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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Elisya Mutiara Br Sitanggang
Universitas prima indonesia
Computer Science & IT Engineering

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IMPLEMENTASI FT-TRANSFORMER UNTUK KLASIFIKASI PENYAKIT DARAH BERDASARKAN PARAMETER HEMATOLOGI RUMAH SAKIT ROYAL PRIMA MEDAN Elisya Mutiara Br Sitanggang; Rinaldy Oscar Dery Lubis; Faeri Berkat Zai; Enggar Satrio; sautdohot siregar
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8040

Abstract

This study aims to develop a Deep Learning model based on the Feature Tokenizer Transformer (FTTransformer) architecture for blood disease classification using 103,024 retrospective secondary Complete Blood Count (CBC) patient data from Royal Prima Hospital Medan. The self-attention mechanism in this model is implemented to automatically map complex interactions among hematological parameters without requiring manual feature engineering. Testing results demonstrate that the FT-Transformer effectively overcomes the challenges of highly imbalanced clinical data, yielding superior and stable multi-class classification performance. This is evidenced by Accuracy, Precision, Recall, and F1-Score metrics reaching 0.98 to 1.00 across four main diagnostic categories: Normal, Anemia, Sepsis/Infection, and Thrombocytopenia. Overall, this computational approach successfully produced a robust and high-precision Clinical Decision Support System (CDSS) prototype for interpreting tabular laboratory results.
Co-Authors Enggar Satrio Faeri Berkat Zai Rinaldy Oscar Dery Lubis sautdohot siregar