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All Journal Jurnal Kesehatan Budi Luhur: Jurnal Ilmu-Ilmu Kesehatan Masyarakat, Keperawatan, dan Kebidanan Journal On Teacher Education (Jote)
Nur Hasanah, Annisa
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Education Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN WHITE BLOOD CELL CLASSIFICATION BASED ON MICROSCOPIC IMAGES: A SCOPING REVIEW Nur Hasanah, Annisa; Oktafirani Al Sas; Yosua Darmadi Kosen
Jurnal Kesehatan Budi Luhur : Jurnal Ilmu-Ilmu Kesehatan Masyarakat, Keperawatan, dan Kebidanan Vol. 18 No. 2 (2025): July 2025
Publisher : STIKes Budi Luhur Cimahi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62817/jkbl.v18i2.432

Abstract

White blood cell (WBC) classification plays a crucial role in hematological diagnosis and is typically performed manually using microscopic images. However, manual analysis is limited by subjectivity and time inefficiency. With recent technological advances, artificial intelligence (AI) offers promising solutions for automated WBC classification that enhance accuracy and efficiency. This study presents a scoping review of 20 scientific publications discussing AI applications in microscopic image-based WBC classification. Literature searches were conducted in PubMed, ScienceDirect, Institute of Electrical and Electronics Enginers (IEEE) Xplore, and Google Scholar using relevant keywords such as “AI”, “white blood cell”, and “microscopic image”. Findings indicate that the most commonly used method is Convolutional Neural Network (CNN), either standalone or hybrid (e.g., YOLOv5, ResNet, Vision Transformer), achieving accuracies up to 99.7%. The datasets were mostly public Blood Cell Count and Detection (BCCD), Leucocyte Images for Segmentation and Classification (LISC), Raabin-WBC or local laboratory sources. The reviewed studies aimed at automatic WBC detection, classification, and morphological identification. Despite encouraging outcomes, challenges such as external validation and limited access to real clinical data remain. Overall, AI has proven effective in enhancing speed, accuracy, and objectivity in WBC classification. Further research is needed to support AI integration into real-world clinical laboratory practice. Keywords : Artificial intelligence, White blood cells, Classification, Microscopic image, CNN
Pengembangan Bahan Ajar Digital Berbasis Kemampuan Sains untuk Anak Usia 5-6 Tahun di Palembang Nur Hasanah, Annisa; Effendi, Darwin; Andriani, Dessi
Journal on Teacher Education Vol. 5 No. 4 (2024): Journal on Teacher Education
Publisher : Universitas Pahlawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jote.v5i4.30482

Abstract

Kemampuan sains anak yang masih rendah dikarenakan  belum adanya bahan ajar yang mendukung. Bahan ajar yang berbasis digital dibutuhkan untuk meningkatkan motivasi anak belajar sains. Bahan ajar digital merupakan bahan ajar yang dibuat dengan komputer dan dilengkapi dengan perangkat multimedia. Untuk itu penelitian ini bertujuan untuk menghasilkan bahan ajar digital berbasis sains yang teruji valid dan praktis. Produk akhir yang dihasilkan dari penelitian ini adalah E-modul atau modul elektronik. Metode penelitian yang digunakan peneliti adalah Research and Development menggunakan model ADDIE, terdapat 5 tahapan yaitu Analisis (analysis), Perancangan (design), Pengembangan (development), Implementasi (implementation) dan Evaluasi (evaluation). Berdasarkan hasil validasi dari tim ahli yaitu ahli bahasa 95% pada kategori sangat valid, ahli media 91% pada kategori sangat valid dan ahli materi 100% pada kategori sangat valid. Produk E-modul telah diujicobakan ke anak kelompok B TK Pembina 7 Palembang dan mereka dapat menggunakan E-modul tersebut, maka dapat disimpulkan bahwa bahan ajar digital berbasis kemampuan sains ini valid dan layak digunakan.
Co-Authors Dessi Andriani, Dessi Effendi, Darwin Oktafirani Al Sas Yosua Darmadi Kosen