cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
 33   34   35   36   37 
Enhancing Web Security and Performance with Hybrid Stateless Authentication Mario, Benedictus; Wiradinata, Trianggoro; Christian, Christian
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9251

Abstract

Ensuring operational integrity across industries and protecting sensitive data require strong authentication systems. This paper presents a novel hybrid stateless authentication method that integrates binary payloads, token specifications, and database solutions. By employing a distinctive expiration policy, our proposed approach overcomes limitations inherent in traditional token revocation strategies while achieving token verification speeds that are up to 86 times faster than conventional statefull session-based methods. Overall, through uniformed benchmarking experiments and a comprehensive review of the literature substantiate the performance and security advantages of our method. Ultimately, this hybrid technique offers a more scalable and secure framework for authentication management, enabling efficient and flexible deployment in high-demand distributed environments.
Evaluation of the Effectiveness of Lightweight Encryption Algorithms on Data Performance and Security on IoT Devices Indrajati, Damar; Ashari, Wahid Miftahul
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9256

Abstract

Data security remains a major concern in the Internet of Things (IoT) landscape due to the inherent limitations in computational power, memory capacity, and energy availability of IoT devices. To address these challenges, lightweight encryption algorithms have emerged as alternatives to conventional cryptographic methods, aiming to balance performance and security. This study evaluates the effectiveness of five encryption algorithms—SIMON64/128, SPECK64/128, XTEA64/128, PRESENT64/128, and AES128—on IoT devices through experimental analysis of their security strength, execution time, CPU utilization, memory usage, and power efficiency. The experiments were conducted on a Raspberry Pi 3B+ using C-based implementations to emulate realistic IoT scenarios. The findings reveal that AES128 offers the strongest security characteristics, including the highest Avalanche Effect (39.29%) and Differential Resistance Score (6.76/10), but at the expense of significant resource consumption. In contrast, SIMON64/128 and SPECK64/128 deliver superior performance in terms of speed and resource efficiency, making them ideal for low-power environments, albeit with concerns about potential cryptographic backdoors. XTEA64/128 emerges as a practical compromise, delivering moderate security and low power consumption without known vulnerabilities. Based on these results, AES128 is suitable for high-capacity IoT platforms prioritizing strong encryption, while SIMON and SPECK are preferable for resource-constrained devices, with XTEA serving as a balanced alternative. This research contributes a comparative framework to guide the selection of encryption algorithms for IoT systems, ensuring an optimal trade-off between security and operational efficiency.
Evaluation of Scalability and Resilience of Hyperledger Fabric in Blockchain Implementation for Diploma Management Pebriyanti, Cahyani; Suranegara, Galura Muhammad
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9262

Abstract

This research aims to evaluate the performance of a Hyperledger Fabric-based blockchain system implemented for digital diploma management. The system is tested using the Caliper benchmark tool in various network and scalability scenarios, including normal conditions (baseline), network delay of 50ms, 100ms, 200ms, and 500ms; packet loss of 1%, 5%, 10%, and 15%; bandwidth limitation of 5 Mbps; high transaction load (scalability standard and scalability optimized); and extreme conditions in the form of Byzantine attacks with malicious nodes of 10%, 30%, and 50%. The evaluation was conducted using four key metrics: transaction success rate, failure rate, average transaction latency, and throughput (TPS). The system recorded high performance under normal network conditions with a success rate of 99.8%, latency of 0.89 seconds, and throughput of 9.7 TPS. Network disruptions such as delay, packet loss, and bandwidth limitation had only a minor impact, with the success rate remaining above 99% and latency gradually increasing. High load in the scalability scenario caused latency to increase to 27.21 seconds and failure rate to rise, but improved significantly after chaincode optimization. Meanwhile, the Byzantine scenario showed a drastic drop in performance with the success rate decreasing to 12.83% and the failure rate increasing to 87.17%. These results show that the Hyperledger Fabric-based digital diploma management system is resilient to common network disruptions and reliable at medium scale, but still requires strengthening the consensus mechanism to deal with extreme conditions and maintain reliability in environments that are not fully trusted.
Optimization of Random Forest Algorithm with Backward Elimination Method in Classification of Academic Stress Levels Amalia, Salsabila Dani; Barata, Mula Agung; Yuwita, Pelangi Eka
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9280

Abstract

Stress is a phenomenon experienced by all individuals as a natural response to pressure, which can impact mental and physical health. In an academic setting, the stress experienced by students is known as academic stress, which can affect their performance and mental well-being. Therefore, there is a need for effective prediction methods to aid in the management and prevention of academic stress. Therefore, there is a need to predict the level of academic stress to aid more effective management and prevention. This study uses a public dataset categorized based on the Student-life Stress Inventory (SSI), which includes psychological, physiological, social, environmental, and academic factors. Data mining is often used to detect diseases, one of which is the Random Forest algorithm. The Random Forest algorithm is applied as a classification technique for academic stress levels, with optimization using the Backward Elimination method for feature selection to improve model accuracy. The results showed that the accuracy of the Random Forest algorithm without feature selection obtained an accuracy of 86%, compared to the random forest algorithm with feature selection using the Backward Elimination method obtained a higher accuracy of 88%. This increase shows that the feature selection method can optimize model performance by selecting more relevant features. Thus, this research is expected to contribute to the management of student academic stress against the risk of academic stress.
User Interface Evaluation of the Sumber Alam Ekspres Application Using the Heuristic Evaluation Method Frobenius, Arvin Claudy; Kurniawan, Rizki Candra
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9285

Abstract

The Sumber Alam Ekspres mobile application is designed to facilitate users in booking bus tickets. Since its launch in December 2020, the app has garnered over 35,000 downloads, averaging 48 downloads per day. Currently, it holds a rating of 4.1 out of 5 on the Google Play Store. User reviews—207 in total—reveal various complaints, particularly regarding mismatched information, malfunctioning features, and unintuitive interface design. To investigate these issues, a usability evaluation was conducted using the Heuristic Evaluation Method with 20 respondents representing different user types and statuses. The evaluation revealed that only one usability principle—Visibility of System Status—achieved a high score (69%). Six heuristics received moderate ratings: Match Between System and the Real World (62.5%), User Control and Freedom (56.5%), Consistency and Standards (53.5%), Error Prevention (52.5%), Recognition Rather Than Recall (59.5%), and Aesthetic and Minimalist Design (63.5%). Meanwhile, three heuristics were rated low: Flexibility and Efficiency of Use (42%), Help Users Recognize, Diagnose, and Recover from Errors (37%), and Help and Documentation (38%). These findings highlight specific areas for improvement in the user interface, particularly in providing adequate guidance, improving efficiency, and ensuring a more intuitive user experience.
Sentiment and Emotional Analysis of The Public Housing Savings Program (TAPERA) using Orange Data Mining Fadly, Hawangga Dhiyaul; Jati, Handaru
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9297

Abstract

This study employs a text analysis methodology to assess public perception of the People's Housing Savings Program (TAPERA), by examining 3.078 tweets containing the keyword "tapera" using the Orange Data Mining application with two analytical approaches: the Valence Aware Dictionary and Sentiment Reasoner (VADER) for sentiment analysis and the Profile of Mood States (POMS) for emotional analysis. The sentiment analysis results indicate 1.481 tweets (48,2%) expressed negative sentiment, 830 tweets (27%) were neutral, and 767 tweets (24,8%) conveyed positive sentiment. These findings suggest that although there is a portion of positive responses toward the TAPERA policy, most of the public tends to express dissatisfaction or scepticism about the program. Furthermore, the emotional analysis identified depression as the most dominant emotion expressed by the public, appearing in 2.019 tweets (65,6%), followed by confusion (14,7%) and anger (9,6%). Positive emotions such as vigour and tension were recorded in significantly lower proportions, at 2,9% and 1,8%, respectively. These results illustrate that the public feels frustrated, confused, and anxious regarding the TAPERA policy, with minimal expressions of optimism or enthusiasm. This analysis highlights the need for a more transparent, educational, and data-driven communication approach to enhance public understanding, trust, and participation in the TAPERA policy. Therefore, the government must design more effective outreach strategies to address public concerns and ensure the successful implementation of this program.
Evaluating the Acceptance and Success of Mobile Banking Systems Using a Combination of UTAUT2 and Delone & McLean Models Khairun Nisak, Novrinda; Ibrahim, Ali; Ermatita
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9301

Abstract

Mobile Banking is a digital banking innovation designed to facilitate financial transactions, payments, and account management. However, ensuring that the application meets user expectations remains a challenge. Based on Playstore reviews, 30% of users reported various obstacles, particularly difficulty accessing the app, leading to transaction failures. This study aims to see what factors affect user satisfaction. The research employed the SemPLS method, chosen due to its ability to handle complex models with multiple latent variables and assess intricate relationships between constructs. SemPLS is particularly useful for exploratory research and allows analysis without strict assumptions regarding data distribution. Data were collected from 382 respondents, determined using the Lemeshow formula. Validity was tested using factor loading (≥0.7), while reliability was confirmed through Cronbach’s Alpha and Composite Reliability (CR) ≥0.7.The findings indicate that human factors significantly impact user satisfaction, contributing 43.6% base R-Square value. Key influencing factors include Price Value, Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, Facilitating Conditions, Habits, and Behavioral Intentions. Among these, Effort Expectancy, which represents ease of use, plays a crucial role in user satisfaction.To improve user experience, it is recommended to enhance access speed by optimizing server performance, reduce transaction failures through system stability improvements, and integrate AI-driven customer support for real-time troubleshooting. Future research could explore the role of trust and security perceptions in increasing user satisfaction and loyalty. These findings emphasize the importance of considering human aspects in digital service development to create a seamless and efficient banking experience.
The Development of a Deployment System Architecture for a Flask-Based Chatbot Using an LSTM NLP Model for Customer Service Question & Answer Mukti, David Ramantya; Salam, Abu
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9305

Abstract

In the past two decades, the rapid growth of e-commerce has significantly transformed global business practices. E-commerce has not only revolutionized the retail industry but also positively impacted businesses and consumer experiences. The ease of online shopping enables users to select products at more competitive prices. Amidst these changes, human-computer interactions have increasingly evolved toward natural conversations through Natural Language Processing (NLP). This study aims to develop a chatbot utilizing Long Short-Term Memory (LSTM) technology as a medium for e-commerce customer service. The dataset used for chatbot development is in JSON format and consists of 580 entries spanning 38 categories or classes. Data processing involves several preprocessing stages, including case folding, lemmatization, tokenization, and padding. The model is developed using a bidirectional LSTM and GRU architecture, followed by regularization techniques to enhance performance. Evaluation results show the model achieves 90% training accuracy and 63% validation accuracy with an F1-score of 62%. While there are indications of overfitting, the observed differences are not statistically significant, indicating the model remains capable of providing reliable responses. Additionally, the model is integrated into a Flask-based web application with an interactive interface to facilitate user access. This study demonstrates that LSTM is effective in addressing vanishing gradient problems.
A Comparative Performance of SMOTE, ADASYN and Random Oversampling in Machine Learning Models on Prostate Cancer Dataset Putra, Aditya Herdiansyah; Salam, Abu
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9308

Abstract

Class imbalance in medical datasets, including prostate cancer, can affect the performance of machine learning models in detecting minority cases. This study compares three oversampling techniques - SMOTE, ADASYN, and Random Oversampling - to address data imbalance in prostate cancer classification. These techniques are applied to Random Forest (RF), Decision Tree (DT), and LightGBM (LGBM), which are evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. In improving the reliability of the evaluation, K-Fold Cross Validation was used to reduce the risk of overfitting and ensure stable results. The findings show that oversampling techniques improve model performance compared to the baseline. Random Oversampling has the best performance for Random Forest with accuracy 0.85, recall 0.888, precision 0.873, F1-score 0.879, and ROC-AUC 0.838. SMOTE produced the highest Decision Tree performance with accuracy 0.80, recall 0.838, precision 0.843, F1-score 0.839, and ROC-AUC 0.788. ADASYN provided the most improvement for LightGBM, achieving accuracy 0.89, recall 0.919, precision 0.913, F1-score 0.913, and ROC-AUC 0.879. These results confirm that the oversampling method improves prostate cancer classification performance by tailoring the resampling technique to the model characteristics.
Sentiment Analysis on Public Perception of the Nusantara Capital on Social Media X Using Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) Methods Haliza, Dinda; Ikhsan, Muhammad
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9318

Abstract

The relocation and development of the National Capital City (IKN) as the center of government activities has become a hot topic, sparking diverse opinions among the public. The proposal to move the capital from DKI Jakarta to East Kalimantan has drawn significant attention from online communities, particularly on social media platform X (Twitter). This study aims to explore public sentiment regarding the development of IKN by applying artificial intelligence-based classification algorithms, namely Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). Sentiments are categorized as positive or negative to provide deeper insights into public perceptions. Through web crawling techniques, a total of 4,000 data points were collected. After the preprocessing stage, 3,608 data points remained, which were then translated into English to facilitate labeling using the Vader Sentiment method. The analysis results indicate that negative sentiment (1,873) is more dominant than positive sentiment (1,735). The data was then split into two sets: 80% for training (2,886 data points) and 20% for testing (722 data points). Based on the evaluation results, SVM and K-NN proved to be effective for sentiment analysis. SVM achieved an accuracy of 76%, precision of 78%, recall of 81%, and an f1-score of 79%, while K-NN attained an accuracy of 65%, precision of 62%, recall of 98%, and an f1-score of 76%. With superior performance, SVM emerges as a more reliable method for classifying public sentiment regarding the IKN development policy.

Page 35 of 70 | Total Record : 695

 33   34   35   36   37 

Filter by Year

2017 2025


Reset
Filter By Issues
All Issue Vol. 9 No. 6 (2025): December 2025 Vol. 9 No. 5 (2025): October 2025 Vol. 9 No. 4 (2025): August 2025 Vol. 9 No. 3 (2025): June 2025 Vol. 9 No. 2 (2025): April 2025 Vol. 9 No. 1 (2025): February 2025 Vol. 8 No. 2 (2024): December 2024 Vol. 8 No. 1 (2024): July 2024 Vol. 7 No. 2 (2023): December 2023 Vol. 7 No. 1 (2023): July 2023 Vol. 6 No. 2 (2022): December 2022 Vol 6 No 1 (2022): July 2022 Vol 5 No 2 (2021): December 2021 Vol. 5 No. 2 (2021): December 2021 Vol 5 No 1 (2021): July 2021 Vol. 5 No. 1 (2021): July 2021 Vol 4 No 2 (2020): Desember 2020 Vol. 4 No. 2 (2020): Desember 2020 Vol 4 No 1 (2020): Juli 2020 Vol 3 No 2 (2019): Desember 2019 Vol 3 No 1 (2019): Juli 2019 Vol 2 No 2 (2018): Desember 2018 Vol 2 No 1 (2018): Juli 2018 Vol. 2 No. 1 (2018): Juli 2018 Vol 1 No 2 (2017): Desember 2017 Vol 1 No 1 (2017): Juli 2017 More Issue