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All Journal Bioma : Jurnal Ilmiah Biologi LANCAH Jurnal Inovasi dan Tren
Hilmi, Dafa Fadhilah
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Economics, Econometrics & Finance Education Immunology & microbiology Social Sciences Other

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Analisis Konsep Rancangan Produk NutriFarm Website Sebagai Inovasi Peternakan Masa Depan Febrianti, Vira; Hilmi, Dafa Fadhilah; Nuhita, Nashifa Maisun; Raziqi, Ahmad 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 Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

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.  
MORPHOLOGICAL CHARACTERIZATION OF BRAIN TUMOR TISSUE IN MRI IMAGES USING CNN AND TRANSFER LEARNING Hilmi, Dafa Fadhilah; Wibawa, Aji Prasetya; Agustavada, Ardha Ardhana Putra; Sholum, Abdullah; Dwiyanto, Felix Andika
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.
Co-Authors Agustavada, Ardha Ardhana Putra Aji Prasetya Wibawa Devy Dwi Arianty Dwiyanto, Felix Andika Febrianti, Vira Hikam Muta’aly Al Isyraq Izzatu Salisah Mafatihurrohmah Maura Y’nauri Yasmin Hidayat Nuhita, Nashifa Maisun Raziqi, Ahmad Raziqi Sholum, Abdullah