Fingerprint file integrity verification in driver license systems requires reliable cryptographic hash algorithms. MD5, currently widely deployed, has been deprecated by NIST (2008) due to demonstrated collision vulnerabilities, while SHA-256 offers enhanced security with potentially higher computational overhead. This study comprehensively compares MD5 and SHA-256 performance and security characteristics to provide evidence-based recommendations for biometric data integrity verification. We conducted empirical benchmarking using 1,000 real operational fingerprint files (BMP 8-bit grayscale, 512×512 pixels, 257 KB uniform) from Regional Police Traffic Directorate. Each file underwent 30 repeated trials with warm-up runs. Testing encompassed performance metrics (execution time, CPU usage, memory consumption), security evaluation (avalanche effect on 100 samples, collision detection), and grouping analysis by finger type using ANOVA (α=0.05). SHA-256 exhibited mean execution time of 2.28 ms, 48% slower than MD5's 1.54 ms (p<0.001), with CPU usage of 1.24% versus 0.98%, while memory consumption remained negligible. Avalanche effect approached ideal 50%: MD5 49.98%±4.43%, SHA-256 49.62%±3.21% (superior consistency). Zero collisions detected in 1,000 files. Grouping analysis revealed statistically significant differences between finger types (p<0.05) but with small effect size (η²<0.05) and negligible magnitude (<0.1 ms). For operational systems, SHA-256 is recommended based on acceptable performance overhead (<1 ms per file, 7.4 seconds daily for 10,000 transactions), superior security (no known attacks), regulatory compliance (NIST/ISO), more stable avalanche effect, and future-proofing capability.
Labuan Bajo as super-priority destinations experience improvement visit in a number of year lastly, however quality service Not yet fully fulfil expectation tourists . Study This analyze perception traveler through Aspect-Based Sentiment Analysis (ABSA) approach using the IndoBERT model. A total of 2,564 reviews multilingual from Google Maps and TripAdvisor processed through translation, pre-processing, extraction aspects, sentiment labels automatic, and model training. Four aspects analyzed based on framework SERVQUAL theory and Tourism Destination Quality: attractions, amenities, accessibility, and price. Model evaluation was conducted using precision, recall, and F1-score per aspect. The results show performance best The amenity and attraction aspects obtained the highest and most consistent scores across all metrics (around 0.83–0.88), indicating that reviews for these two aspects were more explicit and easily mapped by the model. In contrast, the access and price aspects showed lower scores (around 0.65–0.72), indicating linguistic challenges such as implicit aspects, variations in the context of the travel experience, and figurative complaints . The study This give recommendation policy connected data based direct with model findings. Limitations such as translation noise, biased datasets dominated by review positive-neutral, and not existence baseline comparison also discussed. These results confirm that ABSA approach can help stakeholder’s policy, however Still need improvement through other models such as IndoBERTweet, mBERT, or IndoBART.
Our study, Analysis of the impact of lateral Transfers in a stock distribution system with a central warehouse and two stocking points, aims to analyze the effet of lateral stock transfers between the two stocking points on minimizing the total inventory management cost system, retailers manage their inventories according to the (R,S) policy. This study also examines the service level and the stockout rate resulting from the implementation of lateral stock transfers. Each point i (i=1,2) manages its inventory independently in order to meet the consomer demand yi. Each stocking point has a maximun inventory level Si , when customer demand is less than or equal to the reorder point si , an order of quantity Qi= Si-si is placed with the central warehouse. This quantity is delivered after a known lead time Li. If the delivery lead time is too long, stocking point i may request a lateral transfer of quantity Xji from stocking point j, which has excess inventory, in order to avoid a stockout. The originality of this publication stems from the implementation of a numerical application using MATLAB, which allowed us to conduct this analysis.
Phishing attacks are among the most common and dangerous cyber security threats, as they exploit manipulation techniques to steal sensitive user information. This research focuses on leveraging the Random Forest algorithm to identify anomalies caused by phishing attacks in computer network environments. Random Forest was selected for its superior classification performance and its capability to handle a wide variety of data types with minimal over fitting. The experimental dataset consists of captured network traffic, containing both benign activities and malicious events labeled as phishing. The data underwent pre-processing, feature selection, and model training using Random Forest. The experimental results show that the model achieved 98% accuracy, with precision 98%, recall 98%, and F1-score 98%. This study also reveals that URL features such as the percentage of external links redirecting back to the original domain, frequent domain name mismatches, the number of hyphens (-) in the URL, and the presence of data submission via email are relevant and effective in distinguishing phishing from non-phishing URLs. These findings confirm that Random Forest can serve as an effective method for identifying phishing attacks based on URL characteristics.
In the era of digital transformation, the application of data mining in academic data management has become an important requirement for improving the quality of education. One crucial aspect is English proficiency. One of the tools for measuring English proficiency is the Test of English as a Foreign Language (TOEFL) Prediction test, which is administered at every university, including the State Polytechnic of Lhokseumawe. The management of TOEFL Prediction scores can utilize data mining as a basis for more in-depth learning analysis, as well as evaluation material. This study aims to design and develop a model for grouping the TOEFL scores of students at State Polytechnic of Lhokseumawe by applying the Fuzzy C-Means (FCM) algorithm. The research methods included observation and interviews, data collection and pre-processing, cluster model design, web-based system development, and system testing. Evaluation was conducted through Black Box and White Box testing for the system, as well as cluster quality validation using the Xie-Beni Index (XB) and Partition Coefficient. The results showed that the pre-test dataset of first-year students (651 data) produced three clusters with an XB value of 0.623, while the dataset of final-year students (826 data) produced six clusters with an XB value of 0.181. The developed model proved to be able to map students' English language abilities in a more structured manner and could be used as a basis for academic planning and skill improvement.
