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All Journal G-Tech : Jurnal Teknologi Terapan
Angga Aditya Permana
Universitas Multimedia Nusantara, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Modelling The Nexus between Parenting Style and Anti Social Behavior using Ensemble Learning Approach Angga Aditya Permana; Muhammad Fahrury Romdendine
G-Tech: Jurnal Teknologi Terapan Vol 7 No 4 (2023): G-Tech, Vol. 7 No. 4 Oktober 2023
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v7i4.3304

Abstract

Contemporary society is grappling with issues of anti-social behavior in children and adolescents, one of which is influenced by parenting styles. This research employs machine learning technology, particularly ensemble learning, to model the relationship between parenting styles and anti-social behavior. The research data is derived from previous studies encompassing parenting style parameters and anti-social behavior. This data is preprocessed and feature-engineered, then used in modeling through the Random Forest (RF) and Adaptive Boost (AdaBoost) methods. Modeling is conducted in two phases: vanilla modeling and hyperparameter tuning. The results of the tuned models indicate that RF performs better (accuracy=91%) than AdaBoost (accuracy=72%). In conclusion, RF, as a bagging ensemble learning technique, effectively models the relationship between parenting styles and anti-social behavior. Future studies are recommended to gather more training data and develop an early detection system for use by child psychologists in the field.
Ensemble Learning Approach Reveals Significant Clinical Attributes from Real-World Breast Cancer Cases Angga Aditya Permana; Muhammad Fahrury Romdendine
G-Tech: Jurnal Teknologi Terapan Vol 8 No 2 (2024): G-Tech, Vol. 8 No. 2 April 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i2.4044

Abstract

Breast cancer has become on of the leading causes of death in Indonesia. This study contributes to global efforts to combat breast cancer by improving patient outcome prediction accuracy. This study employed ensemble learning techniques such as Random Forest, XGBoost, and LightGBM. The results of the study demonstrates LightGBM's superior performance (accuracy=85%, ROC-AUC=81%, AUPR=85%). Notably, all three algorithms identify key clinical attributes: "Relapse Free Status (Months)", "Overall Survival (Months)", "Nottingham Prognostic Index", and "Lymph Nodes Examined Positive". LightGBM uniquely highlights "pam50_LumA" as significant, suggesting reduced fatality risk for Luminal A subtype patients, while others prioritize "Tumor Size". This research lays groundwork for intelligent systems to predict breast cancer outcomes, potentially transforming patient care and clinical practice.
Co-Authors Muhammad Fahrury Romdendine