Gina Purnama Insany

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%.

Indonesia boasts diverse tropical fruits like ciplukan, harendong, and kecapi, which are nutrient-rich but underutilized. To address this, an automated fruit recognition system was developed using Convolutional Neural Network (CNN) with MobileNet architecture, leveraging Depthwise Separable Convolution (DSC) for efficiency. The model was trained on 5000 images of five local fruits (224 × 224 pixels), split into 70% training, 20% validation, and 10% testing. Optimized for Android, the system enables real-time identification with minimal hardware requirements (e.g., 2 GB RAM for low-end devices). Evaluation metrics (accuracy, precision, recall) achieved 97.43% accuracy, demonstrating MobileNet’s effectiveness. This study highlights deep learning’s potential in preserving and promoting Indonesia’s indigenous fruits.

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.

Heart disease is the leading cause of death across the world. However, such an early prediction of heart attacks can save lives if clinical data are used to predict it accurately. For this, we use four machine learning models: Naive Bayes, Decision Tree, Random Forest and K-Nearest Neighbors (KNN) to predict heart attacks from the data of the patients. Models developed achieved an average accuracy of 65.08%; however, this paper explores the performance of these models in real world healthcare applications. Our focus is on improving model performance by improving the quality of the data, the features and hyperparameter tuning. Future directions indicate combining deep learning techniques and larger dataset for more accurate prediction.

Non-player characters (NPCs) play a crucial role in creating engaging and immersive experiences in role playing games (RPGs). Traditional NPC interactions often rely on scripted dialogues, which can limit their ability to adapt dynamically to player input. This study presents a novel framework that enhances NPC interactions by integrating advanced conversational systems. Utilizing Open AI’s natural language processing capabilities, RPG Maker MZ as the game development platform, and JavaScript for customization, the framework introduces context-aware dialogues that respond intelligently to player queries and actions. By bridging the gap between game AI and conversational systems, this approach enables more lifelike and meaningful NPC behavior. Experimental results indicate that the proposed system significantly improves the narrative depth and overall player experience. These findings demonstrate the potential of combining AI-driven chatbots with game development tools to redefine the role of NPCs in modern gaming.

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.

In the digital era, managing archival data poses challenges for many institutions, including Badan Pusat Statistik (BPS) of Sukabumi Regency, especially when dealing with unstructured PDF documents. This study develops a data pipeline by effectively integrating Optical Character Recognition (OCR) technology with Extract, Transform, Load (ETL) processes. Unstructured data from financial disbursement documents, such as SPM and SP2D, were automatically extracted with high accuracy, achieving an average of 98.52% for SPM using a combination of OCR and PDFPlumber, and 100% for SP2D extracted using PDFPlumber. Extraction results were stored in a data warehouse, then transformed using Apache Spark and loaded into data marts. ETL process was automated using Apache Airflow, which operated reliably according to dependencies. The processed data were presented through an interactive Looker Studio dashboard in real-time, supporting efficient archive management and more informed decision-making. This study not only provides a solution to existing archival management problems but also opens opportunities for further development in the application of big data technologies and business process automation in public sector.

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.

Kebutuhan akan produk perawatan kulit yang aman dan efektif semakin meningkat, terutama bagi individu yang rentan terhadap infeksi jamur Malassezia, penyebab fungal acne (Malassezia folliculitis). Penelitian ini merupakan studi pengembangan yang bertujuan untuk merancang sistem pakar berbasis web yang mampu menganalisis kandungan produk perawatan kulit guna menilai kesesuaiannya bagi kulit yang rentan terhadap fungal acne, dengan menerapkan metode forward chaining. Data dikumpulkan melalui studi literatur dari jurnal ilmiah dan sumber kedokteran terpercaya. Sistem yang dikembangkan terdiri dari tiga komponen utama: basis pengetahuan, mesin inferensi, dan antarmuka pengguna. Hasil uji coba menunjukkan bahwa algoritma forward chaining yang digunakan telah mampu mengidentifikasi bahan fungal acne-safe dan non-fungal acne-safe secara fungsional. Sistem ini diharapkan dapat membantu pengguna dalam memilih produk perawatan kulit yang aman, serta mendukung manajemen Malassezia folliculitis secara lebih tepat.

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.