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All Journal Indonesian Journal of Data and Science Bioma : Jurnal Ilmiah Biologi LANCAH Jurnal Inovasi dan Tren
Dafa Fadhilah Hilmi
Universitas Negeri Malang
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Immunology & microbiology Mathematics Social Sciences Other

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MORPHOLOGICAL CHARACTERIZATION OF BRAIN TUMOR TISSUE IN MRI IMAGES USING CNN AND TRANSFER LEARNING Dafa Fadhilah Hilmi; Aji Prasetya Wibawa; Ardha Ardhana Putra Agustavada; Abdullah Sholum; Felix Andika Dwiyanto
BIOMA : Jurnal Ilmiah Biologi Vol. 15 No. 1 (2026): April 2026
Publisher : Prodi Pendidikan Biologi, FPMIPATI, Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/bioma.v15i1.3550

Abstract

This study evaluates the role of computational pattern recognition as an observational method for analyzing morphological characteristics of brain tumor tissue in MRI data. A total of 6,056 labeled MRI images, including glioma, meningioma, and pituitary tumor cases, were examined. The images were standardized to maintain uniform structural representation and processed using three convolutional-based architectures: a baseline CNN, MobileNetV2, and EfficientNet-B0. Model performance was assessed using accuracy, precision, recall, F1-score, AUC-ROC, and a confusion matrix. The findings show variation in identification performance across tumor categories, with pituitary tumors consistently recognized, while misclassification predominantly occurred between glioma and meningioma. Models based on transfer learning achieved stronger agreement with the reference labels than the baseline CNN, with MobileNetV2 demonstrating the most stable performance. The recurrence of similar misclassification patterns across models suggests the presence of shared morphological characteristics in MRI representations of certain tumor types. Overall, the results support the use of computational image analysis as a structured observational framework that enables consistent evaluation of brain tumor tissue morphology in MRI, providing complementary insights for biological interpretation.
Effect of Spatial, Intensity, and Hybrid Augmentation on Kidney CT Image Classification Ardha Ardhana Putra Agustavada; Aji Prasetya Wibawa; Dafa Fadhilah Hilmi; Abdullah Sholum; Felix Andika Dwiyanto
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.443

Abstract

Kidney diseases remain a significant global health challenge, and computed tomography (CT) plays an important role in supporting their diagnosis through detailed visualization of renal structures. In deep learning-based medical image classification, data augmentation is commonly employed to mitigate the limitations of small training datasets; nevertheless, the effects of distinct augmentation strategies on image characteristics and learning behavior across architectures remain insufficiently explored. This study investigates the impact of spatial, intensity, and hybrid augmentation on kidney CT image classification using CNN, MobileNetV2, and EfficientNet-B0 architectures. A stratified data split was adopted, and all experiments were repeated using three random seeds (1, 42, and 123), with results reported as mean ± standard deviation. Four training scenarios (baseline, spatial, intensity, and hybrid augmentation) were evaluated using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The baseline configuration achieved the highest overall performance, reaching mean accuracies of 99.96 ± 0.04%, 98.32 ± 0.06%, and 94.06 ± 0.58% for CNN, MobileNetV2, and EfficientNet-B0, respectively. Among the augmentation strategies, intensity augmentation achieved the highest performance only for CNN while consistently exhibiting smaller performance degradation relative to the baseline and more stable convergence than the spatial and hybrid augmentation approaches. The consistently high baseline performance, particularly for CNN, underscores the importance of rigorous validation on public medical imaging datasets. These findings indicate that preserving anatomically relevant image characteristics is more beneficial than indiscriminately increasing data diversity. Therefore, while the baseline remained the best overall training strategy, intensity augmentation was the most effective augmentation approach
Analisis Konsep Rancangan Produk Kartu Saku P3K Berbasis QR Code untuk Meningkatkan Nilai Fungsional, Ergonomi, dan Estetika Nabila Putri Aprilia Nur Cahyanti; Wardatul Elsa Bilbina; Putri Hajar Syalsabilah; Bertrand Okthaddeus; Vira Febrianti; Dafa Fadhilah Hilmi; Nashifa Maisun Nuhita; Ahmad Raziqi
LANCAH: Jurnal Inovasi dan Tren Vol. 3 No. 2 (2025): JUNI-NOVEMBER
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ljit.v3i2.5957

Abstract

This study aims to develop a functional, ergonomic P3K pocket card design that meets user needs and is in line with the latest product design developments. This P3K pocket card is designed as a practical health education medium for emergency use. The methods used are a combination of structured literature studies and a qualitative descriptive approach. The research involved a comprehensive review of scientific publications focusing on product design, ergonomics, and health technology from 2020 to 2025. This review aims to integrate important findings regarding function, aesthetics, ergonomics, and value of use in formulating the design principles of the P3K pocket card. Ergonomic principles (ease of use) and aesthetics (visual appeal) are the main focus of the design. The results of the study show that the proposed P3K Pocket Card design is able to effectively integrate clinical functions (essential P3K information), modern design aesthetics, and ergonomics (ease of carrying and access). This design produces an educational medium that is not only practical and accessible, but also increases the potential for P3K knowledge transfer among the general public and non-medical personnel.
Analisis Konsep Rancangan Produk NutriFarm Website Sebagai Inovasi Peternakan Masa Depan Vira Febrianti; Dafa Fadhilah Hilmi; Nashifa Maisun Nuhita; Ahmad Raziqi Raziqi; Devy Dwi Arianty; Izzatu Salisah Mafatihurrohmah; Maura Y’nauri Yasmin Hidayat; Hikam Muta’aly Al Isyraq
LANCAH: Jurnal Inovasi dan Tren Vol. 3 No. 2 (2025): JUNI-NOVEMBER
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ljit.v3i2.5959

Abstract

This study aims to review the latest literature on livestock nutrition, feed management, and digital technology developments in animal husbandry, and how these findings support the development of the NutriFarm Website as a web-based nutrition management platform. The results show that the main problems in livestock farming, especially on a small scale, include nutritional imbalances, high rates of metabolic diseases such as hypocalcemia due to calcium deficiency, and low literacy among farmers in feed management. On the other hand, digital technologies such as the Internet of Things (IoT), decision support systems, and feed automation have been proven to improve livestock efficiency and health, but their adoption remains low due to limited access and complexity of use. The literature synthesis confirms that simple and accessible digital platforms, such as the NutriFarm Website, have the potential to be practical solutions to help farmers calculate nutritional requirements, understand feed composition, and prevent diseases related to nutritional imbalances. This study concludes that the NutriFarm Website not only improves feed management efficiency but also strengthens farmer education and encourages the adoption of technology to support productivity and sustainability in Indonesia's livestock sector.  
Co-Authors Abdullah Sholum Ahmad Raziqi Ahmad Raziqi Raziqi Aji Prasetya Wibawa Ardha Ardhana Putra Agustavada Bertrand Okthaddeus Devy Dwi Arianty Felix Andika Dwiyanto Hikam Muta’aly Al Isyraq Izzatu Salisah Mafatihurrohmah Maura Y’nauri Yasmin Hidayat Nabila Putri Aprilia Nur Cahyanti Nashifa Maisun Nuhita Putri Hajar Syalsabilah Vira Febrianti Wardatul Elsa Bilbina