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All Journal Heca Journal of Applied Sciences Infolitika Journal of Data Science
Husdayanti, Noviana
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Engineering Health Professions Medicine & Pharmacology

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Evaluation of Gradient Boosted Classifier in Atopic Dermatitis Severity Score Classification Suhendra, Rivansyah; Suryadi, Suryadi; Husdayanti, Noviana; Maulana, Aga; Noviandy, Teuku Rizky; Sasmita, Novi Reandy; Subianto, Muhammad; Earlia, Nanda; Niode, Nurdjannah Jane; Idroes, Rinaldi
Heca Journal of Applied Sciences Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v1i2.85

Abstract

This study investigates the application of the Gradient Boosting machine learning technique to enhance the classification of Atopic Dermatitis (AD) skin disease images, reducing the potential for manual classification errors. AD, also known as eczema, is a common and chronic inflammatory skin condition characterized by pruritus (itching), erythema (redness), and often lichenification (thickening of the skin). AD affects individuals of all ages and significantly impacts their quality of life. Accurate and efficient diagnostic tools are crucial for the timely management of AD. To address this need, our research encompasses a multi-step approach involving data preprocessing, feature extraction using various color spaces and evaluating classification outcomes through Gradient Boosting. The results demonstrate an accuracy of 93.14%. This study contributes to the field of dermatology by providing a robust and reliable tool to support dermatologists in identifying AD skin disease, facilitating timely intervention and improved patient care.
Cardiovascular Disease Prediction Using Gradient Boosting Classifier Suhendra, Rivansyah; Husdayanti, Noviana; Suryadi, Suryadi; Juliwardi, Ilham; Sanusi, Sanusi; Ridho, Abdurrahman; Ardiansyah, Muhammad; Murhaban, Murhaban; Ikhsan, Ikhsan
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.131

Abstract

Cardiovascular Disease (CVD), a prevalent global health concern involving heart and blood vessel disorders, prompts this research's focus on accurate prediction. This study explores the predictive capabilities of the Gradient Boosting Classifier (GBC) in cardiovascular disease across two datasets. Through meticulous data collection, preprocessing, and GBC classification, the study achieves a noteworthy accuracy of 97.63%, underscoring the GBC's effectiveness in accurate CVD detection. The robust performance of the GBC, evidenced by high accuracy, highlights its adaptability to diverse datasets and signifies its potential as a valuable tool for early identification of cardiovascular diseases. These findings provide valuable insights into the application of machine learning methodologies, particularly the GBC, in advancing the accuracy of CVD prediction, with implications for proactive healthcare interventions and improved patient outcomes.
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.
Co-Authors Abdurrahman Ridho Earlia, Nanda Ikhsan Ikhsan Ilham Juliwardi Maghfirah, Hayatun Maulana, Aga Muhammad Ardiansyah Muhammad Subianto Murhaban Murhaban Niode, Nurdjannah Jane Qaisa, Rara Syifa Rinaldi Idroes Sanusi Sanusi Sasmita, Novi Reandy Suhendra, Rivansyah Suryadi Suryadi Teuku Rizky Noviandy