Ilmu Pengetahuan Alam (IPA), khususnya dalam Fisika, memiliki peran vital dalam pengembangan Ilmu Pengetahuan dan Teknologi (IPTEK). Pembelajaran IPA memerlukan kemampuan berpikir, berargumen, beranalisis, dan sikap ilmiah untuk menerapkan pengetahuan sebagai solusi terhadap masalah sehari-hari. Konsep fisika sering kali sangat abstrak, seperti Hukum Newton, yang sulit diobservasi langsung. Untuk memahami penerapan Hukum Newton, diperlukan praktikum agar siswa memahami materi lebih mendalam. Namun, kebanyakan sumber pembelajaran saat ini lebih cenderung menggunakan buku. Penelitian ini mengatasi tantangan tersebut dengan mengembangkan aplikasi media pembelajaran fisika berbasis Augmented Reality (AR) melalui metode Multimedia Development Life Cycle (MDLC). Aplikasi ini menciptakan antarmuka menarik dengan animasi 3D visualisasi Hukum Newton dan memanfaatkan AR untuk pengalaman belajar interaktif. Hasil uji kelayakan dengan siswa SMA menunjukkan respons positif dengan tingkat kelayakan sebesar 96,9%, membuktikan aplikasi ini sebagai alternatif efektif untuk meningkatkan pembelajaran fisika di tingkat SMA dengan teknologi terkini.

Unmanaged diabetes can result in a number of complications that need to be hospitalised. Diabetes is a chronic disorder. With preventive treatment, outcomes may be improved through early prediction of diabetes-related hospitalisation using data-driven algorithms. Here, we examine recent advances in deep learning methods for anticipating readmissions and unexpected hospital stays in adult patients with diabetes. Firstly, we present an overview of the main factors that indicate the need for hospitalisation due to diabetic complications. The research on hospitalisation risk prediction using structured health data, such as demographics, prescriptions, test results, etc., using conventional machine learning techniques is then summarised. Using data from insurance claims and electronic health records, we then examine current research that has used deep learning models. It is emphasised that longitudinal data can be included using recurrent neural networks. Model architectures, training methods, and important data modalities are covered. The assessment also addresses deployment difficulty and the model's performance assessment on real-world datasets. Ultimately, potential paths forward include hybrid models that integrate data diversity, explainable predictions, and clinical knowledge. In order to provide evidence-based predictions of the risk of hospitalisation and readmission for diabetes patients, we examine the potential and constraints of recently developed deep learning algorithms in this review.

In digitized world, digital documents are essential for information sharing. However, some organizations continue to place their reliance in traditional hard-copy formats concerning about the legitimacy of documents. This study presents an innovative approach to document verification with digital signatures, distributed databases, and QR codes. Using a two-step process for data integrity and document authentication, the study approach entails developing a Smart Document with a QR code and digital signature. For increased security and scalability, the system design distributes hash fragments among several databases using the hash split approach. The system's excellent performance, resistance to sluggish HTTP-based attacks, and effectiveness in document verification are highlighted by the results and debates. The report ends with recommendations for future improvements to strengthen the system's resilience, like implementing more secure database engines and enhancing fault tolerance. In conclusion, this method offers a viable way to verify documents in hardcopy and electronic formats in a secure and scalable manner.

Cryptocurrency price prediction is a challenging task due to the inherent volatility and complexity of the market. In this research, we propose a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network model for predicting Bitcoin prices. The model is implemented using the TensorFlow and Keras libraries and is evaluated on historical Bitcoin price data obtained from Yahoo Finance. Our approach aims to leverage the strengths of both LSTM and GRU architectures to enhance the accuracy of price predictions. The results suggest that the proposed hybrid LSTM-GRU model holds promise for effectively capturing the complex dynamics of cryptocurrency markets, addressing the challenges associated with traditional time-series analysis techniques.

This study introduces a hybridization of the Bird Mating Optimizer (BMO) with Differential Evolution (DE). The Bird Mating Optimizer exhibits certain limitations, such as a slow convergence rate and a tendency to become trapped in local optima. To address these issues, a new method, BMO-DE, is proposed to enhance the performance of the BMO swarm intelligence algorithm. BMO-DE is a versatile swarm intelligence algorithm applicable to various engineering problems. In this research, it is specifically employed in the optimization of welded beam design, a type of problem characterized by numerous constraints. The penalty function approach is used to handle the constraints associated with welded beam design. Comparative analysis indicates that the proposed BMO-DE method outperforms other swarm intelligence algorithms in tackling this category of problems. Notably, the method demonstrates efficacy in finding optimal solutions with a low number of objective function evaluations, making it a potent and promising approach for addressing such problems.

Human gait recognition as a branch of biometric identification, has witnessed remarkable progress in recent years, thanks to the integration of deep learning techniques. This paper presents a comprehensive review of the latest advancements in the field, specifically focusing on the transformative role of deep learning methodologies. Recent research papers highlight novel approaches in gait recognition, including deferent models proposed that is consisted of using more than one approach together to increase the accuracy. Subsequently, we undertake a comprehensive investigation of the most relevant literature and present an analysis of gait recognition techniques employing deep learning. We discuss the models, systems, accuracy, applications, and datasets utilized in these studies, aiming to outline and structure the research landscape and literature in this domain. Methods for acquiring gait data are distinguished between capturing video frame, radar signals, or from wearable sensors as well as from the available online datasets that are large-scale and significantly contributed to the advancement of deep learning models. The study also shows the verity applications that can utilize human gait recognition to achieve certain goals.

This review paper provides a comprehensive analysis of recent advancements in Facial Expression Recognition (FER) through various deep learning models. Seven state-of-the-art models are scrutinized, each offering unique contributions to the field. The MBCC-CNN model demonstrates improved recognition rates on diverse datasets, addressing the challenges of facial expression recognition through multiple branches and cross-connected convolutional neural networks. The Deep Graph Fusion model introduces a novel approach for predicting viewer expressions from videos, showcasing superior performance on the EEV database. Multimodal emotion recognition is explored in the EEG and facial expression fusion model, achieving high accuracy on the DEAP dataset. The Spark-based LDSP-TOP descriptor, coupled with a 1-D CNN and LSTM Autoencoder, excels in capturing temporal dynamics for facial expression understanding. Vision transformers for micro-expression recognition exhibit outstanding accuracy on datasets like CASMEI, CASME-II, and SAMM. Additionally, a hierarchical deep learning model is proposed for evaluating teaching states based on facial expressions. Lastly, a visionary transformer model achieves remarkable recognition accuracy of 100% on SAMM dataset, showcasing the potential of combining convolutional and transformer architectures. This review synthesizes key findings, highlights model performances, and outlines directions for future research in FER.

Semakin meningkatnya pengunjung website mengakibatkan web server menjadi sibuk merespons permintaan klien. Untuk mengatasi masalah kelebihan beban, penggunaan load balancing menjadi solusi yang efektif. Nginx merupakan aplikasi web server memiliki keunggulan sebagai perangkat lunak sumber terbuka, ringan, dan berkinerja tinggi sebagai HTTP dan reverse proxy. Dalam konteks ini, algoritma penjadwalan menjadi penting untuk menyeimbangkan beban Algoritma penjadwalan seperti Weighted Round Robin (WRR) dan Weighted Least Connection (WLC) menjadi pilihan yang relevan. Pengujian dilakukan dengan mengirimkan 1000, 2000, dan 5000 permintaan dengan bobot server 10:15:20, analisis throughput menunjukkan bahwa algoritma WLC lebih unggul daripada algoritma WRR. WLC mencapai waktu throughput terbaik dengan nilai 108.988 Kbits/sec. Parameter respon time menunjukkan bahwa algoritma WRR memiliki kinerja lebih baik, dengan waktu rata-rata respon sebesar 2.761ms. Untuk parameter packet loss, algoritma WRR juga menunjukkan hasil yang lebih baik, dengan rata-rata nilai packet loss sebesar 56%.

Tujuan dari penelitian ini untuk mengetahui faktor yang mempengaruhi minat berwirausaha di Indonesia dan China serta perbedaan yang dimiliki dan untuk mengetahui perbandingan atau komparasi pendidikan kewirausahaan antara Indonesia dan China. Metode yang digunakan dalam penelitian ini yaitu study literatur kemudian dianalisis dan ditarik kesmpulan. Hasil kajian literatur menunjukkan bahwa Pendekatan pendidikan kewirausahaan di kedua negara dapat disesuaikan dengan mempertimbangkan perbedaan yang ditemukan untuk memenuhi minat dan motivasi kewirausahaan mahasiswa masing-masing negara. Di China, fokus dapat diberikan pada membangun keyakinan mahasiswa untuk mengatasi rintangan, sementara di Indonesia, penekanan dapat diberikan pada memperkuat kepercayaan bahwa berwirausaha memberikan manfaat. Selain itu, penggunaan profil wirausaha sukses, terutama wirausaha muda, dapat menjadi motivator yang efektif di kedua negara. Indonesia maupun China telah mengambil langkah-langkah penting dalam mengembangkan pendidikan kewirausahaan. Meskipun keduanya menghadapi tantangan yang berbeda, baik Indonesia maupun China memiliki potensi besar untuk terus memajukan pendidikan kewirausahaan dan mendukung generasi muda dalam membangun bisnis yang inovatif dan berkelanjutan. Tumbuh dan berkembang pesatnya kewirausahaan di China dan diIndonesia tidak lepas dari peran teknologi informasi digital. Dunia digital menawarkan sumber daya baru yang sangat luas bagi wirausahawan untuk memanfaatkan peluang mempromosikan bisnis yang digelutinya. Mulai dari pengumpulan data terbuka, konten, kode dan layanan yang tumbuh secara eksponensial hingga kontribusi online penggunan dan komunitas di seluruh dunia.
 Keywords : Minat Berwirausaha di Indonesia dan China, Pendidikan Kewirausahaan di Indonesia dan China, Teknologi digital.

A survey of 108 agile practitioners revealed that user stories are the most widely used method for capturing requirements. However, user stories can be interpreted differently by different stakeholders, leading to potential misunderstandings within the development team. Additionally, the interconnectedness of user stories poses challenges during the requirement elicitation process. A Systematic Literature Review (SLR) of 27 articles about user story elicitation process were selected and these are examined to determine user story quality. This research will provide a comprehensive summary of user story elicitation approaches and their application in addressing user story quality issues. The study will also offer insights into selecting appropriate approaches for resolving challenges in user story requirement elicitation. Finally, most user story elicitation approach primarily focus on addressing the issue of ambiguity.