Implementation of East Javanese Local Culture in Graphic Design Elements in Students’ Final Projects: A Literature Review

The background of this research departs from the challenges of globalization that cause visual homogenization and erode local cultural identity. Graphic design is a strategic medium in bridging tradition with modernity, by adapting cultural elements such as batik motifs, Javanese script typography, to cultural icons Reog and Karapan Sapi. This study aims to analyze the integration of East Java local wisdom values in contemporary graphic design through a literature study approach and descriptive qualitative analysis. The research method was carried out by reviewing 215 articles selected using the PRISMA protocol until there were 15 relevant main sources. The results of the study show that there are four main trends in graphic design based on local wisdom, namely the symbolization of performance culture and language (30%), culinary branding and local products (29%), the revitalization of cultural narratives on digital platforms (22%), and the abstraction of traditional crafts into visual assets (18%). The value of the interconnectedness between keywords shows that graphic design is now strongly integrated with interactive technology, education, and the creative economy. In conclusion, the application of East Java's local wisdom in graphic design not only strengthens the region's visual identity but also opens up opportunities for sustainable creative economy innovation

An Innovation Approach for Feature Selection Medical Data Using Joint Fine-Tuning Fusion Graph Convolutional Network

This research addresses the challenge of feature selection in high dimensional medical datasets, where unnecessary or duplicated information can hide patterns and negatively impact model performance. The aim is to develop an efficient feature selection strategy using Fine-tuning Fusion Graph Convolutional Networks (GCNs) to enhance model accuracy and interpretability. The objectives include improving the medical data selection process, increasing generalization, and assisting healthcare professionals in making educated clinical decisions based on the most relevant factors. The study employs Joint Fine-Tuning Fusion Graph Convolutional Networks (GCNs) for feature selection in medical datasets. This approach entails creating several graphs to illustrate feature interrelations, amalgamating them into a cohesive representation, and optimizing the model to emphasize pertinent aspects. The L2-norm of the final embeddings dictates feature significance, directing the choice of the most critical features for enhanced predictive accuracy. The study's findings indicate that GCN-based feature selection improves classification accuracy, especially for the PIDD dataset, enhancing accuracy, precision, recall, and F1-score from 0.74 to 0.75. The Kidney Failure dataset exhibited near-perfect accuracy (0.99) prior to selection, whereas the heart disease dataset had a minor reduction in performance (from 0.81 to 0.80), highlighting the dataset-specific effects of feature selection. GCN-based feature selection improved classification performance, increasing the PIDD dataset's accuracy from 0.74 to 0.75, with no significant effect on the Kidney Failure dataset. Nonetheless, it somewhat diminished performance for the heart disease dataset. Subsequent study ought to enhance feature selection techniques by integrating dataset-specific optimizations and domain expertise to augment model precision and overall generalizability

Mobile 360° Panoramic Training for Commercial Kitchen Safety: Usability and Learning Outcomes

Commercial kitchens are high-risk workplaces where staff routinely face hazards such as slips, burns, lacerations, and chemical exposure. Conventional classroom-based safety training often suffers from low engagement and weak retention, limiting preparedness for dynamic, high-pressure conditions. To address this, the present study developed and evaluated a mobile 360° panoramic training platform to enhance hazard awareness in commercial kitchens. Unlike fully modeled virtual reality (VR) simulations or generic training contexts, the platform delivers authentic kitchen imagery in dual modes—immersive via Google Cardboard and non-immersive via smartphone—balancing realism, accessibility, and cost efficiency. This exploratory quantitative study involved thirty semester-one culinary students (ages 18–23) from Kolej Komuniti Bukit Beruang, Melaka, recruited through a convenience sampling approach. Participants completed pre- and post-training hazard-identification tests and the System Usability Scale (SUS). Usability ratings were consistently high across ease of use, learnability, efficiency, and satisfaction (means 4.27–4.70). Hazard-identification scores increased significantly from 29.33 to 83.67; a paired-samples t-test confirmed the improvement (p < 0.001, d = 3.46). Participant feedback highlighted realism and accessibility as strengths, though reduced interactivity compared to full VR was noted. Findings align with prior VR-based training studies in healthcare and construction, suggesting that panoramic imagery can deliver comparable learning gains at lower cost and deployment effort. Limitations include the small, short-term sample, absence of a control group, and user-reported issues such as headset discomfort and accessibility concerns. Future research should examine longitudinal retention, controlled comparisons with traditional training, and scalability across diverse settings to establish broader real-world impact

A Systematic Review of Machine Learning and Deep Learning Approaches in MRI-Based Brain Tumour Analysis, Detection and Classification

A brain tumour develops when abnormal cell growth happens in or near the brain. These tumours can grow slowly and not be cancerous, or they can grow quickly and spread, which is known as malignancy. Brain tumours put pressure on the surrounding brain tissues, causing symptoms like memory loss, migraines, movement dysfunction, and vision impairment. Brain tumours are often divided into two groups: primary tumours, which start in the brain, and secondary tumours, which are caused by cancers that spread to other regions of the body. Although brain tumours provide a significant medical challenge, patient outcomes have improved thanks to recent advancements in diagnostic and treatment methods. Because of its better soft-tissue contrast and noninvasive nature, magnetic resonance imaging (MRI) is one of the most important medical imaging modalities for the early identification and precise localization of brain tumours. Clinical practice also makes use of other imaging methods such as PET-CT and functional MRI (fMRI). Artificial intelligence and deep learning techniques have demonstrated significant promise in automated brain cancer analysis in recent years. These methods enable precise cancer diagnosis, classification, and segmentation by identifying intricate patterns from MRI data that are challenging to recognize through manual examination. A thorough study of current deep learning and machine learning techniques for MRI-based brain tumour analysis is provided in this paper. The current thorough literature search includes papers released between 2019 and 2024. 67 pertinent articles are chosen for in-depth analysis after predetermined inclusion and exclusion criteria is used. Many of these studies make use of publicly accessible datasets like Figshare, TCIA, and BraTS. The results show that deep learning models frequently outperform traditional machine learning methods in terms of accuracy and robustness, especially convolutional neural network-based designs. However, there are still issues with clinical generalisation, model interpretability, and data heterogeneity

Legal and Public Health Governance for Sustainable Integration of Mobile Health (mHealth) Technologies in East Africa

Mobile health (mHealth), which comprises mobile health applications, telemedicine, SMS-based treatments, and wearable health monitors, has the power to change healthcare delivery, but at the same-time, it is going through a rapid developmental phase that regulators cannot keep up with. This is considered a necessity in balancing the Integration of mHealth technology innovation through enhanced laws within East Africa. It is in view of this that this examines the legal and public health framework in integrating mHealth technology in enhancing the healthcare system within East Africa. The study adopts a doctrinal and systematic analytical method of study directed by the PRISMA framework, allowing thorough legal analysis while at the same time guaranteeing a transparent, stringent, and comprehensive review of related literature. The study found that fragmentation of laws, lack of centralized public health and data governance, unequal access to mHealth services, and constraints on innovation, weakens the integration and regulation of mHealth. Hence, the study recommends and concludes that for effective integration of mHealth in enhancing the public health care system, the research insists on a unified legal system that states unambiguously which data protection benchmarks apply, what the liability conditions are, what the integration of different systems and regulations requirements is, and how to coordinate among different countries' regulators. Besides that, it suggests measures for strengthening the capacity of the targeted groups, such as: medical professionals, trainees, users’ digital literacy campaigns, and local mHealth technology developers’ institutions’ support

Implementation of an Automatic Controlled Power Factor Correction System Utilizing Low-Cost Modules

This paper presents the design and implementation of a PIC microcontroller-based power factor correction system using a stepped capacitor bank and low-cost analog measurement modules. The proposed design aimed to address the low power factor issue caused by inductive loads that intern increases the current, losses, and apparent power demand. The developed PIC-based controller integrated analog conditioning circuits for voltage, current, and phase-angle measurement. The proposed system acquires analog signals from a voltage transformer, a current transformer–op-amp module, and an AD8302-based phase detector, computes real, reactive, and apparent power in real time, and automatically connects or disconnects capacitor-bank steps to maintain the power factor within a predefined band (0.92–0.98). Experimental results on a 4 kW inductive load array indicated that the measurement error of the analog voltage module was approximately 1.32%, while the analog current module exhibited an error of around 3.02% in comparison to digital measuring instruments. Additionally, there was an improvement in the power factor from 0.865 to 0.935, with by a reduction in load current of approximately 7% and a decrease in load reactive power of about 35%. The proposed design confirms satisfactory operation for automatic capacitor-bank control in power factor correction applications

Women's entrepreneurial leadership on culinary micro small medium enterprises success: The mediating role of absorptive capacity

In many developing economies, culinary businesses run by women face dynamic market challenges that demand adaptive leadership and effective knowledge utilization. This study explores the influence of women’s entrepreneurial leadership on the business success of micro, small, and medium enterprises in the culinary sector with absorptive capacity as a mediating factor. A survey method was used to gather quantitative data from 350 women entrepreneurs, and partial least squares structural equation modeling was used for statistics analysis. The results show that women's leadership qualities, such as empathy, teamwork, and flexibility, significantly improve company performance. Additionally, absorptive capacity serves a critical mediating function by allowing entrepreneurs to obtain, absorb, and apply outside knowledge for innovation and market adaptation. The study emphasizes how crucial it is to support women's entrepreneurial leadership and knowledge-absorption skills to promote long-term company growth. These findings provide valuable insights for policymakers, educators, and business practitioners in designing targeted interventions to support women entrepreneurs in resource-constrained environments

Balance of Payments and Exchange Rates in ASEAN Countries: Granger Causality Test

Fluctuations in the balance of payments are a reflection of the instability of the exchange rate; crisis factors also greatly affect the deficit or surplus in the balance of payments. If the exchange rate depreciates, a country will increase exports because domestic prices are relatively cheaper than foreign prices so that it is one of the competitive forces to increase exports, and vice versa if a country experiences appreciation, the country will increase imports. This study aims to analyze and compare whether there is a causal relationship between the balance of payments and currency exchange rates in ASEAN countries. This study uses the Granger Causality Test method, therefore the data of this study is in the form of a time series, namely the years 2005-2019. Only Myanmar and the Philippines have the exchange rate and balance of payments variables which have a causal relationship. This is in line with the curve in the introduction where the exchange rates of the two countries are relatively higher than those of Indonesia and Vietnam, which reach tens to tens of thousands of rupiah. This means that no matter how low the exchange rate (depreciation) is, there is no or little possibility for the countries of Indonesia and Vietnam to export, which reduces the current account, which is part of the balance of payments)

Sustainable waste solutions: Optimizing location-allocation of 3R waste management sites in Gondokusuman, Yogyakarta, Indonesia through multi-maximal covering location approach

Developing a Multi-Maximal Covering Location Model (MMCLM) for waste management in Gondokusuman Sub-district, Yogyakarta, Indonesia, is urgently needed. The closure of the Piyungan landfill has resulted in the need for additional Reduce, Reuse, and Recycle Waste Management Sites (3R-WMSs) to reduce waste that the landfill cannot accommodate. The primary objective of this model is to optimize the location and allocation of demand volume nodes, representing the resident population, to a specific set of 3R-WMS. These demand nodes are located at different distances from 3R-WMSs, including high and low coverage areas. The research in the Gondokusuman Sub-district employed MMCLM with facility capacity constraints and was developed using mixed integer linear programming methodology. The study identified five optimal locations for a 3R-WMS establishment that comprehensively cover all demand nodes (15301) and population clusters (45903) in the sub-district, including both high (5085) and low coverage areas (10216). This research represents a significant step forward in developing a sustainable environment by ensuring easy access to reducing, reusing, and recycling-based waste management facilities for residents

Citra perempuan dalam cerita rakyat Jawa Barat

Penelitian ini bertujuan untuk mengeksplorasi citra tentang karakteristik fisik dan psikis perempuan yang ada dalam buku Kumpulan Cerita Rakyat Provinsi Jawa Barat karya Maya Rohmayati dan Yodi Kurniadi. Metode penelitian yang digunakan adalah pendekatan deskriptif kualitatif, dengan pengumpulan data dilakukan melalui deskripsi langsung serta penelitian literatur. Penelitian ini menggunakan pendekatan kritik sastra feminis sebagai analisis lebih lanjut mengenai citra tokoh utama ditemukan. Analisis data dilakukan secara deskriptif analitik. Hasil penelitian menunjukkan adanya 9 data mengenai citra fisik perempuan dan 32 data mengenai citra psikis perempuan dalam Kumpulan Cerita Rakyat Provinsi Jawa Barat karya Maya Rohmayati dan Yodi Kurniadi berjudul judul Lutung Kasarung, Sangkuriang, Situ Bagendit, Hariang Banga dan Ciung Wanara, dan Talaga Warna. Citra perempuan aspek fisik yang ditemukan adalah mengandung dan melahirkan, sedangkan citra perempuan aspek psikis yang ditemukan diantaranya kecerdasan, tempramen, keinginan, sikap, dan perilaku. Citra perempuan aspek fisik didominasi oleh tokoh Purbasari dan Purbararang sedangkan citra perempuan aspek psikis didominasi oleh Purbararang dalam cerita rakyat Lutung Kasarung. Cerita rakyat yang mendominasi citra perempuan aspek fisik dan citra perempuan aspek psikis adalah cerita rakyat Jawa Barat Lutung Kasarung