Universitas Ahmad Dahlan: UAD Scientific Journal
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A Systematic Review of Machine Learning and Deep Learning Approaches in MRI-Based Brain Tumour Analysis, Detection and Classification
Full text linkA 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
Implementation of an Automatic Controlled Power Factor Correction System Utilizing Low-Cost Modules
Full text linkThis 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
Mobile 360° Panoramic Training for Commercial Kitchen Safety: Usability and Learning Outcomes
Full text linkCommercial 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
An Innovation Approach for Feature Selection Medical Data Using Joint Fine-Tuning Fusion Graph Convolutional Network
Full text linkThis 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
Legal and Public Health Governance for Sustainable Integration of Mobile Health (mHealth) Technologies in East Africa
Full text linkMobile 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 East Javanese Local Culture in Graphic Design Elements in Students’ Final Projects: A Literature Review
Full text linkThe 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
Difference in Biochemical Composition and Nutritional Trait in Different Areas of Fillets of Dorsal, Venter-cha, and Ventral of Carangoides fulvoguttatus
Full text linkEnvironmental parameters are essential to the life cycle and physiological activities of fish. These environmental conditions can profoundly influence fish physiology, behavior, and their nutrition. The purpose of this study was to highlight nutritional properties of dorsal, ventercha and ventral fillets from sea fish Carangoides fulvoguttatus. Dorsal, ventercha and ventral fillet portions of Iranian sea fish (C. fulvoguttatus) analyzed for proximate constituents, energy values and pH. The proximate of C. fulvoguttatus fillets varied between three anatomical locations ventercha, dorsal and ventral. Moisture and fat content differentiated dorsal and ventercha from ventral in fish muscle. In particular, a relatively large variation observed in crude fat content, which trend of significant fat changes between the three portions was as follows: ventral>ventercha>dorsal. The results showed that the ventral part containing significantly less moisture (73.4%±0.7), but the ventercha part had protein content (15.7%±0.3) which was higher than the other two parts of muscle. The ventral part had higher fat content (7.13%±0.1). The energy value (127.08 kcal/100 g) in ventral part found the highest. The ventercha area containing highest protein content and ventral had highest lipid content (7.13%). The index of nutritional quality for protein was always higher in ventercha than in dorsal and ventral, that of fish being especially interesting because it is associated with a relatively lower energy value. The nutritional traits found best in ventral muscle due to highest dry matter and energy value with lowest moisture
Pendampingan pengelolaan sekolah lansia menuju lansia sehat dan mandiri
Full text linkThe elderly, defined as individuals aged 60 and above, often face challenges related to health, social, and economic factors that affect their quality of life. Programs such as Senior Schools and Senior Learning Spaces aim to support the physical, social, and mental health of the elderly through relevant educational activities. However, management and facility challenges required attention. The goal of this research was to enhance the management capacity of Senior Learning Spaces through training for partners in developing elderly learning programs and improving learning infrastructure. This research used a qualitative approach with Focus Group Discussions (FGD) and curriculum workshops at the Senior School of Yayasan Senior Madani Indonesia, Social Club Indonesia, from November 5-10, 2024. The study involved 10 informants and aimed to design a curriculum relevant to the needs of the elderly. The outcome of this reasearch was a structured curriculum for senior schools, covering areas such as worship, health, Quran reading, and simple technology. This curriculum was relevant to the needs of the elderly and supported the goal of fostering healthy, independent, and productive seniors
Exploring teacher's role as motivators and role models in cultivating children's safe behavior: a qualitative study
Full text linkChildren are vulnerable to injuries and accidents in school environments due to cognitive limitations. Teachers play a crucial role in shaping children's safe behavior by transferring knowledge and acting as motivators and role models. This study aims to explore how teachers influence children's safe behavior using a qualitative descriptive approach. Data was collected through observations and focus group discussions (FGDs) and analyzed using thematic analysis. Findings indicate that 67% of children's behavior at school was categorized as safe, while 33% was unsafe. Observations were conducted across the play area, learning spaces, and the street in front of the school. Teachers have actively encouraged safe behavior by providing support and motivation. However, some students continue engaging in unsafe practices despite these efforts. Teachers also attempt to model safe behavior, such as walking calmly in hallways and sitting properly in classrooms. However, FGD findings revealed that half of the teachers did not fully practice safe behavior, notably by failing to wear helmets when riding motorcycles to school. In conclusion, teachers at SD Negeri Krajan have effectively acted as motivators, encouraging students to adopt safe behavior. However, their role as role models remains inconsistent, highlighting the need for greater self-adherence to safety practices. Strengthening teachers' commitment to modeling safe behavior could enhance the effectiveness of safety education in schools
Gambaran rasa sedih dan gembira pada lirik lagu “Sorai” karya Nadin Amizah untuk menguatkan kajian stilistika
Full text linkTujuan penelitian ini untuk menguatkan kajian stilistika dengan menganalisis tema, makna, pemilihan diksi, gaya bahasa dan citraan berkonteks rasa sedih dan gembira pada lirik lagu "Sorai” karya Nadin Amizah. Data penelitian melibatkan analisis lirik lagu "Sorai" untuk memperdalam memahami tema, makna, pemilihan diksi, gaya bahasa, dan citraan rasa sedih dan gembira. Penelitian ini menggunakan metode deskriptif kualitatif dan sumber data utamanya adalah lirik lagu "Sorai" yang dipopulerkan oleh Nadin Amizah di kanal Youtube-nya. Teknik penyediaan data menggunakan dua tahap yaitu simak dan transkripsi. Peneliti mendengarkan lirik lagu "Sorai" melalui video klip di Youtube untuk menganalis lirik lagu yang telah ditranskripsi dengan mencatat kata dan kalimat yang relevan. Kemudian, data tersebut dikategorikan untuk menunjukkan pemilihan diksi, gaya bahasa, dan citraan rasa sedih dan gembira. Analisis data menggunakan teknik Miles dan Hubberman, yang meliputi reduksi data, penyajian data, dan penarikan simpulan untuk menghasilkan hasil yang sesuai dengan tujuan penelitian. Hasil penelitian menunjukkan gambaran rasa sedih dan gembira pada lirik lagu “Sorai” meliputi tema, makna, pemilihan diksi kata dasar, kata berimbuhan, dan kata majemuk yang berkonteks rasa sedih dan gembira untuk menciptakan suasana bernuansa negatif dan positif, serta penggunaan gaya bahasa dan citraan dalam lirik lagunya