic literature review, we get 20 studies that relevant. Then, we know that knowledge acquisition barriers caused by human and organization. Knowledge transfer barriers caused by human, organization and technology. We also summarize the solution that proposed in the studies.
Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study Md. Wira Putra Dananjaya; Putu Gita Pujayanti
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2540

Abstract

Academic performance prediction is a crucial area in education; however, the complexity of influencing factors often cannot be adequately captured by simple linear models. This research conducts a methodological comparative analysis of five machine learning models Simple Linear Regression, Multiple Linear Regression (MLR), Decision Tree, Random Forest, and Artificial Neural Network (ANN) to determine the most accurate predictive approach using a comprehensive dataset encompassing academic, behavioral, and psychosocial factors. The models were evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. Evaluation results on the test data revealed that the Multiple Linear Regression (MLR) model unexpectedly delivered the most superior performance, achieving an R2 value of 0.7324 and the lowest RMSE of 2.0391. Further analysis from non-linear models identified Attendance and Hours_Studied as the two factors with the highest predictive influence. This study concludes that interpretable models like MLR can be highly effective when supported by relevant features, offering practical implications for institutions to develop effective early warning systems by focusing on key, actionable factors.
A Risk Management Guide for Information System Infrastructure in Digital Banking Raden Budiraharjo; Silhi; Ali Jazzy; Na'il Ghani Prihartono
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2621

Abstract

Digital banks rely heavily on IT infrastructure to support digital services, data management, and transaction processing, making them vulnerable to risks such as system failures, cybersecurity threats, and regulatory compliance. The implementation of Information Systems Risk Management (ISRM) is crucial to ensure data security and regulatory compliance. This study integrates ISO-31000: 2018, NIST SP 800-30, COBIT 2019, and Risk IT Framework to design a comprehensive risk management guide for banks, especially digital banks. ISO-31000: 2018 is used to define the objectives, scope, stakeholders, risk tolerance, and boundaries of risk management., NIST SP 800-30 is used for risk identification and assessment, Risk IT Framework is used to determine risk responses, and COBIT 2019 provides principles and practices that can be implemented to address risks. The research approach includes risk identification, assessment of likelihood and impact, selection of risk response options (Avoid, Reduce/Mitigate, Share/Transfer, Accept), and implementation of action plans. The study shows that the integration of this framework enables the bank to effectively address high-priority risks. After implementing the COBIT 2019-based mitigation plan, the risk score can be significantly lowered, putting the risk in an acceptable position. In addition, this approach enables the bank to comprehensively identify information technology and systems risks and implement action plans to reduce risks to an acceptable level.
Hybrid Approach for Protein Secondary Structure Prediction with KNN, SVM, and Neural Network Algorithms Benjamin Mukanya Ntumba; Jean Paul Ngbolua Koto-Te-Nyiwa; Blaise Bikandu Kapesa; Nathanael Kasoro Mulenda
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2658

Abstract

One of the main challenges in bioinformatics is predicting the structures of macromolecules, particularly nucleic acids and proteins. In this study, we propose a hybrid approach integrating K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Neural Network (NN) algorithms. We perform an in-depth analysis using various metrics, including accuracy, Q3 score, ROC, and precision-recall curves. Based on the RS126 dataset, we compared our hybrid model with individual approaches, revealing that our model achieves an accuracy of 80% and a Q3 score of 86%, outperforming each of the algorithms separately. These results validate the effectiveness of combining models for protein secondary structure prediction (PSSP). We show that the hybrid model outperforms the other models for this task. We also discuss the implications of these results and propose future work to further improve the accuracy and robustness of the model. This approach could have important implications for protein structure modeling, in particular for understanding their three-dimensional structure and function.

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