The rapid development of the internet has been accompanied by an increase in threats posed by intruders, including DDoS attacks, backdoors, exploits, and worms, among others. To address these attacks, accurate techniques for classifying attacks are necessary. Attack classification aims to identify detected threat activities, which can then be analysed using network forensics. This study identifies threats based on network data collected in a dataset known as UNSW-NB15. The quality of the UNSW-NB15 dataset is improved through the use of pre-processing techniques, including feature encoding and normalization. The ensemble feature selection technique is used to obtain the best features based on the highest ranking of the dataset attribute features. Furthermore, threat anomaly detection is performed using machine learning to classify threats using hybrid classification algorithms, namely Naïve Bayes, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbourhood (K-NN), Neural Network (NN), and C.45. The threat classification results using the Naïve Bayes algorithm show that the accuracy measurement reaches 0.9848. The precision, recall, and F1 score measurements reach 0.9847. Threat classification using Random Forest reaches 0.9626, and the F1 score reaches 0.9624. Threat classification using the Neural Network algorithm achieved an accuracy of 0.9463, a precision of 0.946, a recall of 0.939, and an F1 score of 0.93.
This study aimed to analyze the reliability of the ACM, identify the main failure modes, compare the application of Reliability-Centered Maintenance (RCM) and Total Productive Maintenance (TPM) methods, and develop a risk management model based on Failure Mode, Effects, and Criticality Analysis (FMECA). The study employed a descriptive quantitative approach using a case study of the ACM P/N 782630-5 installed on the CN235-220 Maritime Patrol Aircraft (MPA) operated by Air Squadron 800, Pusat Penerbangan TNI Angkatan Laut (Puspenerbal TNI AL). The data consisted of historical maintenance records from 2016–2025, which were analyzed using the parameters of Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR), availability, Overall Equipment Effectiveness (OEE), and FMECA. The results showed 21 malfunction incidents with a total downtime of 283 hours, an MTBF value of 190.48 hours, an MTTR value of 13.48 hours, and a failure rate of 0.00525 failures per hour. The components with the highest criticality levels were the bearings, rotors, and turbine wheels. Reliability analysis indicated that the recommended preventive maintenance interval was every 68–70 operating hours. The TPM analysis produced an availability score of 92.93%, performance efficiency of 96.69%, quality rate of 97.52%, and OEE value of 87.63%, indicating that maintenance effectiveness was categorized as good. The recommended maintenance strategy combined Condition-Based Maintenance, Scheduled Maintenance, Autonomous Maintenance, and Focused Improvement, integrated with risk management to improve ACM reliability and enhance aircraft operational readiness.
Silver nanoparticles (AgNPs) have attracted considerable attention due to their exceptional physicochemical properties and broad-spectrum antibacterial activity. This study reports the first application of Ipomoea tricolor (morning glory) leaf extract distinguished by its uniquely high alkaloid (ergine, isoergine) and flavonoid (quercetin, rutin, kaempferol) content as a bifunctional bioreductant and capping agent for eco-friendly AgNP synthesis. Compared to commonly used plant sources, I. tricolor provides a rare combination of electron-rich phytochemicals that yield exceptionally small (18.4 ± 3.2 nm), highly stable (zeta potential: −32.4 mV, PDI: 0.214), and potently antibacterial nanoparticles without requiring additional stabilizers. Comprehensive characterization via UV-Vis (SPR at 425 nm), FTIR, XRD (FCC structure, 16.7 nm crystallite), TEM, and DLS confirmed nanoparticle formation and phytochemical capping. Antibacterial evaluation against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Streptococcus mutans demonstrated inhibition zones of 9.6–22.3 mm and MIC values of 6.25–25 µg/mL superior to AgNPs from most previously reported plant sources. These results establish I. tricolor-mediated AgNPs as promising sustainable candidates for biomedical applications, particularly in addressing antimicrobial resistance.
This study examines the effect of logistics service quality (LSQ) on customer satisfaction and repurchase intention in e-commerce, focusing on the motorcycle spare parts market. Given the projected growth of e-commerce, LSQ is critical for competitive differentiation. Despite technological advancements, issues like extended transit times, customs delays, and inconsistent service standards often lead to customer dissatisfaction, impacting repurchase intentions. Thus, enhancing overall logistics service quality requires further investigation. A quantitative approach using Structural Equation Modeling (SEM) with AMOS software was utilized to analyze the relationships among web design, return logistics service, responsiveness, information quality, overall LSQ, customer satisfaction, and repurchase intention. Data was collected via online questionnaires from 112 respondents, primarily male (82
