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All Journal Jurnal Kesehatan Budi Luhur: Jurnal Ilmu-Ilmu Kesehatan Masyarakat, Keperawatan, dan Kebidanan Jurnal Kesehatan Bakti Tunas Husada
Annisa Nur Hasanah
Universitas Bakti Tunas Husada
Chemistry Health Professions Medicine & Pharmacology Nursing Public Health Social Sciences

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ANALISIS QUALITY CONTROL HEMATOLOGY ANALYZER DI LABORATORIUM HEMATOLOGI UNIVERSITAS BAKTI TUNAS HUSADA: ANALYSIS OF QUALITY CONTROL OF HEMATOLOGY ANALYZER IN THE HEMATOLOGY LABORATORY OF BAKTI TUNAS HUSADA UNIVERSITY Annisa Nur Hasanah; Resa Handini
Jurnal Kesehatan Bakti Tunas Husada: Jurnal Ilmu-ilmu Keperawatan, Analis Kesehatan dan Farmasi Vol 25 No 1 (2025)
Publisher : LPPM Universitas Bakti Tunas Husada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36465/jkbth.v25i1.1483

Abstract

Quality Control pada alat Hematologi penting dilakukan, mengingat perlunya menganalisis keakuratan, kebenaran dan evaluasi berdasarkan aturan Westgard untuk menentukan penyimpangan pengukuran pada alat. Penelitian ini bertujuan memastikan bahwa hasil pemeriksaan hematology alat hematology analyzer memenuhi standar aturan Westgard. Metode yang digunakan dalam penelitian ini adalah deskriptif analitik menggunakan darah kontrol yang diperiksa selama 1 bulan dalam alat hematology analyzer di laboratorium hematology Universitas Bakti Tunas Husada. Hasil penelitian didapatkan terhadap pemeriksaan Jumlah Leukosit, Jumlah Eritrosit, HGB, HCT dan Jumlah Trombosit, hasil analisis menunjukkan rerata nilai Jumlah leukosit 7.616 sel/ul dengan nilai target 8.100 sel/ul, Standar Deviasi 2.8 Koefisien Variasi 3.72%, nilai rerata Jumlah Eritrosit 4.65 jt sel/ul dengan nilai target 4.58 juta sel/ul, Standar Deviasi 1.7 Koefisien Variasi 3.70%, nilai rerata Hemoglobin 13.5 g/dl dengan nilai target 13.7 g/dl, Standar Deviasi 0.3 Koefisien Variasi 2.70, nilai Hematokrit 39.3 % dengan nilai target 41.8% Standar Deviasi 0.5 Koefisien Variasi 1.27% dan nilai Jumlah Trombosit 242.000 sel/ul dengan nilai target 301.000 sel/ul Standar Deviasi 29.8 Koefisien Variasi 12.3. Dari hasil penelitian dapat disimpulkan bahwa dalam pengawasan pemeriksaan Quality Control di Laboratorium Hematologi di Universitas Bakti Tunas Husada didapatkan hasil yang baik. Diharapkan kualitas hasil pemeriksaan Quality Control dapat tetap terjaga.
Application of Artificial Intelligence in White Blood Cell Classification Based on Microscopic Images: A Scoping Review Annisa Nur Hasanah; 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.
Co-Authors Oktafirani Al Sas Resa Handini Yosua Darmadi Kosen