Patriot Intan Abadi merupakan salah satu industri di bidang peternakan ialah suatu bisnis usaha yang bergerak di bidang peternakan ayam petelur. Tiap karyawan di PT Patriot Intan Abadi ini hendak diberikan pendapatan atas hasil kerjanya tiap akhir bulan. Sistem penggajian di industri ini masih memakai metode manual. Sistem manual mempunyai keterbatasan semacam lamanya waktu perhitungan pendapatan serta ada efek kesalahan perhitungan pendapatan yang hendak diberikan kepada karyawan yang menyebabkan merugikan ataupun menguntungkan salah satu pihak. Dengan kasus tersebut hingga riset ini hendak menciptakan sistem informasi penggajian dengan berbasis website, sebab dengan sistem informasi berbasis website hendak bisa kurangi kecurangan- kecurangan yang sepanjang ini terjalin dalam sistem penggajian. Pada sistem data informasi karyawan, informasi jabatan& informasi penggajian, penulis memakai diagram arus informasi ERD, serta laporan dengan memakai pemograman php serta mysql, dapat memudahkan serta memesatkan proses pengolahan informasi penggajian, serta berikutnya sistem yang terbuat diuji dengan pengujian Blackbox Testing pada tiap menu ada 7 percobaan yang berperan dengan baik serta cocok dengan hasil yang diharapkan.

Indonesian people, especially the younger generation, often overlook the great potential of herbal leaves that are easily found around their homes. These leaves not only offer health benefits but also hold significant economic value. This research developed a system to classify 10 types of herbal leaves (Annona muricata, Anredera cordifolia, Piper betle, Ocimum basilicum, Peperomia pellucida, Psidium guajava, Isotoma longiflora, Coleus scutellarioides, Ageratum conyzoides, and Syzygium polyanthum) using artificial intelligence (AI). The study employed the Convolutional Neural Network (CNN) method and the You Only Look Once (YOLO) v11 algorithm, focusing on evaluating the performance of YOLOv11 in three variants, Nano, Small, and Medium. The results showed that the YOLOv11 Medium variant achieved the best performance, with the highest mAP50-95 value of 0.743 and mAP50 of 0.974 at the last epoch. The YOLOv11 Small variant outperformed Nano in precision (0.947 vs. 0.933) and mAP50 (0.973 vs. 0.972), while YOLOv11 Nano had slightly higher recall (0.921 vs. 0.906). Confusion Matrix results for YOLOv11 Medium showed precision (P) = 0.932, recall (R) = 0.928, mAP50 = 0.974, and mAP50-95 = 0.743. Based on these metrics, YOLOv11 Medium stood out as the best-performing variant, followed by Small and Nano. This research highlights the potential of AI technology to enhance the utilization of herbal leaves, which can provide broader health benefits and support the local economy.

Bank transaction fraud detection has emerged as an important area of research in the economic sector, driven by the developing sophistication of fraudulent activities and the considerable economic losses they entail. This paper reviews numerous methodologies and technologies employed in the real-time identification and mitigation of fraudulent transactions, including traditional statistical techniques, machine learning algorithms and advanced artificial intelligence strategies. It enhances the need to combine anomaly detection structures with behavioral analytics to enhance detection accuracy while addressing challenges like data privacy, the need to balance false positives and negatives and the need for adaptive systems. By evaluating the most recent developments and case studies, this study provides a comprehensive assessment of what is happening in bank transaction fraud detection and presents future directions for enhancing safety features.

Malware detection has become a critical area of research due to the increasing sophistication of cyberattacks targeting various platforms, including IoT devices, Android systems, and desktop environments. This study employed the systematic literature review (SLR) method, following PRISMA guidelines, to analyze recent advancements in malware detection using machine learning (ML) models. A total of six studies were selected based on strict inclusion and exclusion criteria, focusing on algorithms, datasets, performance metrics, and targeted platforms. The review reveals that ensemble methods like Gradient Boosting and XGBoost achieve high detection accuracy, with several models exceeding 90% on benchmark datasets such as VirusShare and MSCAD. Additionally, IoT platforms emerged as the most commonly targeted environment in malware detection research, emphasizing their vulnerability. Despite these advancements, the review identifies gaps in dataset diversity and platform-specific optimizations. This study provides insights into the current trends, challenges, and future directions for machine learning-based malware detection.

Migraine is a common neurological illness that has a major influence on the quality of life; yet, precise categorization and prediction remain difficult because of its complicated symptoms and multiple triggers. This work investigates the use of advanced machine learning (ML) algorithms to improve migraine diagnosis and prediction, drawing on a large dataset that includes clinical, lifestyle, and environmental aspects. Various machine learning models, such as ensemble methods, deep learning, and hybrid approaches, are tested to see how well they discriminate migraine from other headache conditions and predict migraine episodes. Feature selection approaches are used to identify the most important predictors, which improve model interpretability and performance. Experimental results show that the proposed machine learning framework outperforms established diagnostic methods in terms of classification accuracy, sensitivity, and specificity. The study demonstrates how ML-driven solutions may be used to manage migraines in a tailored way, helping medical practitioners with early diagnosis and intervention techniques. My suggested framework, NeuroVote(ensemble model), offers a remarkable 99.99% classification accuracy for migraines. Future studies will concentrate on optimizing models for clinical deployment and incorporating real-time data from wearable technology.

Brain stroke is a medical condition where the disruption or interference of blood supply causes damage to blood cells. As a result, the effective area loses the abilities and tasks it performs. It usually affects people over the age of 25 and under 70. This research paper predicts the chances of brain stroke before it happens to save the lives of people. The dataset used in this research paper, found on the Kaggle website, has 4981 samples and 11 different features, or risk factors, on which the basis of this medical condition or event occurs. This study uses different supervised machine learning algorithms: Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, and K-Nearest Neighbors (KNN). We obtained the best results from Random Forest during research, which is 94.3%.