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Infolitika Journal of Data Science
Published by PT. Heca Sentra Analitika
ISSN : -     EISSN : 30258618     DOI : https://doi.org/10.60084/ijds
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Infolitika Journal of Data Science is a distinguished international scientific journal that showcases high caliber original research articles and comprehensive review papers in the field of data science. The journals core mission is to stimulate interdisciplinary research collaboration, facilitate the exchange of knowledge, and drive the advancement and application of innovative strategies within the data science domain. Topics of this journal includes, but not limited to Data Mining and Analysis, Machine Learning and Artificial Intelligence, Big Data and Data Engineering, Predictive Modeling and Forecasting, Natural Language Processing, Computer Vision, Data Visualization and Interpretation, Ethics and Privacy in Data Science, Applications of Data Science, Interdisciplinary Approaches
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 31 Documents
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Assessing LightGBM Performance in Automated Leukemia Cell Classification Qaisa, Rara Syifa; Maghfirah, Hayatun; Suryadi, Suryadi; Husdayanti, Noviana; Suhendra, Rivansyah
Infolitika Journal of Data Science Vol. 4 No. 1 (2026): May 2026 (In Press)
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v4i1.351

Abstract

Leukemia is a type of blood cancer that requires fast and accurate diagnosis for effective treatment. Manual identification of leukemia blood cell subtypes is often challenging, time-consuming, and prone to observer variability, making automated image-based classification essential. This study evaluates the performance of the Light Gradient-Boosting Machine (LightGBM) as a computationally efficient and interpretable alternative to deep learning models for classifying leukemia subtypes. The dataset includes 3,000 microscopic images representing five classes: acute lymphocytic, acute myelogenous, chronic lymphocytic, chronic myelogenous, and healthy blood cells. Images were preprocessed using bilinear interpolation to balance quality and efficiency, and 90 statistical features were extracted across 13 distinct color spaces. The model was trained on an 80% subset and validated on a 20% hold-out set after hyperparameter optimization. LightGBM achieved robust performance with an accuracy of 93.3%, precision of 99.1%, recall of 94.9%, and an F-measure of 96.8%. Feature importance analysis revealed that texture variance in the YIQ color space (STD_YIQ_I) was the most critical predictor, highlighting the biological relevance of chromatin texture in classification. These results indicate that LightGBM is an effective, lightweight, and reliable approach for leukemia subtype classification, holding strong potential for implementation in resource-constrained automated diagnostic systems.

Page 4 of 4 | Total Record : 31

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