Face recognition (FR) is a widely used biometric technology. Nevertheless, achieving efficient and robust FR is still challenging due to variations in illumination, pose, and facial expression. A vital step in any FR system is to select the most informative features and eliminate the redundant ones. In this study, a hybrid approach combining Principal Component Analysis (PCA) and the Dolphin Swarm Algorithm (DSA) with Euclidean Distance as a lightweight classifier is proposed. Experiments were made by using the ORL dataset, which consists of 400 grayscale images. With a 98% recognition rate for this hybrid approach against a recognition rate of 90–92% that can be achieved by PCA only, the proposed PCA+DSA outperformed standalone PCA while still being computationally economical. The metrics of Recognition Rate, Receiver Operating Characteristic (ROC), Cumulative Match Curve (CMC), and Expected Performance Curve (EPC) provided numerous confirmations for this Hybrid model. Additionally, the convergence analysis corroborated DSA’s efficacy in feature selection as the fitness was nearly 92% after nine iterations. Without requiring sophisticated classifiers or deep learning models, our findings show that the identification rate can be improved by combining a bio-inspired optimization technique and the classical PCA method. However, the current study is limited to the ORL dataset in a controlled environment. Future research will focus on implementing and evaluating the system in real-time scenarios on larger and more diverse datasets to enhance its scalability and robustness in practical applications.

Lithium-ion battery demand continues to rise as consumer electronics, hybrid and electric vehicles, and other technologies evolve. This means that numerous lithium-ion batteries are likely to be disposed of, leading to serious disposal problems and negative impacts on the environment and energy conservation. The commonly used lithium-ion battery recycling methods, which are chemical and mechanical, pose challenges, such as some batteries exploding, thermal runaway, or fire. Classification of used lithium-ion battery waste is required to be efficient and reliable. The purpose of this study was to develop a deep learning-based classification model of lithium-ion battery components for automated recycling. A dataset containing images of end-of-life lithium-ion battery components was collected from selected recycling centres in Nairobi, Kenya, and Kaggle.com. The images from selected fields were annotated using the Labelimg annotation tool. The dataset was split into three sets: 70% as the training set, 15% as the validation set, and 15% testing set. A Yolov8n model was then trained using the training set to detect and classify end-of-life lithium-ion battery components. The performance of the model was evaluated using the validation set and test set. The final trained model attained 0.903 precision, 0.792 recall, 0.852 mAP@0.5, 0.724 mAP@0.5-0.95, and 0.844 f1-score. The results from this research study could pave the way for innovative battery recycling measures, guaranteeing that valuable resources are reclaimed and toxic battery waste is managed efficiently and safely.

Brain tumor detection using deep learning has become a focus of research due to its potential to improve patient diagnosis and management. This study presents a comparative analysis to evaluate how the fundamental design philosophies of convolutional neural network (CNN) architectures, namely sequential (VGG16), residual (ResNet50), and encoder-decoder (U-Net), influence performance on brain tumor classification tasks using a public MRI dataset. The data undergoes preprocessing, including normalization, data augmentation, and division into training and testing subsets. Evaluation is conducted using accuracy, precision, recall, and F1-Score metrics. The results show that ResNet50 consistently outperforms other architectures with an average accuracy of 95.35%, followed by VGG16 (93.93%). Conversely, U-Net, designed for segmentation, demonstrates the lowest performance (79%). The superiority of ResNet50 highlights the benefits of residual connections in addressing the vanishing gradient problem for classification tasks. The poor performance of U-Net confirms the hypothesis that architectural suitability for a specific task is more critical than complexity alone. This study highlights the strength of ResNet50 as a reliable approach for brain tumor classification. It underscores the importance of selecting an architecture specifically designed for the intended medical image analysis task. In addition, an analysis of model complexity and computational efficiency was conducted, showing that while ResNet50 achieved the highest accuracy, VGG16 provided a more favorable trade-off between performance and training time.

The proliferation of fake news across digital platforms has raised critical concerns about information reliability. A notable example is the viral rumour falsely claiming that the Nigerian Minister of the Federal Capital Territory, Nyesom Wike, had collapsed at an event and was rushed to an undisclosed hospital an entirely fabricated claim that caused public confusion. While both traditional machine learning and deep learning approaches have been explored for automated fake news detection, many existing models have been limited to topic-specific datasets and often suffer from overfitting, especially on smaller datasets like ISOT. This study addresses these challenges by proposing a standalone Bidirectional Long Short-Term Memory (BiLSTM) model for fake news classification using the ISOT dataset. Unlike multi-modal frameworks such as the MM-FND model by state-of-the-art model, which achieved 96.3% accuracy, the proposed BiLSTM model achieved superior results with 98.98% accuracy, 98.22% precision, 99.65% recall, and a 98.93% F1-score. The model demonstrated balanced classification across both fake and real news and exhibited strong generalization capabilities. However, training and validation performance plots revealed signs of overfitting after epoch 2, suggesting the need for regularization in future work. This study contributes to the growing body of research on fake news detection by showcasing the efficacy of a focused, sequential deep learning model over more complex architectures, offering a practical, scalable, and robust solution to misinformation detection

Credit card fraud detection (CCFD) remains a critical research domain due to the dynamic, adversarial, and highly imbalanced nature of fraudulent activities in financial systems. This study employs a systematic mapping review guided by the PRISMA 2020 guidelines. It analytically synthesizes 40 peer-reviewed and open-access studies, focusing on methodological trends, machine learning techniques, datasets, optimization strategies, and evaluation metrics. Supervised learning (SL) models, including Random Forest, Decision Trees, Support Vector Machine (SVM), and XGBoost, accounted for nearly half of the reviewed studies and consistently demonstrated strong performance. Deep learning (DL) frameworks, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and their variants, have demonstrated strong capabilities in capturing sequential and high-dimensional patterns of fraud. However, their effectiveness is constrained by class imbalance and dataset bias. Ensemble and hybrid models further enhanced predictive accuracy but introduced higher computational costs and lower interpretability. A key finding is the heavy reliance on the ECCT 2013 dataset (used in more than half of the reviewed studies), which supports reproducibility but limits generalizability to modern fraud contexts. Optimization strategies, such as the Synthetic Minority Oversampling Technique (SMOTE), hyperparameter tuning, and dimensionality reduction, have proven effective in improving recall and reducing false negatives; however, they have been inconsistently applied. Similarly, evaluation metrics were uneven, with accuracy dominating (reported in 75% of studies), while more informative measures such as recall, F1-score, Precision-Recall curves (AUPRC), and Matthews Correlation Coefficient (MCC) received less emphasis despite their relevance to imbalanced data. Overall, while many models achieve high accuracy in controlled environments, their scalability, adaptability, and trustworthiness in real-world deployment remain limited. Future research should prioritize cross-dataset evaluations, standardized metrics, and emerging paradigms such as federated learning, self-supervised approaches, and explainable AI to guide the development of robust and deployable fraud detection systems.

Nighttime traffic accidents caused by driver fatigue remain a critical issue as most visual-based detection systems find it challenging to interpret facial cues under poor lighting conditions. Key obstacles include decreased accuracy in dark settings, difficulties in detecting eye and mouth features, and the impractical nature of real-time approaches that rely on physiological sensors. This study introduces a vision-based drowsiness detection framework that integrates the Adaptive Low-light Image Enhancement (LIME) method with a Support Vector Machine (SVM) classifier employing an RBF kernel. The dataset comprises 11.566 images of eyes and mouths, which are analyzed to extract features like Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), and blink frequency. Evaluation results show that the SVM model with the RBF kernel attained 90.94% accuracy, 91.22% precision, and 91.82% recall. This system is effective in detecting drowsiness under low-light conditions and has the potential to be implemented as an early warning feature in vehicles.
 
Keywords: Drowsiness Detection, SVM, EAR, MAR, Adaptive LIME

Dalam upaya meningkatkan interaksi manusia-mesin, penelitian deteksi sentimen sudah banyak dilakukan peneliti untuk tujuan tersebut. Seiring dengan berkembangnya Mesin Pembelajaran, penelitian ini akan membandingkan kemampuan empat model klasifikasi : CNN, CRNN, SVM, dan MLP—dalam mengidentifikasi sentimen berbasis gambar Mel-spectrogram. Penelitian ini memanfaatkan representasi Mel-Spectrogram dari 640 sampel image ( gambar ) spektrogram yang mencakup delapan kelompok kelas sentimen berbeda. Setelah melalui tahap praproses data gambar dan ekstraksi fitur, kinerja model dievaluasi menggunakan validasi silang 10-fold serta metrik akurasi, presisi, recall, dan F1-score. CNN dan CRNN mencapai akurasi tertinggi (100%), sedangkan SVM dan MLP mencapai 99,22%. Dari sisi waktu pelatihan, SVM membutuhkan waktu paking sedikit, yaitu sebesar 0,45 detik. Penelitian ini bertujuan untuk mengetahui efektivitas pendekatan image (gambar) Mel-Spectrogram dan menegaskan perlunya pertimbangan trade-off antara akurasi tinggi dan efisiensi komputasi dalam pemilihan model.
Kata Kunci – Analisis, Mel-Spectogram, Sentimen, Waktu Proses

Kanker merupakan penyakit  yang ditandai pertumbuhan sel tubuh tidak terkendali, cepat menyebar dan menyerang organ tubuh lainnya, kasusnya meningkat pesat di Indonesia. Tujuan penelitian ini menganalisis perbedaan tingkat fatigue sebelum dan sesudah diberikan terapi musik terhadap tingkat fatigue pada penderita kanker payudara di Rumah Singgah Kanker Samarinda. Penelitian ini menggunakan metode kuantitatif pre-eksperimen dengan rancangan penelitian pre test  post test one group design. Populasi yang dipilih dalam penelitian ini adalah wanita yang menderita kanker payudara di Rumah Singgah Kanker Samarinda, dengan sampel 19 responden melalui teknik total sampling. Kriteria inklusi adalah klien kanker usia 18 tahun, kooperatif dan dapat berkomunikasi dengan baik, mengalami. Variabel independen adalah terapi musik dan variabel dependen adalah tingkat fatigue. Instrumen adalah kuesioner FACIT-Fatigue yang sudah teruji validitas (0,88) dan reliabilitas(0,646). Analisa data menggunakan uji paired t test. Hasil penelitian menunjukkan perbedaan efektivitas pada hari pertama sebelum diberikan terapi musik hingga hari ketujuh pemberian terapi musik (p value 0,004). Kesimpulan terdapat pengaruh dalam pemberian terapi musik klasik dan suara alam pada tingkat fatigue pasien kanker payudara. Saran untuk penelitian selanjutnya bisa mengembangkan terapi lain yang mampu mengatasi dan menurunkan tingkat fatigue pada pasien kanker payudara

Pneumonia adalah infeksi komorbid sering terjadi pada individu, menyerang saluran pernapasan berdampak pada masalah keperawatan bersihan jalan nafas, gangguan pertukaran gas. Pneumonia tidak hanya menjadi masalah kesehatan pada anak, tetapi juga merupakan penyebab utama morbiditas dan mortalitas pada dewasa. Penelitian bertujuan untuk mengidentifikasi asuhan keperawatan  pasien dengan pneumonia berupa pengkajian, diagnosa, intervensi, dan evaluasi, dengan masalah keperawatan gangguan pertukaran gas di RSUD Bangil Kabupaten Pasuruan. Merupakan penelitian studi kasus, dengan pengumpulan data melalui kegiatan wawancara, observasi, dan perawatan.Jumlah sampel  sebanyak 6 orang pneumonia yang mengalami masalah keperawatan, gangguan pertukaran gas, pola pernafasan yang tidak efektif. Intervensi keperawatan  mencakup pemantauan pernapasan dan manajemen saluran napas. Analisa data berdasarkan SDKI, implementasi dilakukan dengan panduan SIKI, implementasi (SIKI), evaluasi keperawatan dilakukan berdasarkan standar luaran yang dikeluarkan oleh PPNI (SLKI). Hasil evaluasi akhir didapatkan permasalahan keperawatan dapat teratasi sebagian. Direkomendasikan peneliti selanjutnya untuk melakukan studi serupa dengan  menekankan teknik terapeutik berupa posisi semi fowler berbasis EBN sehingga dapat mengatasi masalah keperawatan

Ibu hamil risiko tinggi beranggapan dengan kondisinya berisiko tinggi, maka janinnya akan mengalami kecacatan bahkan kematian, sehingga mengakibatkan kecemasan. Kehamilan risiko tinggi juga mempengaruhi keteraturan Antenatal Care (ANC). Faktor lain yang mempengaruhi adalah dukungan keluarga. Tujuan penelitian adalah untuk mengetahui hubungan dukungan keluarga dengan kecemasan dan keteraturan ANC pada ibu hamil risiko tinggi. Penelitian ini menggunakan metode korelasi analitik dengan pendekatan cross sectional. Populasi 65 ibu hamil risiko tinggi di wilayah kerja Puskesmas Tikung Bulan Januari-Februari 2025, didapatkan 34 sampel dengan teknik cluster sampling. Data penelitian menggunakan kuesioner dukungan keluarga dan kecemasan, serta observasi ANC  pada buku kesehatan ibu dan anak. Data di analisis menggunakan uji rank spearman.  Hasil penelitian menunjukkan 67,6% memiliki dukungan keluarga baik, sebanyak 55,9% tidak cemas, dan 70,6% teratur dalam ANC; ada hubungan dukungan keluarga dengan kecemasan pada ibu hamil risiko tinggi ( p value 0,006); ada hubungan dukungan keluarga dengan keteraturan ANC  pada ibu hamil risiko tinggi (p value 0,000) . Untuk penelitian selanjutnya mengkorelasikan faktor yang lainnya seperti peran tenaga kesehatan, sampel diperbanyak, serta meluaskan lokasi penelitian (kabupaten).